RESEARCH CENTRE

Inria Centre
at Université de Lorraine

IN PARTNERSHIP WITH:

Université de Lorraine, CNRS

2023
ACTIVITY REPORT

Project-Team

CAPSID

Computational Algorithms for Protein
Structures and Interactions

IN COLLABORATION WITH: Laboratoire lorrain de recherche en
informatique et ses applications (LORIA)

DOMAIN

Digital Health, Biology and Earth

THEME

Computational Biology

Contents

Project-Team CAPSID

1 Team members, visitors, external collaborators

2 Overall objectives

2.1 Computational Challenges in Structural Biology . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Two Research Axes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 Research program

3.1 Knowledge Discovery in Structural Databases . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.2 Knowledge discovery from protein structural databases . . . . . . . . . . . . . . . . .
3.1.3 Function Annotation in Large Protein Graphs . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . .
3.1.4 Knowledge discovery algorithms in large biological knowledge graphs
3.2 Integrative Multi-Component Assembly and Modelling . . . . . . . . . . . . . . . . . . . . .
3.2.1 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.2 Coarse-Grained Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.3 Assembling Multi-Component Complexes and Integrative Structure Modelling . . .
3.2.4 Protein-Nucleic Acid Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4 Application domains

4.1 Biomedical Knowledge Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Prokaryotic Type IV Secretion Systems
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Protein - RNA Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 3D structural differences among HLA antigens . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5 Investigating the dynamical behaviour of protein complexes with MD simulation . . . . . .
4.6 Learning from protein surfaces to accurately predict their functional sites.

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5 Social and environmental responsibility

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5.1 Environmental Footprint of Research Activities . . . . . . . . . . . . . . . . . . . . . . . . . . 10

6 New software, platforms, open data

10
6.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
6.1.1 CROMAST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
6.1.2 HIPPO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
6.1.3 RRMpip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
6.1.4 ssRNATTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
6.2 New platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
6.3 Open data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

7 New results

12
7.1 Axis 1 : Knowledge Discovery in Structural Databases . . . . . . . . . . . . . . . . . . . . . . . 12
7.1.1 Knowledge graph mining with embedding-based methods . . . . . . . . . . . . . . . 13
7.1.2 Biological network modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.1.3 Machine Learning and Scalable Graph-based Approaches . . . . . . . . . . . . . . . . 14
7.2 Axis 2 : Integrative Multi-Component Assembly and Modeling . . . . . . . . . . . . . . . . . 14
7.2.1 Modeling and design of RNA-RRM complexes . . . . . . . . . . . . . . . . . . . . . . . 14
7.2.2 3D Modeling of proteins and protein complexes . . . . . . . . . . . . . . . . . . . . . . 16
7.2.3 Investigating the dynamical behaviour of protein complexes with MD simulation . . 16
7.2.4 Machine learning methods for proteomics, interactomics and protein design . . . . 17
7.2.5 Miscellaneous results on structural studies of host-pathogen interactions . . . . . . 18

8 Partnerships and cooperations

8.2 International research visitors

18
8.1 International initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
8.1.1 Participation in other International Programs . . . . . . . . . . . . . . . . . . . . . . . 18
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
8.2.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
8.2.2 Visits to international teams
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
8.3.1 Other european programs/initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
8.4 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
8.5 Regional initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

8.3 European initiatives

9 Dissemination

22
9.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
9.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
9.1.2 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
9.1.3 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 22
9.1.4 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
9.1.5 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
9.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
9.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
9.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
9.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
9.2.4 Internal or external Inria responsibilities . . . . . . . . . . . . . . . . . . . . . . . . . . 23

10 Scientific production

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10.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
10.2 Publications of the year
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
10.3 Cited publications

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Project-Team CAPSID

Creation of the Project-Team: 2015 July 01

Keywords

Computer sciences and digital sciences

A3.1.1. – Modeling, representation

A3.1.9. – Database

A3.1.10. – Heterogeneous data

A3.1.11. – Structured data

A3.2.1. – Knowledge bases

A3.2.2. – Knowledge extraction, cleaning

A3.2.4. – Semantic Web

A3.2.5. – Ontologies

A3.2.6. – Linked data

A3.3.2. – Data mining

A3.5.1. – Analysis of large graphs

A6.1.4. – Multiscale modeling

A6.2.7. – High performance computing

A6.3.3. – Data processing

A6.5.5. – Chemistry

A8.2. – Optimization

A9.1. – Knowledge

A9.2. – Machine learning

Other research topics and application domains

B1.1.1. – Structural biology

B1.1.2. – Molecular and cellular biology

B1.1.7. – Bioinformatics

B2.2.1. – Cardiovascular and respiratory diseases

B2.2.4. – Infectious diseases, Virology

B2.4.1. – Pharmaco kinetics and dynamics

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Marie-Dominique Devignes [Team leader, CNRS, Researcher, HDR]

• Isaure Chauvot de Beauchêne [CNRS, Researcher]

• Yasaman Karami [INRIA, Researcher]

• Hamed Khakzad [INRIA, Advanced Research Position]

• Bernard Maigret [CNRS, Emeritus]

Faculty Members

• Sabeur Aridhi [UL, Associate Professor, from Jul 2023]

• Sabeur Aridhi [UL, Associate Professor Delegation, until Jun 2023]

• Malika Smaïl-Tabbone [UL, Associate Professor, HDR]

Post-Doctoral Fellow

• Dominique Mias-Lucquin [UL, Post-Doctoral Fellow, until Jun 2023]

PhD Students

• Diego Amaya Ramirez [UL, ATER, until Sep 2023]

• Kamrul Islam [UL, until Jan 2023]

• Mohammed Khatbane [UL, from Oct 2023]

• Omid Mokhtari [INRIA, from Oct 2023]

• Victor Pryakhin [UL, from Oct 2023]

Technical Staff

• Hrishikesh Dhondge [CNRS, Engineer, until Aug 2023]

• Anna Kravchenko [CNRS, Engineer]

• Athénaïs Vaginay [CNRS, Engineer, until Mar 2023]

• Taher Yacoub [CNRS, Engineer, from Nov 2023]

Interns and Apprentices

• Benjamin Gottis [CNRS, Intern, from Feb 2023 until Jul 2023]

• Jean-Baptiste Paquin [UL, Intern, from Mar 2023 until Apr 2023]

• Hugo Rimet [UNIV NANTES, Intern, from Mar 2023 until May 2023]

Administrative Assistants

• Antoinette Courrier [CNRS]

• Sophie Drouot [INRIA]

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External Collaborators

• Taha Boukhobza [UL, HDR]

• Emmanuel Bresso [CHRU Nancy]

• Pablo Chacon [Université de Madrid]

2 Overall objectives

2.1 Computational Challenges in Structural Biology

NB: This section has been remodeled since the death of Dave Ritchie in 2019.

Many of the processes within living organisms can be studied and understood in terms of biochemical
interactions between large macromolecules such as DNA, RNA, and proteins. To a first approximation,
DNA may be considered to encode the blueprint for life, whereas proteins and RNA make up the three-
dimensional (3D) molecular machinery. Many biological processes are governed by complex systems
of proteins and/or RNA which interact cooperatively to regulate the chemical composition within a
cell or to carry out a wide range of biochemical processes such as photosynthesis, metabolism, and
cell signalling, for example. It is becoming increasingly feasible to isolate and characterise some of the
individual molecular components of such systems, but it still remains extremely difficult to achieve
detailed models of how these complex systems actually work. Consequently, a new multidisciplinary
approach called integrative structural biology has emerged which aims to bring together experimental
data from a wide range of sources and resolution scales in order to meet this challenge [58, 72].

Understanding how biological systems work at the level of 3D molecular structures presents fascin-
ating challenges for biologists and computer scientists alike. Despite being made from a small set of
simple chemical building blocks, protein and nucleic acid (NA) molecules have a remarkable ability to
self-assemble into complex molecular machines which carry out very specific biological processes. As
such, these molecular machines may be considered as complex systems because their properties are
much greater than the sum of the properties of their component parts.

2.2 Two Research Axes

The overall objective of the CAPSID team is to develop algorithms and software to help study biological
systems and phenomena from a structural point of view. In particular, the team aims to develop al-
gorithms which can help to model the structures of large multi-component biomolecular machines and
to develop tools and techniques to represent and mine knowledge of the 3D shapes of proteins, NA and
their interactions. Thus, a unifying theme of the team is to tackle the recurring problem of representing
and reasoning about large 3D macromolecular shapes. More specifically, our aim is to develop compu-
tational techniques to represent, analyse, and compare the shapes and interactions of biomolecules in
order to better understand how their 3D structures relate to their biological function. In summary, the
CAPSID team is organised according to two research axes whose complementarity constitutes an original
contribution to the field of structural bioinformatics:

• Axis 1: Knowledge Discovery in Structural Databases,

• Axis 2: Integrative Multi-Component Assembly and Modeling.

In the first axis, our main objective is to design, implement and test new KDD ("Knowledge Discovery
in Databases") approaches to exploit specifically the structural information contained and sometimes hid-
den in many biological databases. These approaches will be oriented towards understanding molecular
interactions in living organisms under physiological or pathological conditions.

In the second axis, our main objective is to propose new and fast methods to model the 3D structure
of multi-component systems and characterize their dynamic behaviour. The challenge here is to integrate
molecular flexibility into 3D models, thanks to molecular dynamics simulation and/or combinatorial
approaches.

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Inria Annual Report 2023

Finally, the complementarity of the two axes will be expressed through a common objective oriented
towards the proposal of possible new treatments against diseases, based on the knowledge extracted and
on the advances in 3D modeling of flexible molecular interactions. This objective will benefit from our
network of biologist and clinician partners.

3 Research program

This section presents the current CAPSID research program. Several subjects initially present at the
creation time (2015) or at last evaluation (2017) are no longer presented due to the death of Dave Ritchie.

3.1 Knowledge Discovery in Structural Databases

3.1.1 Context

In this axis, the CAPSID team develops methods related to knowledge discovery from databases (KDD,
[36]). The diversity of biological databases and resources is such today that it is more and more difficult to
consider each database independently from the others [62]. A limited subset of these resources is devoted
to the 3D structure of biological objects (proteins, nucleic acids, glycanes...). Structural information is
also contained in databases classifying protein domains as building blocks of proteins that can be reused
in different proteins sharing the same function (Pfam, CATH and InterPro are well-known examples
of such databases) [56, 67, 26]. There are millions of proteins across all living species but only tens of
thousands of domains that are combined in proteins. Thus, complex tasks such as predicting protein
function or interactions can be simplified when envisaged at the domain level.
Due to the great diversity of databases, Knowledge Graphs (KGs) are more and more used to represent and
integrate biological information. There is no single definition of KGs as these graphs cover a large variety
of domains and data representation contexts (for instance the GAFAM companies advertize various KG
uses). The main feature that differentiates KGs from classical graphs is the fact that both nodes (or entities)
and edges (or relations) in the graph are heterogeneous and belong to various types described in the KG
schema (metagraph). The field of biological knowledge discovery in KG is expanding rapidly [52]. Most
biological KGs today are developed for drug repurposing tasks (e.g. HetioNet [41] or DRKG). Clinicians
are also very interested in network science carried out on rich knowledge graphs as a mean to interpret
biomarker studies. However, there is still a need for curated, reliable biological KGs and for efficient
knowledge discovery methods in KGs.

3.1.2 Knowledge discovery from protein structural databases

Concerning protein structural databases, we aim to explore novel classification paradigms exploiting
existing resources about protein folds and domains [21, 22, 56, 67]. In particular it will be interesting to
use Kpax, our structural alignment tool [63], to define domain-domain similarity matrices. A non-trivial
issue with clustering is how to handle similarity rather than mathematical distance matrices, and how
to find the optimal number of clusters, especially when using a non-Euclidean similarity measure. We
will adapt the algorithms and the calculation of quality indices to the Kpax similarity measure. More
fundamentally, it will be necessary to integrate this classification step in the more general KDD process
leading from data to knowledge.

For example, protein domain classification is relevant for studying domain-domain interactions
(DDI). Our previous work on Knowledge-Based Docking (KBDOCK, [38, 40]) will be updated and extended
using newly published DDIs. Methods for inferring new DDIs from existing protein-protein interactions
(PPIs) will be developed. Efforts should be made for validating such inferred DDIs so that they can be
used to enrich DDI classification and predict new PPIs.

3.1.3 Function Annotation in Large Protein Graphs

Knowledge of the functional properties of proteins can shed considerable light on how they might
interact. However, huge numbers of protein sequences in public databases such as UniProt/TrEMBL
lack any functional annotation, and the functional annotation of such sequences is a highly challenging

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problem. We are developing graph-based and machine learning techniques to annotate automatically
the available unannotated sequences with functional properties such as Enzyme Commission (EC)
numbers and Gene Ontology (GO) terms (note that these terms are organised hierarchically allowing
generalization/specialization reasoning). The idea is to transfer annotations from expert-reviewed
sequences present in the UniProt/SwissProt database (about 560 thousands entries) to unreviewed
sequences present in the UniProt/TrEMBL database (about 80% of 180 millions entries). For this, we have
to learn from the UniProt/SwissProt database how to compute the similarity of proteins sharing identical
or similar functional annotations. Various similarity measures can be tested using cross-validation
approches in the UniProt/SwissProt database. For instance, we can use primary sequence or domain
signature similarities. More complex similarities can be computed with graph-embedding techniques.

3.1.4 Knowledge discovery algorithms in large biological knowledge graphs

KGs are particularly useful and appropriate in biology, to represent and integrate the complex contents
of biological databases [60]. We intend to design algorithms for leveraging information embedded in
biological KGs (also known as complex networks). In biology, KGs mostly represent PPIs, integrated
with various properties attached to proteins, such as pathways, drug binding or relation with diseases.
Setting up similarity measures for proteins in a knowledge graph is a difficult challenge. Our objective
is to extract useful knowledge from such graphs in order to better understand and highlight the role of
multi-component assemblies in various types of cell or organisms. Ultimately, knowledge graphs can be
used to model and simulate the functioning of such molecular machinery in the context of the living cell,
under physiological or pathological conditions.

3.2 Integrative Multi-Component Assembly and Modelling

3.2.1 Context

This axis deals with 3D protein structure and interactions. In fact, the long-lasting problem of predicting
a 3D structure from a protein sequence has been solved in 2021 by the AlphaFold2 (DeepMind) [46]
or RosettaFold methods [24]. This success, revealed in the CASP14 (Critical Assessment of Stucture
Prediction) challenge, was possible not only thanks to AI methods but also because the amount of
experimental 3D structures has reached a sufficient size in the Protein Data Bank (PDB). For the same
type of reasons, the rigid docking problem (in which the bodies to dock are rigid) seems to be on the way
to being solved as well [28, 35]. However, research is still required to address the problem of docking
disordered proteins or flexible nucleic acids that will fold as they bind to proteins. This is the direction
taken by the team since the arrival of Isaure Chauvot de Beauchêne, the inventor of a fragment-based
approach for RNA docking onto proteins.
Modeling protein - and even more RNA - flexibility accurately during docking is very computationally
expensive. This is due to the very large number of internal degrees of freedom in each molecule, associated
with twisting motions around covalent bonds. Therefore, it is highly impractical to use detailed force-field
or geometric representations in a brute-force docking search. Instead, most docking algorithms use fast
heuristic methods to perform an initial rigid-body search in order to locate a relatively small number of
candidate binding orientations, and these are then refined using a more expensive interaction potential
or force-field model, which might also include flexible refinement using molecular dynamics (MD), for
example.

3.2.2 Coarse-Grained Models

Many approaches have been proposed in the literature to take into account protein (and more recently
RNA/DNA) flexibility during docking. The most thorough methods rely on expensive atomistic simu-
lations using MD. However, much of a MD trajectory is unlikely to be relevant to a docking encounter
unless it is constrained to explore a putative protein-protein/NA interface. Consequently, MD is normally
only used to refine a small number of candidate rigid body docking poses. A much faster - but more
approximate - method is to use "coarse-grained" (CG) normal mode analysis (NMA) techniques to reduce
the number of flexible degrees of freedom to just one or a handful of the most significant vibrational
modes [34, 54, 57, 59]. In our experience, docking ensembles of NMA conformations do not give much

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Inria Annual Report 2023

improvement over basic FFT-based soft docking [71], and it is very computationally expensive to use
side-chain repacking to refine candidate soft docking poses [39].

In the last few years, CG force-field models have become increasingly popular in the MD community
because they allow very large biomolecular systems to be simulated using conventional MD programs [23].
Typically, a CG force-field representation replaces the 5-15 atoms in each amino acid with 2-4 “pseudo-
atoms” (each pseudo-atom represents few atoms of an amino-acid as a single bead). It then assigns each
pseudo-atom a small number of parameters to represent its chemo-physical properties. By directly at-
tacking the quadratic nature of pair-wise energy functions, coarse-graining can speed up MD simulations
by up to three orders of magnitude. Nonetheless, such CG models can still produce useful models of
very large multi-component assemblies [66]. Furthermore, this type of CG model effectively integrates
many internal DOFs to build a smoother but still physically realistic energy surface [42]. We are currently
developing a CG scoring function for RNA-protein docking by fragments assembly.

3.2.3 Assembling Multi-Component Complexes and Integrative Structure Modelling

We also want to develop related approaches for integrative structure modeling using cryo-electron
microscopy (cryo-EM). Thanks to recent developments in cryo-EM instruments and technologies, it is
now feasible to capture low resolution images of very large macromolecular machines. However, while
such developments offer the intriguing prospect of being able to trap biological systems in unprecedented
levels of detail, there will also come with an increasing need to analyse, annotate, and interpret the
enormous volumes of data that will soon flow from the latest instruments. In particular, a new challenge
that is emerging is how to fit previously solved high resolution protein structures into low resolution
cryo-EM density maps. However, the problem here is that large molecular machines will have multiple
sub-components, some of which will be unknown, and many of which will fit each part of the map almost
equally well. Thus, the general problem of building high resolution 3D models from cryo-EM data is like
building a complex 3D jigsaw puzzle in which several pieces may be unknown or missing, and none of
which will fit perfectly. We wish to proceed firstly by putting more emphasis on the single-body terms in
the scoring function [33], and secondly by using fast CG representations and knowledge-based distance
restraints to prune large regions of the search space, as initiated with the EROS-DOCK software [6, 65].

3.2.4 Protein-Nucleic Acid Interactions

As well as playing an essential role in the translation of DNA into proteins, RNA molecules carry out
many other essential biological functions in cells, often through their interactions with proteins. A critical
challenge in modeling such interactions computationally is that the RNA is often highly flexible, especially
in single-stranded (ssRNA) regions of its structure. These flexible regions are often very important
because it is through their flexibility that the RNA can adjust its 3D conformation in order to bind to a
protein surface. However, conventional protein-protein docking algorithms generally assume that the 3D
structures to be docked are rigid, and so are not suitable for modeling protein-RNA interactions. There is
therefore much interest in developing dedicated protein-RNA docking algorithms which can take RNA
flexibility into account. This research topic has been initiated with the recruitement of Isaure Chauvot
de Beauchêne in 2016 and is becoming a major activity in the team. A novel flexible docking algorithm
is currently under development in the team. It first docks small fragments of ssRNA (typically three
nucleotides at a time) onto a protein surface, and then combinatorially reassembles those fragments in
order to recover a contiguous ssRNA structure on the protein surface [29, 30].

As the correctness of the initial docking of the fragments settles an upper limit to the correctness of
the full model, we are now focusing on improving that step. A key component of our docking tool is the
energy function of the protein-fragment interactions that is used both to drive the sampling (positioning
of the fragments) by minimization, and to discriminate the correct final positions from decoys (i.e., false
positives). We are developing a new approach to create knowledge-based parameters for coarse-grain
energy functions from public structural data, in collaboration with Sjoerd de Vries (INSERM). Such
approach will be applied first to ssRNA-protein complexes, then to other types of complexes such as
protein-peptides.

Another key requirement for this approach is an exhaustive but non-redundant library of possible
internal conformations of RNA fragments. Our library is built by clustering hundreds of thousands of

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experimentally known RNA structures, based on an approximate geometric similarity criteria. We want
to develop new algorithms for the clustering of 3D conformations based on internal coordinates and on
epsilon-net theory, in order to optimise the representativity and computational cost of the library.

In the future, we will improve the combinatorial algorithm used for reassembling the docked frag-
ments using both experimental constraints and knowledge-based constraints pertaining from the re-
search carried out in Axis 1.

4 Application domains

4.1 Biomedical Knowledge Discovery

Participants: Marie-Dominique Devignes

(contact person), Malika Smaïl-
Tabbone (contact person), Sabeur Aridhi, Kamrul Islam, Athén-
aïs Vaginay.

Our main application for Axis 1 : "New Approaches for Knowledge Discovery in Structural Databases",
concerns biomedical knowledge discovery. We intend to develop KDD approaches on preclinical (experi-
mental) or clinical datasets integrated with knowledge graphs with a focus on discovering which PPIs or
molecular machines play an essentiel role in the onset of a disease and/or for personalised medicine.

As a first step we have been involved since 2015 in the ANR RHU “FIGHT-HF” (Fight Heart Failure)
project, which is coordinated by the CIC-P (Centre d’Investigation Clinique Plurithématique) at the CHRU
Nancy and INSERM U1116. In this project, the molecular mechanisms that underly heart failure (HF) are
re-visited at the cellular and tissue levels in order to adapt treatments to patients’ needs in a more person-
alised way. The CAPSID team is in charge of a workpackage dedicated to network science. A platform has
been constructed with the help of a company called Edgeleap (Utrecht, NL) in which biological molecular
data and ontologies, available from public sources, are represented in a single integrated complex network
also known as knowledge graph. We are developing querying and analysis facilities to help biologists and
clinicians interpreting their cohort results in the light of existing interactions and knowledge. We are also
currently analysing pre-clinical data produced at the INSERM unit on the comparison of aging process
in obese versus lean rats. Using our expertise in receptor-ligand docking, we are investigating possible
cross-talks between mineralocorticoid and other nuclear receptors.

Another application is carried out in the context of an interdisciplinary project funded by the Uni-
versité de Lorraine, in collaboration with the CRAN laboratory. It concerns the study of the role of estrogen
receptors in the development of gliobastoma tumors. The available data is high-dimensional but involves
rather small numbers of samples. The challenge is to identify relevant sets of genes which are differentially
expressed in various phenotyped groups (w.r.t. gender, age, tumor grade). The objectives are to infer
pathways involving these genes and to propose candidate models of tumor development which will be
experimentally tested thanks to an ex-vivo experimental system available at the CRAN.

Finally, simulating biological networks will be important to understand biological systems and test
new hypotheses. One major challenge is the identification of perturbations responsible for the transform-
ation of a healthy system to a pathological one and the discovery of therapeutic targets to reverse this
transformation. Control theory, which consists in finding interventions on a system in order to prevent it
to go in undesirable states or to force it to converge towards a desired state, is of great interest for this
challenge. It can be formulated as “How to force a broken system (pathological) to act as it should do
(normal state)?”. Many formalisms are used to model biological processes, such as Differential Equations
(DE), Boolean Networks (BN), cellular automata. In her PhD thesis, Athenaïs Vaginay investigates ways to
find a BN fitting both the knowledge about topology and state transitions “inferred“ from experimental
data. This step is known as “boolean function synthesis”. Our aim is to design automated methods for
building biological networks and define operators to intervene on them [70]. Our approaches will be
driven by knowledge and will keep close connection with experimental data.

4.2 Prokaryotic Type IV Secretion Systems

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Inria Annual Report 2023

Participants:

Isaure Chauvot de Beauchêne (contact person), Marie-Dominique Dev-
ignes, Bernard Maigret, Dominique Mias-Lucquin.

Concerning Axis 2 : "Integrative Multi-Component Assembly and Modeling", our first application

domain is related to prokaryotic Type IV secretion systems.

Prokaryotic type IV secretion systems constitute a fascinating example of a family of nanomachines
capable of translocating DNA and protein molecules through the cell membrane from one cell to an-
other [20]. The complete system involves at least 12 proteins. The structure of the core channel involving
three of these proteins has recently been determined by cryo-EM experiments for Gram-negative bac-
teria [37, 64]. However, the detailed nature of the interactions between the other components and the
core channel remains to be found. Therefore, these secretion systems represent a family of complex
biological systems that call for integrated modeling approaches to fully understand their machinery.

In the framework of the Lorraine Université d’Excellence (LUE-FEDER) “CITRAM” project we are
pursuing our collaboration with Nathalie Leblond of the Genome Dynamics and Microbial Adaptation
(DynAMic) laboratory (UMR 1128, Université de Lorraine, INRAE) on the mechanism of horizontal
transfer by integrative conjugative elements (ICEs) and integrative mobilisable elements (IMEs) in
prokaryotic genomes. These elements use type IV secretion systems for transferring DNA horizontally
from one cell to another. We have discovered more than 200 new ICEs/IMEs by systematic exploration of
72 Streptococcus genomes and characterised a new class of relaxases [68]. We have modeled the dimer of
this relaxase protein by homology with a known structure. For this, we have created a new pipeline to
model symmetrical dimers of multi-domains proteins. As one activity of the relaxase is to cut the DNA for
its transfer, we are also currently studying the DNA-protein interactions that are involved in this very first
step of horizontal transfer (see next section).

4.3 Protein - RNA Interactions

Participants:

Isaure Chauvot de Beauchêne (contact person), Antoine Moniot,
Anna Kravchenko, Hrishikesh Dhondge, Marie-Dominique Devignes,
Malika Smaïl-Tabbone.

The second application domain of Axis 2 concerns protein-nucleic acid interactions. We need to
assess and optimise our new algorithms on concrete protein-nucleic acid complexes in close collabora-
tion with external partners coming from the experimental field of structural biology. To facilitate such
collaborations, we are creating automated and re-usable protein-nucleic acid docking pipelines.

This is the case for our PEPS collaboration “InterANRIL” with the IMoPA lab (CNRS-Université de
Lorraine). We are currently working with biologists to apply our fragment-based docking approach [30]
to model complexes of the long non-coding RNA (lncRNA) ANRIL with proteins and DNA [19].

In the framework of our LUE-FEDER CITRAM project (see above), we are adapting this approach and
pipeline to single-strand DNA docking, in order to model the complex formed by a bacterial relaxase and
its target DNA [68].

In the framework of our H2020 ITN project RNAct, we tackle a defined group of RNA-binding proteins
containing RNA-Recognition Motifs (RRM) [53, 31]. We study existing and predicted complexes between
various types of RRMs and various RNA sequences in order to infer rules of their sequence-structure-
interaction relationship, and to help design new synthetic proteins with targeted RNA specificity. This
work is made in tight collaboration with computer scientists and biophysicists of the consortium.

4.4 3D structural differences among HLA antigens

Participants: Marie-Dominique Devignes (contact person), Malika Smaïl-Tabbone,

Diego Amaya Ramirez, Bernard Maigret.

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This application domain has emerged in Axis 2 through the Inria-Inserm PhD thesis project of Diego
Amaya Ramirez, in collaboration with the Immunology and Histocompatibility Laboratory at the APHP
Saint-Louis Hospital in Paris. Differences between donor and recipient HLA proteins are one of the major
limitations of organ transplant because of HLA ubiquity on cells of tissues and organs [27]. Indeed, in case
of incompatibility between the HLA proteins of the donor and those of the patient, an immune response
is triggered in the patient that can result in rejection of the transplanted organ. The thesis project aims at
deciphering the role played by tiny 3D structure differences between donor and recipient HLA proteins
in determining the production of donor-specific antibodies by the recipient. We are currently developing
methods to compare local structure variations between HLA proteins, taking into account the dynamics
of these proteins.

4.5 Investigating the dynamical behaviour of protein complexes with MD simulation

Participants:

Yasaman Karami
Isaure Chauvot de Beauchêne, Viktor Pryakhin.

(contact person), Malika

Smaïl-Tabbone,

Allosteric pathways in protein-nucleic acid complexes. Novel methodological approaches based on
deep learning (AlphaFold2 and RosettaFold) have started to make remarkable advances in protein struc-
ture prediction and design. However, our knowledge regarding their dynamical behaviour and function is
still highly limited. One important example is the notion of allostery which refers to processes whereby a
binding event at one site of a biological macromolecule affects the binding activity at another distinct
functional site, enabling the regulation of the corresponding function. The allosteric behavior of a mac-
romolecular system arises from the properties of the native free-energy landscape of the system, and
how this landscape is remodeled by various perturbations, such as ligand binding, protonation, muta-
tions, post-translational modifications, or interactions with other molecules. Therefore, understanding
allosteric pathways of communication is of high importance and is not well understood yet. All-atom
MD simulations could be used to capture subtle dynamical changes that are associated with allosteric
signaling. Moreover, graph theory-based methods were developed to investigate the set of trajectories
generated by MD simulations and extract allosteric pathways. Yasaman Karami has previously developed
a method called COMMA (COMmunication Mapping) that describes the dynamical architecture of a pro-
tein by predicting the network of communications within the system [48] and she is constantly working
at improving it.
In 2023, Benjamin Gottis was hired for an M2 internship (6 months, February-July) to improve COMMA
for the analysis of large protein complexes. His internship resulted in the improvement of threshold
definitions in COMMA and accelerating the calculations. Moreover, Yasaman Karami and Malika Smaïl-
Tabbone recruited a PhD student, Victor Pryakhin who obtained a doctoral contract from Université
de Lorraine and started in October 2023. His PhD subject is to investigate communication networks
in protein-RNA complexes using deep learning approaches. At the same time, Yasaman Karami has
obtained computational resources on Jean Zay super computer to perform MD simulations on a set
of protein-RNA complexes. The results of this computations will be used in Viktor Pryakhin’s doctoral
project.

Dynamics of Type IV Pilus. Type IV pili (T4P) are dynamic filaments at the surface of many bacteria
that can rapidly extend and retract and withstand strong forces. T4P are responsible for various cellular
processes due to their highly dynamic behavior and are crucial for bacterial virulence in many human
pathogens, including Enterohemorrhagic Escherichia coli (EHEC), Pseudomonas aeruginosa (PaK), Neis-
seria meningitidis (Nm), Neisseria gonorrhoeae (Ng), and Myxococcus xanthus (Mx). They are assembled
by complex protein machinery localized in the bacterial envelope, formed by the repeat of major pilin
subunits organised in a helical manner. The structure of these T4Ps have been determined by combining
NMR with a cryo-EM density map of the pilus, resulting in an atomistic model of the T4Ps filament.
Despite the recent progress in cryo-EM and integrative structural biology, the high flexibility of this family
of fibers often limits the resolution of the structure. Modeling is therefore a necessary step in structure
determination. To unravel the structural basis of different T4Ps dynamics, we performed extensive and

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large-scale classical and steered MD simulations of five different T4Ps, EHEC, PaK, Nm, Ng and Mx. The
results highlighted key regions for the function of those filaments. Such simulations require an important
number of computational resources, which were provided thanks to the Swiss National Supercomputing
Centre (CSCS) and GENCI computing center. This project is one of the main activities of Yasaman Karami,
in collaboration with Michael Nilges (Institut Pasteur, Paris) and Edward Egelman (University of Virginia,
USA).

4.6 Learning from protein surfaces to accurately predict their functional sites.

Participants: Hamed Khakzad (contact person), Marie-Dominique Devignes,

Omid Mokhtari.

Today, deep learning from large datasets of sequences and structures (AlphaFold, RosettaFold) enables
structure prediction from sequence with remarkable accuracy. However, the performance of these
methods on predicting protein-protein interactions (PPIs) is still arguable as in the case of AlphaFold
multimer [35, 51]. More importantly, none of these methods can address the interactions between
other types of macromolecules such as DNA, RNA, and ligands. Taking a different stance, this project
is focused on a different aspect of macromolecular structure; the surface. Extracting and learning
surface features, which are critical for function including the interaction with other macromolecules,
has been less explored than other aspects of macromolecular structures. The idea here is that the
surface geometrical and chemical patterns could be used to understand and predict important aspects
of macromolecular functions. Although these features are difficult to determine visually, they can be
learned by a deep neural network trained over large-scale datasets. This project will attempt to create a
broader methodological framework in comparison to the existing approaches that can be used to capture
aspects of surface conformational diversity, and interactions with different types of macromolecules
including small molecules (ligands), protein-RNA, protein-DNA, and protein- protein interactions. This
project is part of Hamed Khakzad’s ANR-CPJ funding, and a PhD student (Omid Mokhtari) was recruited
in October 2023 to work on this topic.

5 Social and environmental responsibility

5.1 Environmental Footprint of Research Activities

In structural bioinformatics and deep learning approaches, the computational costs are usually very high.
The CAPSID team pays attention to use shared equipment (Platform MBI-DS4H, Grid5K) for running
HPC (High Performance Computing) jobs as efficiently as possible. In particular, we use the "best effort"
mode for distributed jobs.
When travelling to conferences, members of the CAPSID team prefer the train solution as often as possible.

6 New software, platforms, open data

6.1 New software

6.1.1 CROMAST

Name: Cross-Mapper of domain Structural instances

Keywords: Protein domain, 3D structure, Classification, Databases, Workflow

Scientific Description: see the scientific paper 10.1093/bioadv/vbad081

Functional Description: CroMaSt (Cross Mapper of domain Structural instances) is an automated it-
erative workflow to clarify the assignment of protein domains to a given domain type of interest,
based on their 3D structure and by cross-mapping of domain structural instances between domain

Project CAPSID

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databases. CroMaSt (for Cross-Mapper of domain Structural instances) will classify all structural
instances of a given domain type into 4 different categories (Core, True, Domain-like, and Failed)

Release Contributions: Operational version for publication

URL: https://workflowhub.eu/workflows/390

Publication: hal-04210856

Contact: Hrishikesh Dhondge

6.1.2 HIPPO

Name: HIstogram-based Pseudo-POtential

Keywords: Structural Biology, Computational biology

Functional Description: Pipeline to create scoring potentials from docking decoys

Contact: Isaure Chauvot De Beauchêne

6.1.3 RRMpip

Name: RRM_modeling_pipeline

Keywords: Structural Biology, Computational biology

Functional Description: pipeline to create 3D models of RRM protein domains

Contact: Isaure Chauvot De Beauchêne

6.1.4 ssRNATTRACT

Keywords: Computational biology, Structural Biology

Functional Description: 3D modelling of protein-ssRNA complexes by fragment assembly

URL: https://github.com/isaureCdB/ssRNATTRACT

Contact: Isaure Chauvot De Beauchêne

6.2 New platforms

Participants: Marie-Dominique Devignes (scientific responsible), Malika Smaïl-
Tabbone (contact person), Sabeur Aridhi, Bernard Maigret, Antoine Mo-
niot, Diego Amaya Ramirez.

The CAPSID team is at the origin of the creation of the LORIA MBI-DS4H research platform that
provides a shared environment to the CAPSID and ORPAILLEUR teams for running distributed intensive
computation. This platform is also the place for optimizing codes that can be run later on Grid 5K or on
the Jean-Zay supercalculator. Moreover, the platform offers opportunities for newcomers in the team to
get trained to good practices in development and in sharing code and data.

The technical support of the platform is ensured by the LORIA SISR (Service d’Ingénierie en Soutien

de la Recherche) via a private project on gitlab.

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6.3 Open data

Inria Annual Report 2023

Benchmark simulated datasets for clustering of mixed data

Description We deposited in the Inria open data repository seven sets of synthetic datasets to be used for
benchmarking clustering algorithms for mixed (continuous and categorical) data. The synthetic
datasets correspond to 9 simulation designs described in the README file and refer to a 2021
publication in Scientific Reports [61]

Contact Marie-Dominique Devignes

url doi:10.57745/6IFQYQ

Benchmark of proteins bound to ssDNA

Description We explored the Protein Data Bank (PDB) to collect protein-ssDNA structures and create a multi-
conformational docking benchmark including both bound and unbound protein structures. Due to
ssDNA high flexibility when not bound, no ssDNA unbound structure is included in the benchmark.
This benchmark is, to our knowledge, the first one made to peruse available structures of ssDNA-
protein interactions to such an extent, aiming to improve computational docking tools dedicated
to this kind of molecular interactions. Related publication: [55].

Contact Isaure Chauvot de Beauchêne

url doi:10.57745/3W8CCV

MD simulations and ML dataset of HLA-EpiCheck epitope predictor tool

Description This dataset contains all the data used to implement the B-cell epitope predictor tool called HLA-

EpiCheck (preprint available on bioRXiv).

Contact Diego Amaya-Ramirez and Marie-Dominique Devignes

url doi:10.57745/GXZHH8

Experiences with a training DSW knowledge model for early-stage researchers

Description Data management is fast becoming an essential part of scientific practice, driven by open science
and FAIR (findable, accessible, interoperable, and reusable) data sharing requirements. Whilst
data management plans (DMPs) are clear to data management experts and data stewards, under-
standings of their purpose and creation are often obscure to the producers of the data, which in
academic environments are often PhD students. Within the RNAct EU Horizon 2020 ITN project,
we engaged the 10 RNAct early-stage researchers (ESRs) in a training project aimed at formulating
a DMP. To do so, we used the Data Stewardship Wizard (DSW) framework and modified the existing
Life Sciences Knowledge Model into a simplified version aimed at training young scientists, with
computational or experimental backgrounds, in core data management principles. We collected
feedback from the ESRs during this exercise. Here, we introduce our new life-sciences training DMP
template for young scientists. We report and discuss our experiences as principal investigators (PIs)
and ESRs during this project and address the typical difficulties that are encountered in developing
and understanding a DMP. We found that the DS-wizard can also be an appropriate tool for DMP
training, to get terminology and concepts across to researchers [7].

Contact Malika Smaïl-Tabbone and Marie-Dominique Devignes

url doi:10.12688/openreseurope.15609

7 New results

7.1 Axis 1 : Knowledge Discovery in Structural Databases

Participants: Marie-Dominique Devignes, Malika Smaïl-Tabbone, Sabeur Aridhi,

Kamrul Islam, Athénaïs Vaginay.

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7.1.1 Knowledge graph mining with embedding-based methods

In the context of Md Kamrul Islam’s PhD project, we addressed the problem of link prediction in large
knowledge graphs (KGs) using KG embedding methods. These methods aim to learn low dimensional
vector representations of entities and relations in a KG. Such representations (in a latent space) facilitate
link prediction tasks along with other downstream tasks. In this context, it is important to achieve both an
efficient KG embedding and explainable predictions. During learning of efficient embeddings, sampling
negative triples is an important step as KGs only come with observed positive triples. We previoulsy
proposed an efficient simple negative sampling (SNS) method based on the assumption that the entities
which are closer to the corrupted entity in the embedding space are able to provide high-quality negative
triples [44]. As for explainability, we also report in the same paper a new rule mining method which
exploits the learned embeddings [44].
We then extended this work to propose an integrated drug repurposing, evaluation and explanation
pipeline for COVID-19 disease [11]. The workflow starts with collecting and cleaning a COVID-19 centric
drug repurposing knowledge graph (DRKG). Then, high-quality and compact ensemble embeddings are
learned using three embedding methods. The embeddings are then used to train a deep neural network
based model to predict the probability of unobserved triples connecting drugs with 27 COVID-19 proteins
(as drug targets). The top-100 predictions are evaluated based on (i) cross-matching with in-trial drugs
for COVID-19 and (ii) molecular evaluation based on compound and protein structures. Beside these
evaluations, we learn high quality rules from DRKG and provide possible explanations of predictions.
This study demonstrates how complementary embedding methods can be used to generate high-quality
ensemble embeddings of a KG and how to use embeddings for the drug repurposing task. To the best of
our knowledge, it is the first attempt to combine virtual screening methods with KG embedding methods
in predicting and evaluating repurposable drugs for COVID-19. Besides the retrieval of many in-trial drugs,
both methods show a converging result that the Fosinopril compound could be a new potential nsp13
inhibitor. Experimental validation of Fosinopril compound to treat COVID-19 is a potential perspective of
this study. The molecular evaluation results and explanations of the predictions make us confident about
the drawn conclusions. Md Kamrul Islam has successfully defended his PhD on December 16, 2022 [43].

7.1.2 Biological network modeling

Boolean Networks (BNs) refer to a simple formalism used to study complex biological systems when the
prediction of exact reaction times is not of interest. BNs play a key role in understanding the dynamics
of the studied systems and in predicting their disruption in case of complex human diseases. The Bio-
Models database is a well-known repository of peer-reviewed models represented in the Systems Biology
Markup Language (SBML). Most of these models are quantitative, but in some use cases, qualitative
models—such as BNs—are better suited. In the context of Athénais Vaginay’s PhD project, we proposed
SBML2BN, a pipeline dedicated to the automatic transformation of quantitative SBML models to Boolean
networks [69]. Our approach takes advantage of several SBML elements (reactions, rules, events) as well
as a numerical simulation of the concentration of the species over time to constrain both the structure and
the dynamics of the Boolean networks to synthesise. Finding all the BNs complying with given structure
and dynamics was formalised as an optimisation problem formulated in the answer-set programming
framework.
We ran SBML2BN on more than 200 quantitative SBML models, and we could construct Boolean networks
which are compatible with the structure and the dynamics of the SBML models [69]. The most recent
work relies on abstract simulation of a chemical reaction network (CRN) to avoid the tricky binarization
task as we propose to simulate chemical reaction networks with the deterministic semantics abstractly,
without any precise knowledge on the initial concentrations. For this, the concentrations of species
are abstracted to Booleans stating whether the species is present or absent, and the derivatives of the
concentrations are abstracted to signs saying whether the concentration is increasing, decreasing, or
unchanged. We use abstract interpretation over the structure of signs for mapping the ODEs of a reaction
network to a Boolean network with nondeterministic updates. The abstract state transition graph of such
Boolean networks can be computed by finite domain constraint programming over the finite structure of
signs. Constraints on the abstraction of the initial concentrations can be added naturally, leading to an
abstract simulation algorithm that produces only the part of the abstract state transition graph that is

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Inria Annual Report 2023

reachable from the abstraction of the initial state. We proved the soundness of our abstract simulation
algorithm, and showed its applicability to reaction networks in the SBML format from the BioModels
database [14]. Athénaïs Vaginay has successfully defended her PhD on July, 7 2023 [16].

7.1.3 Machine Learning and Scalable Graph-based Approaches

In the context of a collaboration with researchers from both the University Badji Mokhtar-Annaba (Al-
geria), the University of Quebec At Montreal (UQAM) and the University of Lille, Sabeur Aridhi proposed a
genetic algorithm for random forest [12]. The proposed algorithm has three main objectives: (1) strength-
ening the classification accuracy of individual decision trees as well as that of the forest, (2) making use of
diversity measures among the decision trees to improve the generalization of the constructed model, and
(3) minimizing the number of trees in the forest and finding an optimal subset of the random forest.

7.2 Axis 2 : Integrative Multi-Component Assembly and Modeling

Participants:

Isaure Chauvot de Beauchêne, Marie-Dominique Devignes,
Malika Smaïl-Tabbone, Bernard Maigret, Yasaman Karami,
Hamed Khakzad, Dominique Mias-Lucquin, Antoine Moniot,
Anna Kravchenko, Hrishikesh Dhondge, Diego Amaya Ramirez.

7.2.1 Modeling and design of RNA-RRM complexes

Our recent H2020 ITN project RNAct (2018-2022) aimed at designing new RNA-binding proteins based
on the evolutionary conserved protein domain1, called RNA Recognition Motif (RRM). In this context and
in the continuity of this project, we develop approaches to create 3D models of ssRNA-RRM complexes,
building up on our expertise in RNA-protein modeling by fragments assembly [30]. We use the ATTRACT
docking software to sample and evaluate possible (low binding energy) positions of RNA fragments on
the protein surface, then assemble the geometrically compatible poses with compatible sequences, to
build a continuous model for the full RNA sequence.

Empirical inference of an RNA-protein energy function.
In the frame of Anna Kravchenko’s thesis
and in collaboration with Sjoerd de Vries (SISR team, LORIA), we have implemented a new method
to create energy parameters for ssRNA-protein interactions in coarse-grained representation. In the
ATTRACT procedure, each amino-acid of the protein and each nucleotide of the RNA is represented by 2
to 7 pseudo-atoms (“beads”). For each model of an RNA-protein interaction, the energy is computed
as the sum of the bead-bead energies, and the model with the lowest energy is considered as the most
probable. Each bead-bead energy depends solely on the 2 bead types (among 17 RNA types and 32
protein types) and the inter-bead distance. The models are then ranked by energy, in order to select the
top-ranked poses that are supposed to be enriched in correct positions. We previously used parameters
created in 2010 and tailored for double-stranded RNA, but their performance is poor on ssRNA. One
main goal of Anna Kravchenko’s PhD was to optimize those parameters for a better discrimination of
the correct positions for a given ssRNA fragment on an RRM protein domain. In 2022, we have set up a
novel “histogram-based” approach, called HIPPO (HIstogram-based Pseudo-POtential). From a training
set of solved RRM-ssRNA complexes, we derived a set of non-redundant RRM-fragment complexes
(here a fragment is a tri-nucleotide of RNA). From each RRM-fragment complex and for each bead-bead
type, we build a log-odds histogram of the occurrences of bead-bead distances (discretized into bins)
observed in correct/incorrect poses retrieved from the fragment docking run corresponding to this
RRM-fragment complex. The set of histograms obtained for a given RRM-fragment complex is called a
"scoring potential" and later used to score the poses retrieved from fragment docking runs performed
with RRMs and fragments derived from other solved RRM-ssRNA complexes constituting the test set.
In 2023, we have greatly improved this approach by creating a consensus scoring procedure from 4 sets of

1An evolutionary conserved protein domain is a protein domain for which the aligned sequences do not differ much across all

possible living species.

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histogramms/scoring potentials that were identified to cover at best the diversity of RRM-ssRNA binding
modes while avoiding over-fitting. We tested this consensus procedure on a benchmark of RRM-fragment
complexes, extracted from 51 experimental structures of RRM-ssRNA complexes. HIPPO achieved a
successful enrichment in correct poses (60% of correct poses in the 20% top-ranked poses) for 53% of our
RRM-fragment complexes with HIPPO, versus 26% with the old parameters. Most importantly, HIPPO
achieved a high enrichment for at least 1 fragment of the full ssRNA for 75% of the RRM-ssRNA complexes,
versus 54% with the old parameters. This led us to adapt the assembling of geometrically compatible
poses, by retaining less top-ranked poses for one of the fragments (iteratively supposed to be the highly
enriched one), thus decreasing the complexity of the whole fragment-based docking procedure.
As a great surprise, we also found out that those parameters trained on ssRNA-RRM structure only, also
perform better than the old parameters for ssRNA-protein complexes that do not contain the RRM protein
domain. This encourage us to train a general scoring potential for all ssRNA-protein complexes in the
near future. We also found that, in mot cases, a large majority of the top-ranked correct poses are selected
by only one of the 4 sets of histograms. This work was presented at the ISMB-ECCB 2023 international
conference in Lyon in July 2023 [18] and a full paper is actually under revision.
For 2024, a way to improve HIPPO’s performance would be to predict which of the 4 sets will perform
the best on a given protein-fragment case. This would avoid retaining the false positives returned by the
other three. This may be achieved with supervised machine learning techniques based on the sequence
of the fragment and the sequence or/and structure of the protein, and/or on the docking poses. Such a
pre-trained classifier not only would drastically improve the performance of the scoring but could also
give biological insight into the most prevalent protein-ssRNA binding modes.

Search of self-avoiding paths for fragment-based docking. The assembly of compatible poses in our
fragment-based modeling of RNA-protein complexes can be formalized as the search of paths of low
global energy in a graph representing the pairwise compatibility of poses (fragment positions on the
protein), each pose having a binding energy. The algorithm we used so far to sample paths does not
take into account possible steric collisions - or clashes - between non-consecutive fragments. Chains
containing such collisions have to be filtered a posteriori. In collaboration with Yann Ponty (LIX) and
Fabrice Leclerc (IBC), we tried to avoid such collisions more efficiently, by integrating this global constraint
during path sampling. We adapted the historical Noga Alon’s color-coding algorithm2 for the search for
self-avoiding paths of k vertices in a directed graph, in order to circumvent the NP-completeness of this
problem. It consists in randomly assigning one of k colors to each vertex, applying an O(n2k ) algorithm
for finding well-colored paths of size k (1 vertex per color) with minimal global energy, and reiterating
the method E times, where E is the expectation of the waiting time for a good coloring for a given path
(E = (kk )/k!) which can be approximated by ek . In this way, we obtain the best chain of poses with a high
probability in O(n(2e)k )) time, compared to a brute force complexity in nk .
To further reduce the size of the problem, and improve computation time, we have grouped the poses
into cliques of clashing poses, which can therefore be systematically assigned the same color. The goal
of cliques is to maximize the number of graph edges in a clique, i.e. edges which will never be chosen
during path sampling, thus reducing the total number of possible paths. This work was recently accepted
for the international conference RECOMB 2024, to be held April 29 - May2, 2024 in Cambridge MA (USA).

Cross-mapping of protein domain structural instances. To address the issue of inconsistencies of
protein domain classification in different domain databases, we developed a tool named Cross-Mapper
for domain Structural instances (CroMaSt). This tool is a workflow that uses database querying and
structural alignment to assign a confidence level to each 3D structure of a given domain in relation to
its possible membership of a given domain type. This workflow was primarily developed for RRMs, but
can easily be adapted to any structural domain. The workflow has been formalized using the Common
Workflow Language (CWL) 3. Its rationale is based on the cross-mapping of PDB (Protein Data Bank)
entries retrieved as RRM, in the Pfam and CATH domain classifications. Entries that are mapped in both
classifications are considered as the “core” RRMs. Those that are specific to only one classification are
further analyzed using structural alignment, in order to determine whether they are RRM-like or false

2Noga Alon, Raphael Yuster and Uri Zwick. Color-coding. Journal of the ACM, 42:844-856. 1995
3https:www.commonwl.org

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Inria Annual Report 2023

RRMs [8]. The workflow can be used to create datasets for specific domains with a good confidence level,
which are useful for characterizing domain structural diversity or for further analyses such as machine
learning, evolutionary studies or synthetic biology. This work was an important contribution of the
doctoral thesis of Hrishikesh Dhondge who successfully defended it on 11 July 2023 [15].

7.2.2 3D Modeling of proteins and protein complexes

Modeling protein-DNA complexes to fight antibiotic resistance.
In the context of the FEDER-CITRAM
project (collaboration with the DynAMic and LPCT labs, Université de Lorraine), we study a new type
of relaxase (RelSt3) as a key protein for horizontal DNA transfer, one of the processes responsible for
the spread of antibiotic resistance in bacteria. The relaxase cuts parts of the bacterial DNA genome,
then brings the cut DNA piece to the “coupling protein” inserted in the bacterial membrane, which
transfers this piece to the recipient cell. In 2022, we contributed to the exploration of DNA processing
features of RelSt3 [50]. In 2023, we extended this work to the 3D modeling of different types of relaxases
and coupling proteins in different bacteria strains, thanks to AlphaFold and including the evaluation
of the most reliable parts of each model. This paves the way for usage of those models in studying the
relaxase-DNA and relaxase-coupling protein interactions, in order to target them by inhibitors.
In parallel, a new round of virtual screening inhibitors has been carried out targeting the ssDNA binding
pocket of RelSt3. The inhibitory activity of the selected comppounds is currently being tested in the
DynAMic lab.

In the context of the Inria-
Structural basis of donor-specific antibody response in graft rejection.
Inserm PhD project of Diego Amaya Ramirez and in collaboration with the Immunology and Histo-
compatibility Laboratory at the APHP Saint-Louis Hospital in Paris, we study the structural differences
between donor and recipient HLA proteins to understand and possibly predict the immune response
triggered in the recipient, which can result in rejection of the transplanted organ. A dataset of 207 HLA 3D
structures has been created, both from PDB and AlphaFold predictions. Molecular dynamics runs (10 ns)
have been analyzed at the level of single residues or surface patches centered on surface residues. Surface
patches are described by a set of 18 descriptors, including both static and dynamic properties, such as
hydrophobicity, electrostatic charges, relative solvent-accessible surface area and side-chain flexibility. A
machine-learning predictor for B-cell epitopes on HLA proteins (HLE-EpiCheck) has been trained using
an Extra Trees ensemble learning method to discriminate between positive (confirmed epitopes) and
negative (non epitope) surface patches. HLA-EpiCheck prediction performance outperformed the per-
formance achieved by DiscoTope-3.0, a state-of-the-art B-cell epitope predictor in the task of predicting
HLA epitopes. HLA-EpiCheck was also used to assess the epitope status of a subset of non-confirmed
eplets. The predictions were compared to experimental data and a notable consistency was found. These
results suggest that HLA-EpiCheck could be used to better define HLA matching between donor and
recipient in order to reduce de novo DSA formation and graft rejection. This work was presented as a
poster at the ISMB-ECCB 2023 International conference in July 2023 [17]. A full paper has been submitted
for publication in December 2023 (preprint available on bioRXiv).

7.2.3 Investigating the dynamical behaviour of protein complexes with MD simulation

Analysis of COVID-19 spike binding to ACE2.
In collaboration with Laurent Chaloin at the IRIM
(Institut de Recherche en Infectiologie de Montpellier, CNRS-Université de Montpellier), we investigated
the interaction between the RBD domain of SARS-CoV-2 Spike protein and human ACE2 along very
long molecular dynamics (MD) simulations (1.5 µs, performed on Jean Zay super computer at GENCI,
IDRIS-CNRS in Orsay). A panel of methods was used to analyse the MD trajectories. The goal of this
project is to compare the Omicron variants with the Delta variant of SARS-CoV-2. The manuscript is
under revision.

Nucleosomes and post-translational modifications. Nucleosomes are made of proteins and DNA and
Yasaman Karami has started a new collaboration with Emmanuelle Bignon, a recently recruited CNRS
researcher at LPCT (Laboratoire de Physique et Chimie Théorique). Together, they study the effect of

Project CAPSID

17

post-translational modifications of proteins on the nucleosome4, using MD simulations and COMMA
analysis [48]. The results of their work are now under consideration at the Computational and Structural
Biotechnology journal with a favorable revision (preprint available on bioRXiv).

In collaboration with Pontus Nordenfelt (Lund University),
M1-IgG interaction network and dynamics.
Yasaman Karami and Hamed Khakzad investigated the role of hinge flexibility for the opsonic function of
antibodies5. An opsonic IgG1 monoclonal antibody, Ab25 [25], targeting the M protein of the bacteria
Streptococcus pyogenes (S. pyogenes), was engineered into the IgG2-4 subclasses. Despite reduced binding,
the IgG3 version demonstrated enhanced opsonic function. MD simulations showed that IgG3 Fc region
exhibits extensive mobility in 3D space relative to the antigen due to its extended hinge region. The
MD simulations also showed altered Fab-antigen interactions, in line with IgG3 diminished affinity. We
explored the impact of hinge-engineering by generating a panel of IgG antibodies, IgGh, containing the
CH1-3 domains of IgG1 and different segments of IgG3 hinge. Hinge-engineering enhanced opsonic
function, with the most potent hinge having 47 amino acids. IgGh47 far exceeded the parent IgG1 and
even the IgG3 version. The IgGh47 was protective against S. pyogenes in a systemic infection mouse model,
contrary to parent IgG3 and IgG1. The in vitro phenotype of IgGh47 was generalizable to clinical isolates
with different M protein types. Finally, we generated IgGh47 versions of anti-SARS-CoV-2 mAbs, which
exhibited strongly enhanced in vitro opsonic function compared to the original IgG1. The improved
function of the IgGh47 subclass in two distant biological systems provides new insights into antibody
function and how to enhance it for opsonic function. The paper received favorable revision from Nature
Communications (preprint available in BioRXiv [45]).

Unraveling the complexity of glycosaminoglycan deficiencies caused by B3GALT6 loss-of-function
mutations: a multifaceted approach Deleterious variants in beta-1,3-galactosyltransferase (B3GALT6)
compromise the early steps of glycosaminoglycan (GAG) synthesis causing a spondylodysplastic subvari-
ety of Ehlers-Danlos syndromes (spEDS). Unfortunately, the mechanisms by which pathogenic mutations
impair B3GALT6 function and translate into a severe, often lethal, connective tissue disorder are poorly
defined. In collaboration with Sylvie Fournel-Gigleux at IMoPA, Nancy, Yasaman Karami and Hamed
Khakzad dissected the patho-mechanisms of spEDS by a multi-tiered approach from exploring the
protein structure alterations by MD simulations to atomic force microscopy-based extracellular matrix
(ECM) investigations. We found that pathogenic variants produced a unique structural B3GalT6 altera-
tion resulting in loss of function that was partially rescued by glucuronosyltransferase 1 (GlcAT-1), the
next enzyme in the pathway. Transcriptomics revealed that the resulting GAG defects predominantly
dysregulated collagen maturation. In line, by leveraging a B3galT6-invalidated ECM model recapitulating
the condition, we suggest that the absence of collagen XII glycosylation contributes to altered tissue
structure and biomechanics. Our findings uncover a novel link between GAG and collagen defects and
shed light on the pathobiology of spEDS. The manuscript has been submitted recently to Journal of
Clinical Investigations. In continuity with this work, the team is now involved in the ANR GlycoLink
project (2023-2027) coordinated by Sylvie Fournel-Gigleux.

7.2.4 Machine learning methods for proteomics, interactomics and protein design

In the recent years, the application of deep
Deep Learning and de novo MS-based peptide sequencing.
learning represented a breakthrough in the mass spectrometry (MS) field by improving the assignment of
the correct sequence of amino acids from observable MS spectra without prior knowledge, also known
as de novo MS-based peptide sequencing. However, like other modern neural networks, models do not
generalize well enough as they perform poorly on highly varying N- and C-termini peptide test sets. To
mitigate this generalization problem, Hamed Khakzad and colleagues from Lund University (Sweden)
conducted a systematic investigation to unravel the requirements for building generalizable models
and boosting the performance on the MS-based de novo peptide sequencing task. Several experiments
confirmed that the peptide diversity of the training set directly impacts the resulting generalizability of
the model. Data showed that the best models were the multienzyme models (MEMs), i.e., models trained

4A nucleosome is a region of DNA that is wrapped around a core of proteins, inside the cell nucleus.
5Opsonic function: capacity of antibodies to coat pathogens present in the blood and thereby promote their recognition by the

phagocytosis system responsible of their elimination.

18

Inria Annual Report 2023

from a compendium of highly diverse peptides, such as the one generated by digesting samples from a
wide variety of species with a group of proteases. The applicability of these MEMs was later established by
fully de novo sequencing eight of the ten polypeptide chains of five commercial antibodies and extracting
over 10,000 proving peptides[10].

Machine learning and protein design.
In collaboration with the team of Pierre Tuffery (Université
Paris-Cité), Yasaman Karami developed a method to design cyclic peptides and propose candidate linkers.
Large-scale data-mining of available protein structures was previously shown as useful for the precise
identification of protein loop conformations, even from remote structural classes [47]. This approach
was transposed to linkers, allowing head-to-tail peptide cyclization. This project has strong potential
for cyclic petpide-based drug design and was published in 2023 in Journal of Chemical Informatics and
Modelling [49].
From a more general point of view, Hamed Khakzad has written in collaboration with Bruno Correia’s
team at Ecole Polytechnique Fédérale de Lausanne and Michael Bronstein from University of Oxford, a
review on the recent developments and technologies in deep learning methods with examples of their
performance to generate novel functional proteins [13].

7.2.5 Miscellaneous results on structural studies of host-pathogen interactions

Mutational analysis of vinculin and cell adhesion Vinculin is a cytoskeletal linker strengthening cell
adhesion. The Shigella IpaA invasion effector binds to vinculin to promote vinculin supra-activation
associated with head-domain mediated oligomerization. In collaboration with Pr Guy Tran van Nhieu
(I2BC, Université Paris-Saclay), Hamed Khakzad has investigated the impact of mutations within the
vinculin D1D2 subdomains, which are predicted to interact with IpaA VBS3. These mutations influence
the rate of D1D2 trimer formation, with distinct effects on monomer disappearance, consistent with
structural modeling of a “closed” and “open” D1D2 conformer induced by IpaA. Notably, mutations
targeting the closed D1D2 conformer significantly reduced Shigella invasion of host cells, in contrast to a
mutation targeting a putative D2 coiled-coil motif or a cysteine clamp affecting later stages of vinculin
head-domain oligomerization. All mutations affected the focal adhesions (FAs) formation. Our findings
suggest that IpaA-induced vinculin supra-activation primarily reinforces matrix adhesion in infected cells,
rather than promoting bacterial invasion. Consistently, shear stress studies pointed to a key role for IpaA-
induced vinculin supra-activation in accelerating and strengthening cell matrix adhesion. Additionally,
our results support the involvement of vinculin supra-activation in FAs maturation and cell adhesion. The
manuscript received favorable revisions from Life Science Alliance (preprint available in bioRXiv [32]).
This work initiated the ANR grant application TRIVIAL under evaluation for the AAPG2024.

Functional proteomics to reveal streptolysin O as a novel plasminogen-binding streptococcal protein
The bacteria S. pyogenes is a highly adaptive human specific pathogen weaponized with multifunctional
bacterial proteins, known to exploit the plasminogen (PLG)-plasmin (PLM) system to promote its dis-
semination and survival in the human host. In collaboration with Pr Johan Malmström (Lund University,
Sweden), Hamed Khakzad applied a series of functional proteomics methods to chart the protein-protein
interaction landscape centred on streptolysin O (SLO), revealing that this critical cytolytic toxin binds
specifically to PLG. Binding of SLO to PLG potentially alters the shape of PLG from a compact to a partially
relaxed conformation, thereby accelerating more PLM production via tissue-type plasminogen activator.
Our results reveal a conserved moonlighting pathomechanistic role for SLO carried in all S. pyogenes
isolates, extending beyond its established cytolytic activity. A deeper investigation is warranted in order
to better understand the profound relationships between bacterial adaptation and host haemostasis
during different stages of infection. A manuscript is under preparation to describe this work.

8 Partnerships and cooperations

8.1 International initiatives

8.1.1 Participation in other International Programs

Project CAPSID

NewDAFI

19

Participants: Malika Smaïl-Tabbone, Sabeur Aridhi, Marie-Dominique Devignes,

Bernard Maigret.

Funding: COFECUB - CAPES 2023

Title: New drugs against invasive fungal infections: from hits to optimised leads through machine

learning

Partner Institution(s): The Catholic University of Brasília (UCB), Brazil

Date/Duration: 2023-2026

Additionnal info/keywords: The main objective of this project is to convert the previous knowledge
acquired, from comparative genomics, selection of new therapeutic targets and identification of a
new class of antifungals, into a product that can reach the preclinical phase, and that effectively
contributes to the fight against fungal emerging diseases and nosocomial infections. Our ambition
is also to perpetuate the international scientific exchange between Brazil and France for training
qualified human resources, especially in interdisciplinary areas.

8.2 International research visitors

8.2.1 Visits of international scientists

Other international visits to the team

Pr Edward Egelman

Status Professor

Institution of origin: University of Virginia

Country: USA

Dates: 11-13 July 2023

Context of the visit: Ongoing collaboration with Yasaman Karami and 1st Nancy Computational Struc-

tural Biology (NCSB) day

Mobility program/type of mobility: Invitation for a lecture and scientific discussions

Pr Maria Sueli Felipe

Status Professor

Institution of origin: Catholic University of Brasilia

Country: Brazil

Dates: 22-27 October 2023

Context of the visit: Exchange of senior scientists in the frame of a COFECUB-CAPES program (2023-

2026) coordinated by Malika Smaïl-Tabbone.

Mobility program/type of mobility: Lecture and scientific discussions

8.2.2 Visits to international teams

Research stays abroad

20

Inria Annual Report 2023

Marie-Dominique Devignes

Visited institution: Catholic University of Brasilia and State University of Maringa

Country: Brazil

Dates: 20-30 November 2023

Context of the visit: Exchange of senior scientists in the frame of a COFECUB-CAPES program (2023-

2026) coordinated by Malika Smaïl-Tabbone.

Mobility program/type of mobility: Lectures and Master Classes

Sabeur Aridhi

Visited institution: University of Trento, Pr Alberto Montresor.

Country: Italy

Dates: 21-26 May 2023

Context of the visit: Inria delegation and short fellowship support (LORIA).

Mobility program/type of mobility: scientific discussions.

8.3 European initiatives

8.3.1 Other european programs/initiatives

Through her implication in the French institute of Bioinformatics (joint coordination of the Open Sci-
ence and Interoperability taskforce), Marie-Dominique Devignes is a member of the European ELIXIR
Interoperability platform.

8.4 National initiatives
ANR EPIHLA

Participants: Marie-Dominique Devignes (contact person), Malika Smaïl-Tabbone,

Diego Amaya Ramirez, Bernard Maigret.

Title: HLA compatibility in organ transplantation : from antigens to epitopes (EPIHLA)

Duration: October 2022-October 2025

Coordinator: Pr. Jean-Luc Taupin (Inserm U976, Saint-Louis Hospital, Paris)

Inria contact: Marie-Dominique Devignes

Partner Institutions:

• Inserm U976 IRSL Saint-Louis Hospital (Paris)

• LORIA CNRS (Nancy)

• INSERM U1016 Cochin Institute (Paris)

• CNRS U144 Institut Curie (Paris)

Summary: The EPIHLA project has two major aims. (1) It aims at correctly representing HLA molecule
3D structure and superimposing predicted conformations in order to identify 3D differences that
could constitute epitopes and eplets, targets of donor-specific antibodies. (2) It aims at developing
the capacity to isolate and clone anti-HLA antibody genes from patients’ B lymphocytes. The results
will provide decisive new information on the understanding of humoral alloreactivity and will make
it possible to better anticipate transplant rejection. This project was initially based on the Inria-
Inserm PhD project of Diego Amaya Ramirez (2019-2022). This thesis ("HLA genetic system and
organ transplantation: understanding the basics of immunogenicity to improve donor - receptor
compatibility when assigning grafts to recipients") is not finished yet and still co-supervised by
Marie-Dominique Devignes and Pr. Jean-Luc Taupin.

Project CAPSID

ANR GlycoLink

21

Participants: Hamed Khakzad (contact person), Yasaman Karami, Isaure Chauvot

de Beauchêne.

Title: Exploring Glycosyltransferases complexes involved in glycosaminoglycan-Linker assembly

Duration: October 2023-October 2026

Coordinator: Pr. Sylvie Fournel-Gigleux (UMR 7365 CNRS-Université de Lorraine)

Inria contact: Hamed Khakzad

Partner Institutions:

• UMR 7365 CNRS-Université de Lorraine (Nancy)

• IBS - UMR 5075 (Grenoble)

• INRIA Center of Université de Lorraine (Nancy)

• ICOA, UMR 7311 CNRS U-Orléans (Orléans)

Summary: The supramolecular arrangement of glycosyltransferases (GT) and accessory enzymes (kinase,
phosphatase, sulfotransferases) governs the repertoire of carbohydrates and their multiple biolo-
gical functions. The assembly of glycosaminoglycans (GAG), a major class of linear glycopolymers,
is initiated by a unique tetrasaccharide linker (GlcAb1-3Galb1-3Galb1-4Xyl) synthesized by the
coordinated action of five GT. It serves as a unique primer for the elongation of GAG chains. A
recent paradigm puts forward a multimolecular complex called “GAGosome" formed of GT and
auxiliary enzymes as a major mechanism to guarantee fidelity and efficiency of GAG synthesis.
Indeed, association of the heparan-sulfate (HS) polymerases EXT1/EXT2 and of chondroitin-sulfate
(CS) synthases (CSS) sustain GAG elongation. Recent evidence also supports complex formation
between GAG-linker enzymes. GlycoLink will explore the formation and organization of mul-
timolecular complexes between GT and partners involved in the assembly of the GAG-linker region,
and their functional impact in rare genetic diseases.

8.5 Regional initiatives

LUE-FEDER CITRAM (2017-2023) The CITRAM project (Conception d’Inhibiteurs de la Transmission
de Résistances AntI-Microbiennes), co-funded by Lorraine Université d’Excellence (LUE) and FEDER was
extended until June 2023.
Partners other than CAPSID are:

• DynAMic lab (Genome dynamics and microbial adaptation, INRAE-Université de Lorraine UMR

1128), Team of Nathalie Leblond, coordinator ;

• LPCT (Laboratoire de Physique et Chimie Théoriques, CNRS-Université de Lorraine UMR 7019),

Team of Chris Chipot.

MolAI4Cryo (2023-2025) The molAI4Cryo project (Modeling and Artificial Intelligence applied to Cryo-
EM 3D structures to fight COVID) has obtained a grant from the Région Grand-Est to equip the IMoPA
laboratory with a cutting-edge equipement in cryo-electromicroscopy. The CAPSID team will be involved
in proposing algorithms to use cryo-EM results as constraints for modeling protein-RNA complexes.
Partners other than CAPSID are:

• IMoPA (Ingénierie Moléculaire et Physiopathologie Articulaire, CNRS-Université de Lorraine UMR

7365), Team of Xavier Manival, coordinator ;

• DynAMic lab (Genome dynamics and microbial adaptation, INRAE-Université de Lorraine UMR

1128), Team of Nathalie Leblond and Nicolas Soler ;

• LPCT (Laboratoire de Physique et Chimie Théoriques, CNRS-Université de Lorraine UMR 7019),

Team of Chris Chipot and François Dehez.

22

Inria Annual Report 2023

9 Dissemination

Participants:

All Team Members.

9.1 Promoting scientific activities

9.1.1 Scientific events: organisation

Yasaman Karami organized in Nancy (12 July 2023) a seminar called NCSB (Nancy Computational
Structural Biology) with the help from both Inria and Loria. The event was held successfully with about
50 participants at the Université de Lorraine. We had the pleasure of having Professor Edward Egelman as
the keynote speaker. In total we had 7 oral communications by both regional (IMoPA, DynaAMic, LPCT,
Inria-Loria) and international (Germany, USA) scientists.

Member of the organizing committees
Isaure Chauvot de Beauchêne was member of the organizing
committee of AlgoSB Winter school 2023: "From structure resolution to dynamical modeling in cryo-
electron microscopy".

Member of the conference program committees Malika Smaïl-Tabbone was member of the program
committee of DATA ANALYTICS 2023 international conference and EGC 2023 francophone conference.
Sabeur Aridhi was member of the program committee of IEEE Big Data 2023 international conference
and of the ICML (International Conference on Machine Learning) Workshop on Computational Biology
(WCB) 2023. Isaure Chauvot de Beauchêne was member of the program committee of ISMB-ECCB 2023
international conference (Lyon, 23-27 July 2023).

Member of the editorial boards Malika Smaïl-Tabbone is member of the Scientific Reports Editorial
Board (from December 2023).
Marie-Dominique Devignes is guest editor of Bioinformatic Advances.

Reviewer - reviewing activities Marie-Dominique Devignes reviewed articles for Computational Struc-
tural Biotechnology Journal.
Yasaman Karami reviewed articles for Nucleic Acid Research (NAR) and Computational Structural Bio-
technology journals.

9.1.2 Invited talks

Marie-Dominique Devignes gave an invited talk on "Open Science at the French Institute of Bioinformat-
ics" during the Love Data Week organized at the Université de Lorraine, 13-16 March 2023.
Yasaman Karami gave an invited talk on “Conformational dynamics of Type-IV Pilus” during the Statistical
Physics and Low Dimensional systems (SPLDS) Conference organized by the LPCT lab at Pont-a-Mousson,
24-26 May, 2023.

9.1.3 Leadership within the scientific community

Marie-Dominique Devignes is co-leader of the Open Science and Interoperability Task at the French
Institute of Bioinformatics (with Alban Gaignard - Institut du Thorax, Nantes, Platform BiRD, and Frederic
de Lamotte - INRAE, Department of Biology et Plant Improvement, Montpellier). In this frame, she is
member of the ELIXIR European Interoperability platform and she co-authored a paper on automatic
FAIR assessment of web resource data [9].
Hamed Khakzad is an active member of the Rosetta Commons scientific community.

9.1.4 Scientific expertise

Sabeur Aridhi reviewed an ANR project in 2023.

Project CAPSID

23

9.1.5 Research administration

Malika Smaïl-Tabbone is member of the Scientific Council of the Université de Lorraine. As such, she
carries out various scientific expertises within the broad framework of the University of Lorraine.

9.2 Teaching - Supervision - Juries

9.2.1 Teaching

• Malika Smaïl-Tabbone is an associate professor at the Université de Lorraine with a full service. She
is co-responsible with Pascal Moyal of the IMSD track ("Ingénierie Mathématique pour la Science
des Données") in the Applied Mathematics Master’s degree at the Université de Lorraine. She is
also a member of the pedagogic team of the CMI BSE ("Cursus Master Ingénieur Biologie-Santé-
Environnement").

• Sabeur Aridhi is an assistant professor at the Université de Lorraine with a full service. He is re-
sponsible for the major in IAMD ("Ingénierie et Applications des Masses de Données") at TELECOM
Nancy.

• Marie-Dominique Devignes teaches every year 10 to 16h in the CMI BSE.

• Diego Amaya Ramirez held an ATER position from January to August 2023.

• Yasaman Karami gave 27 hours on Machine learning with Python as part of the Data Scientist
continuing education course at the Institute for Digital Management and Cognition (IDMC), Nancy.

• Hamed Khakzad gave 16 hours on deep learning methods in the frame of the Advanced AI course

(3A) at TELECOM Nancy.

9.2.2 Supervision

In 2023, there have been in total 7 PhD students supervised by CAPSID members. Three of them
successfully defended their thesis. Three of them have been recruited and started their PhD in October
2023. Hamed Khakzad is also co-supervising with Rebekka Wild a PhD thesis that started in October 2023
at the University of Grenoble.

9.2.3 Juries

Malika Smaïl-Tabbone was reviewer of two PhD theses.
Marie-Dominique Devignes was reviewer of one PhD thesis and examiner of two PhD theses.
(Not counting the participation of team members in the juries of CAPSID PhD students)

9.2.4 Internal or external Inria responsibilities

Yasaman Karami is a member of the Inria Comité de Développement Technologique (CDT). The main
task of the committee is to evaluate the application and recruitement of research engineers.

10 Scientific production

10.1 Major publications

[1] S. Z. Alborzi, A. Ahmed Nacer, H. Najjar, D. W. Ritchie and M. D. Devignes. ‘PPIDomainMiner:
Inferring domain-domain interactions from multiple sources of proteinprotein interactions’. In:
PLoS Computational Biology 17.8 (2021), e1008844. DOI: 10.1371/journal.pcbi.1008844. URL:
https://hal.archives-ouvertes.fr/hal-03435140.

24

Inria Annual Report 2023

[2] E. Bresso, J.-P. Ferreira, N. Girerd, M. Kobayashi, G. Preud’homme, P. Rossignol, F. Zannad, M.-D.
Devignes and M. Smaïl-Tabbone. ‘Inductive database to support iterative data mining: Application
to biomarker analysis on patient data in the Fight-HF project’. In: Journal of Biomedical Informatics
135 (Nov. 2022), p. 104212. DOI: 10.1016/j.jbi.2022.104212. URL: https://hal.univ-lorra
ine.fr/hal-03805671.

[3] M. K. Islam, D. Amaya-Ramirez, B. Maigret, M.-D. Devignes, S. Aridhi and M. Smaïl-Tabbone.
‘Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge
graph embedding’. In: Scientific Reports 13.1 (Mar. 2023), p. 3643. DOI: 10.1038/s41598-023-30
095-z. URL: https://inria.hal.science/hal-04017432.

[4] A. Moniot, Y. Guermeur, S. J. de Vries and I. Chauvot de Beauchêne. ‘ProtNAff: Protein-bound
Nucleic Acid filters and fragment libraries’. In: Bioinformatics 38.162022-07-01 (2022), pp. 3911–
3917. DOI: 10.1093/bioinformatics/btac430. URL: https://hal.science/hal-03765772.

[5] D. W. Ritchie and S. Grudinin. ‘Spherical polar Fourier assembly of protein complexes with arbitrary
point group symmetry’. In: Journal of Applied Crystallography 49.1 (Feb. 2016), pp. 158–167. DOI:
10.1107/S1600576715022931. URL: https://hal.inria.fr/hal-01261402.

[6] M. E. Ruiz Echartea, I. Chauvot de Beauchêne and D. Ritchie. ‘EROS-DOCK: Protein-Protein
Docking Using Exhaustive Branch-and-Bound Rotational Search’. In: Bioinformatics 35.23 (2019),
pp. 5003–5010. DOI: 10.1093/bioinformatics/btz434. URL: https://hal.archives-ouvert
es.fr/hal-02269812.

10.2 Publications of the year

International journals

[7] M.-D. Devignes, M. Smaïl-Tabbone, H. Dhondge, R. Dolcemascolo, J. Gavaldá-García, R. A. Higuera-
Rodriguez, A. Kravchenko, J. Roca Martínez, N. Messini, A. Pérez-Ràfols, G. Pérez Ropero, L.
Sperotto, I. Chauvot de Beauchêne and W. Vranken. ‘Experiences with a training DSW know-
ledge model for early-stage researchers’. In: Open Research Europe 3 (2023), p. 97. DOI: 10.12688
/openreseurope.15609.1. URL: https://hal.science/hal-04234402.

[8] H. Dhondge, I. Chauvot de Beauchêne and M.-D. Devignes. ‘CroMaSt: a workflow for assessing
protein domain classification by cross-mapping of structural instances between domain databases
and structural alignment’. In: Bioinformatics Advances 3.1 (1st Jan. 2023). DOI: 10.1093/bioadv
/vbad081. URL: https://inria.hal.science/hal-04210856.

[9] A. Gaignard, T. Rosnet, F. de Lamotte, V. Lefort and M.-D. Devignes. ‘FAIR-Checker: supporting
digital resource findability and reuse with Knowledge Graphs and Semantic Web standards’. In:
Journal of Biomedical Semantics 14.1 (July 2023), p. 7. DOI: 10.1186/s13326-023-00289-5. URL:
https://hal.science/hal-04148181.

[10] C. Gueto-Tettay, D. Tang, L. Happonen, M. Heusel, H. Khakzad, J. Malmström and L. Malmström.
‘Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry
proteomics’. In: PLoS Computational Biology 19.1 (20th Jan. 2023), e1010457. DOI: 10.1371/jour
nal.pcbi.1010457. URL: https://hal.science/hal-04403345.

[11] M. K. Islam, D. Amaya-Ramirez, B. Maigret, M.-D. Devignes, S. Aridhi and M. Smaïl-Tabbone.
‘Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge
graph embedding’. In: Scientific Reports 13.1 (Mar. 2023), p. 3643. DOI: 10.1038/s41598-023-30
095-z. URL: https://inria.hal.science/hal-04017432.

[12] N. E. I. Karabadji, A. Amara Korba, A. Assi, H. Seridi, S. Aridhi and W. Dhifli. ‘Accuracy and diversity-
aware multi-objective approach for random forest construction’. In: Expert Systems with Applica-
tions 225.1 (Apr. 2023), p. 120138. DOI: 10.1016/j.eswa.2023.120138. URL: https://inria.ha
l.science/hal-04079595.

[13] H. Khakzad, I. Igashov, A. Schneuing, C. Goverde, M. Bronstein and B. Correia. ‘A new age in
protein design empowered by deep learning’. In: Cell Systems 14.11 (Nov. 2023), pp. 925–939. DOI:
10.1016/j.cels.2023.10.006. URL: https://hal.science/hal-04288627.

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International peer-reviewed conferences

[14]

J. Niehren, C. Lhoussaine and A. Vaginay. ‘Core SBML and its Formal Semantics’. In: CMSB 2023 -
21th International Conference on Formal Methods in Systems Biology. Luxembourg, Luxembourg,
13th Sept. 2023. URL: https://inria.hal.science/hal-04125922.

Doctoral dissertations and habilitation theses

[15] H. Dhondge. ‘Structural characterization of RNA binding to RNA recognition motif (RRM) domains
using data integration, 3D modeling and molecular dynamic simulation’. Université de Lorraine,
11th July 2023. URL: https://theses.hal.science/tel-04219324.

[16] A. Vaginay. ‘Synthesis of Boolean networks from the structure and dynamics of reaction networks’.

Université de Lorraine, 7th July 2023. URL: https://hal.univ-lorraine.fr/tel-04257373.

Other scientific publications

[17] D. Amaya-Ramirez, R. Lhotte, C. Usureau, M. Devriese, M. Smaïl-Tabbone, J.-L. Taupin and D. Marie-
Dominique. ‘HLA-EpiCheck : A B-cell epitope prediction tool on HLA antigens using molecular
dynamics simulation data’. In: ISMB-ECCB 2023 - Intelligent System in Molecular Biology and
European Conference on Computational Biology merged event. Lyon, France, 23rd July 2023. URL:
https://inria.hal.science/hal-04405086.

[18] A. Kravchenko, S. Jacob de Vries, M. Smaïl-Tabbone and I. Chauvot de Beauchene. ‘HIPPO: HIstogram-

based Pseudo-POtential for scoring ssRNA-protein fragment-based docking poses’. In: The 31st
Annual Intelligent Systems For Molecular Biology (ISMB) and the 22nd Annual European Confer-
ence on Computational Biology (ECCB). Lyon, France, 23rd July 2023. URL: https://hal.scienc
e/hal-04168414.

10.3 Cited publications

[19] C. Alfeghaly, I. Behm-Ansmant and S. Maenner. ‘Study of Genome-Wide Occupancy of Long Non-
Coding RNAs Using Chromatin Isolation by RNA Purification (ChIRP)’. In: Methods Mol Biol 2300
(2021), pp. 107–117.

[20] C. E. Alvarez-Martinez and P. J. Christie. ‘Biological diversity of prokaryotic type IV secretion

systems’. In: Microbiology and Molecular Biology Reviews 73 (2011), pp. 775–808.

[21] A. Andreeva, D. Howorth, C. Chothia, E. Kulesha and A. G. Murzin. ‘SCOP2 prototype: a new
approach to protein structure mining’. In: Nucleic Acids Res 42.Database issue (Jan. 2014), pp. D310–
314. DOI: 10.1093/nar/gkt1242.

[22] A. Andreeva, E. Kulesha, J. Gough and A. G. Murzin. ‘The SCOP database in 2020: expanded
classification of representative family and superfamily domains of known protein structures’. In:
Nucleic Acids Res 48.D1 (Jan. 2020), pp. D376–D382. DOI: 10.1093/nar/gkz1064.

[23] M. Baaden and S. R. Marrink. ‘Coarse-grained modelling of protein-protein interactions’. In:

Current Opinion in Structural Biology 23 (2013), pp. 878–886.

[24] M. Baek, F. DiMaio, I. Anishchenko, J. Dauparas, S. Ovchinnikov, G. R. Lee, J. Wang, Q. Cong,
L. N. Kinch, R. D. Schaeffer, C. Millán, H. Park, C. Adams, C. R. Glassman, A. DeGiovanni, J. H.
Pereira, A. V. Rodrigues, A. A. van Dijk, A. C. Ebrecht, D. J. Opperman, T. Sagmeister, C. Buhlheller,
T. Pavkov-Keller, M. K. Rathinaswamy, U. Dalwadi, C. K. Yip, J. E. Burke, K. C. Garcia, N. V. Grishin,
P. D. Adams, R. J. Read and D. Baker. ‘Accurate prediction of protein structures and interactions
using a three-track neural network’. In: Science 373.6557 (Aug. 2021), pp. 871–876. DOI: 10.1126/s
cience.abj8754.

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[25] W. Bahnan, L. Happonen, H. Khakzad, V. Kumra Ahnlide, T. de Neergaard, S. Wrighton, O. André, E.
Bratanis, D. Tang, T. Hellmark, L. Björck, O. Shannon, L. Malmström, J. Malmström and P. Nordenfelt.
‘A human monoclonal antibody bivalently binding two different epitopes in streptococcal M protein
mediates immune function’. In: EMBO Molecular Medicine 15.2 (2022), e16208. DOI: https://doi
.org/10.15252/emmm.202216208. eprint: https://www.embopress.org/doi/pdf/10.1525
2/emmm.202216208. URL: https://www.embopress.org/doi/abs/10.15252/emmm.2022162
08.

[26] M. Blum, H. Y. Chang, S. Chuguransky, T. Grego, S. Kandasaamy, A. Mitchell, G. Nuka, T. Paysan-
Lafosse, M. Qureshi, S. Raj, L. Richardson, G. A. Salazar, L. Williams, P. Bork, A. Bridge, J. Gough, D. H.
Haft, I. Letunic, A. Marchler-Bauer, H. Mi, D. A. Natale, M. Necci, C. A. Orengo, A. P. Pandurangan,
C. Rivoire, C. J. A. Sigrist, I. Sillitoe, N. Thanki, P. D. Thomas, S. C. E. Tosatto, C. H. Wu, A. Bateman
and R. D. Finn. ‘The InterPro protein families and domains database: 20 years on’. In: Nucleic Acids
Res 49.D1 (Jan. 2021), pp. D344–D354. DOI: 10.1093/nar/gkaa977.

[27] A. Bonneau and C. Monchaud. ‘La transplantation d’organes en France’. In: Actualités Pharma-
ceutiques 60.605 (2021), pp. 18–20. DOI: https://doi.org/10.1016/j.actpha.2021.02.005.
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[28] D. F. Burke, P. Bryant, I. Barrio-Hernandez, D. Memon, G. Pozzati, A. Shenoy, W. Zhu, A. S. Dunham,
P. Albanese, A. Keller, R. A. Scheltema, J. E. Bruce, A. Leitner, P. Kundrotas, P. Beltrao and A. Elofsson.
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I. Chauvot de Beauchêne, S. J. De Vries and M. Zacharias. ‘Fragment-based modelling of single
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[34] S. E. Dobbins, V. I. Lesk and M. J. E. Sternberg. ‘Insights into protein flexibility: The relationship
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[35] R. Evans, M. O’Neill, A. Pritzel, N. Antropova, A. Senior, T. Green, A. Žídek, R. Bates, S. Black-
well, J. Yim, O. Ronneberger, S. Bodenstein, M. Zielinski, A. Bridgland, A. Potapenko, A. Cowie,
K. Tunyasuvunakool, R. Jain, E. Clancy, P. Kohli, J. Jumper and D. Hassabis. ‘Protein complex pre-
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[36] W. J. Frawley, G. Piatetsky-Shapiro and C. J. Matheus. ‘Knowledge Discovery in Databases: An

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[37] R. Fronzes, E. Schäfer, L. Wang, H. R. Saibil, E. V. Orlova and G. Waksman. ‘Structure of a type IV

secretion system core complex’. In: Science 323 (2011), pp. 266–268.

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[38] A. Ghoorah, M.-D. Devignes, M. Smaïl-Tabbone and D. Ritchie. ‘KBDOCK 2013: A spatial classi-
fication of 3D protein domain family interactions’. In: Nucleic Acids Research 42.D1 (Jan. 2014),
pp. 389–395. DOI: 10.1093/nar/gkt1199. URL: https://hal.inria.fr/hal-00920612.

[39] A. Ghoorah, M.-D. Devignes, M. Smaïl-Tabbone and D. Ritchie. ‘Protein Docking Using Case-Based
Reasoning’. In: Proteins - Structure, Function and Bioinformatics 81.12 (Oct. 2013), pp. 2150–2158.
DOI: 10.1002/prot.24433. URL: https://hal.inria.fr/hal-00880341.

[40] A. Ghoorah, M.-D. Devignes, M. Smaïl-Tabbone and D. Ritchie. ‘Spatial clustering of protein binding
sites for template based protein docking’. In: Bioinformatics 27.20 (Aug. 2011), pp. 2820–2827. DOI:
10.1093/bioinformatics/btr493. URL: https://hal.inria.fr/inria-00617921.

[41] D. S. Himmelstein, A. Lizee, C. Hessler, L. Brueggeman, S. L. Chen, D. Hadley, A. Green, P. Khankh-
anian and S. E. Baranzini. ‘Systematic integration of biomedical knowledge prioritizes drugs for
repurposing’. In: Elife 6 (Sept. 2017).

[42] H. I. Ingólfsson, C. A. Lopez, J. J. Uusitalo, D. H. de Jong, S. M. Gopal, X. Periole and S. R. Marrink.
‘The power of coarse graining in biomolecular simulations’. In: WIRES Comput. Mol. Sci. 4 (2013),
pp. 225–248. URL: http://dx.doi.org/10.1002/wcms.1169.

[43] K. Islam. ‘Explainable link prediction in large complex graphs - application to drug repurposing’.
Theses. Université de Lorraine, Dec. 2022. URL: https://hal.univ-lorraine.fr/tel-040273
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[44] K. Islam, S. Aridhi and M. Smaïl-Tabbone. ‘Negative Sampling and Rule Mining for Explainable
Link Prediction in Knowledge Graphs’. In: Knowledge-Based Systems 250 (Aug. 2022), p. 109083.
DOI: 10.1016/j.knosys.2022.109083. URL: https://hal.science/hal-03684205.

[45] A. Izadi, Y. Karami, E. Bratanis, S. Wrighton, H. Khakzad, M. Nyblom, B. Olofsson, L. Happonen,
D. Tang, M. Nilges, J. Malmström, W. Bahnan, O. Shannon, L. Malmström and P. Nordenfelt. ‘The
increased hinge flexibility of an IgG1-IgG3 hybrid monoclonal enhances Fc-mediated protection
against group A streptococci’. In: bioRxiv (2023). DOI: 10.1101/2023.10.14.562368. eprint:
https://www.biorxiv.org/content/early/2023/10/18/2023.10.14.562368.full.pdf.
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[46]

J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates,
A. Žídek, A. Potapenko, A. Bridgland, C. Meyer, S. A. A. Kohl, A. J. Ballard, A. Cowie, B. Romera-
Paredes, S. Nikolov, R. Jain, J. Adler, T. Back, S. Petersen, D. Reiman, E. Clancy, M. Zielinski, M.
Steinegger, M. Pacholska, T. Berghammer, S. Bodenstein, D. Silver, O. Vinyals, A. W. Senior, K. Kavuk-
cuoglu, P. Kohli and D. Hassabis. ‘Highly accurate protein structure prediction with AlphaFold’. In:
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[47] Y. Karami, F. Guyon, S. De Vries and P. ry. ‘DaReUS-Loop: accurate loop modeling using fragments

from remote or unrelated proteins’. In: Sci Rep 8.1 (Sept. 2018), p. 13673.

[48] Y. Karami, E. Laine and A. Carbone. ‘Dissecting protein architecture with communication blocks
and communicating segment pairs’. In: BMC Bioinformatics 17 Suppl 2.Suppl 2 (Jan. 2016), p. 13.

[49] Y. Karami, S. Murail, J. Giribaldi, B. Lefranc, F. Defontaine, O. Lesouhaitier, J. Leprince, S. de Vries
and P. Tuffery. ‘Exploring a Structural Data Mining Approach to Design Linkers for Head-to-Tail
Peptide Cyclization’. In: J Chem Inf Model 63.20 (Oct. 2023), pp. 6436–6450.

[50] H. Laroussi, Y. Aoudache, E. Robert, V. Libante, L. Thiriet, D. Mias-Lucquin, B. Douzi, Y. Roussel,
I. ne, N. Soler and N. Leblond-Bourget. ‘Exploration of DNA processing features unravels novel
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[51]

J. Liu, Z. Guo, T. Wu, R. S. Roy, F. Quadir, C. Chen and J. Cheng. ‘Enhancing alphafold-multimer-
based protein complex structure prediction with MULTICOM in CASP15’. In: Commun Biol 6.1
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[52] F. MacLean. ‘Knowledge graphs and their applications in drug discovery’. In: Expert Opin Drug

Discov 16.9 (Sept. 2021), pp. 1057–1069.

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[53] C. Maris, C. Dominguez and F. H. Allain. ‘The RNA recognition motif, a plastic RNA-binding
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[54] A. May and M. Zacharias. ‘Energy minimization in low-frequency normal modes to efficiently allow
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[55] D. Mias-Lucquin and I. Chauvot de Beauchene. ‘Conformational variability in proteins bound
to single-stranded DNA: A new benchmark for new docking perspectives’. In: Proteins: Structure,
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[56]

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[58] C. Morris. ‘Towards a structural biology work bench’. In: Acta Crystallographica PD69 (2013),

pp. 681–682.

[59] D. Mustard and D. Ritchie. ‘Docking essential dynamics eigenstructures’. In: Proteins: Structure,
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[60] D. N. Nicholson and C. S. Greene. ‘Constructing knowledge graphs and their biomedical applica-
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[68] N. Soler, E. Robert, I. Chauvot de Beauchêne, P. Monteiro, V. Libante, B. Maigret, J. Staub, D. W.
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[69] A. Vaginay, T. Boukhobza and M. Smaïl-Tabbone. ‘From quantitative SBML models to Boolean
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<scratchpad>
- Main topic: Research Centre CAPSID at Inria Centre at Université de Lorraine
- Key points: 
  - CAPSID is a research centre focused on computational algorithms for protein structures and interactions
  - The centre is involved in various projects, including biomedical knowledge discovery, protein-nucleic acid interactions, and structural biology
  - CAPSID aims to develop algorithms and software to study biological systems and phenomena from a structural point of view
  - The centre is collaborating with various partners, including universities, research institutions, and hospitals
- Structure: 
  - The document is organized into sections, including Research Program, Application Domains, and New Software and Platforms
  - The centre is divided into various teams, including the CAPSID team, the LORIA team, and the DynAMic team
- Potential artifacts to ignore: 
  - Web-related elements, such as links, HTML tags, and other web-related elements
  - Unnecessary information, such as contact information and funding details
</scratchpad>

<final_summary>
The CAPSID research centre at Inria Centre at Université de Lorraine is focused on developing computational algorithms for protein structures and interactions. The centre is involved in various projects, including biomedical knowledge discovery, protein-nucleic acid interactions, and structural biology. CAPSID aims to develop algorithms and software to study biological systems and phenomena from a structural point of view, with a focus on understanding molecular interactions and predicting functional sites. The centre is collaborating with various partners, including universities, research institutions, and hospitals, to advance the field of structural biology and develop new treatments for diseases.
The CAPSID research centre at Inria Centre at Université de Lorraine is focused on developing computational algorithms for protein structures and interactions. The centre is involved in various projects, including biomedical knowledge discovery, protein-nucleic acid interactions, and structural biology. CAPSID aims to develop algorithms and software to study biological systems and phenomena from a structural point of view, with a focus on understanding molecular interactions and predicting functional sites. The centre is collaborating with various partners, including universities, research institutions, and hospitals, to advance the field of structural biology and develop new treatments for diseases.