RESEARCH CENTRE

Inria Centre
at Université Côte d’Azur

2023
ACTIVITY REPORT

Project-Team

ABS

Algorithms - Biology - Structure

DOMAIN

Digital Health, Biology and Earth

THEME

Computational Biology

Contents

Project-Team ABS

1 Team members, visitors, external collaborators

2 Overall objectives

3 Research program

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1 Modeling the dynamics of proteins
3.2 Algorithmic foundations: geometry, optimization, machine learning . . . . . . . . . . . . .
3.3 Software: the Structural Bioinformatics Library . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Applications: modeling interfaces, contacts, and interactions . . . . . . . . . . . . . . . . . .

4 Application domains

5 Social and environmental responsibility

5.1 Footprint of research activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Impact of research results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 Highlights of the year

7 New software, platforms, open data

7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.1 SBL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8 New results

8.1 Modeling the dynamics of proteins

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.1.1 Enhanced conformational exploration of protein loops using a global parameteriza-
tion of the backbone geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2 Algorithmic foundations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2.1 Geometric constraints within tripeptides and the existence of tripeptide reconstruc-
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.3 Applications in structural bioinformatics and beyond . . . . . . . . . . . . . . . . . . . . . .
8.3.1 Discriminating physiological from non-physiological interfaces in structures of pro-
tein complexes: A community-wide study . . . . . . . . . . . . . . . . . . . . . . . . .

tions

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9 Partnerships and cooperations

9.1 International research visitors

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9.1.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
9.2 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

10 Dissemination

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10.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
10.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
10.1.2 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
10.1.3 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 12
10.1.4 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
10.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
10.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
10.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
10.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
10.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
10.3.1 Internal or external Inria responsibilities . . . . . . . . . . . . . . . . . . . . . . . . . . 13
10.3.2 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

11 Scientific production

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11.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
11.2 Publications of the year
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11.3 Cited publications

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Project-Team ABS

Creation of the Project-Team: 2008 July 01

Keywords

Computer sciences and digital sciences

A2.5. – Software engineering

A3.3.2. – Data mining

A3.4.1. – Supervised learning

A3.4.2. – Unsupervised learning

A6.1.4. – Multiscale modeling

A6.2.4. – Statistical methods

A6.2.8. – Computational geometry and meshes

A8.1. – Discrete mathematics, combinatorics

A8.3. – Geometry, Topology

A8.7. – Graph theory

A9.2. – Machine learning

Other research topics and application domains

B1.1.1. – Structural biology

B1.1.5. – Immunology

B1.1.7. – Bioinformatics

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Frédéric Cazals [Team leader, INRIA, Senior Researcher, HDR]

• Dorian Mazauric [INRIA, Researcher, HDR]

• Edoardo Sarti [INRIA, Researcher]

PhD Students

• Guillaume Carriere [INRIA, from Oct 2023]

• Ercan Seckin [INRAE, from Oct 2023]

Technical Staff

• Come Le Breton [INRIA, Engineer]

Interns and Apprentices

• Tejas Anand [INRIA, Intern, from May 2023 until Jul 2023, IIT Delhi]

• Jules Herrmann [INRIA, Intern, from May 2023 until Aug 2023, Université Paris Cité]

• Parth Patel [INRIA, Intern, from May 2023 until Jul 2023, IIT Delhi]

• Manpreet Singh [INRIA, Intern, from May 2023 until Jul 2023, IIT Delhi]

• Suren Suren [INRIA, Intern, from May 2023 until Jul 2023, IIT Delhi]

Administrative Assistant

• Florence Barbara [INRIA]

External Collaborator

• David Wales [UNIV CAMBRIDGE]

2 Overall objectives

Biomolecules and their function(s). Computational Structural Biology (CSB) is the scientific domain
concerned with the development of algorithms and software to understand and predict the structure
and function of biological macromolecules. This research field is inherently multi-disciplinary. On
the experimental side, biology and medicine provide the objects studied, while biophysics and bioin-
formatics supply experimental data, which are of two main kinds. On the one hand, genome sequen-
cing projects give supply protein sequences, and ~200 millions of sequences have been archived in
UniProtKB/TrEMBL – which collects the protein sequences yielded by genome sequencing projects. On
the other hand, structure determination experiments (notably X-ray crystallography, nuclear magnetic
resonance, and cryo-electron microscopy) give access to geometric models of molecules – atomic co-
ordinates. Alas, only ~150,000 structures have been solved and deposited in the Protein Data Bank (PDB),
a number to be compared against the ∼ 108 sequences found in UniProtKB/TrEMBL. With one structure
for ~1000 sequences, we hardly know anything about biological functions at the atomic/structural level.
Complementing experiments, physical chemistry/chemical physics supply the required models (energies,
thermodynamics, etc). More specifically, let us recall that proteins with n atoms has d = 3n Cartesian
coordinates, and fixing these (up to rigid motions) defines a conformation. As conveyed by the iconic

Project ABS

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Figure 1: The synergy modeling - experiments, and challenges faced in CSB: illustration on the problem
of designing miniproteins blocking the entry of SARS-CoV-2 into cells. From [25]. Of note: the first step
of the infection by SARS-CoV-2 is the attachment of its receptor binding domain of its spike (RBD, blue
molecule), to a target protein found on the membrane of our cells, ACE2 (orange molecule). A strategy
to block infection is therefore to engineer a molecule binding the RBD, preventing its attachment to
ACE2. (A) Design of a helical protein (orange) mimicking a region of the ACE2 protein. (B) Assessment of
binding modes (conformation, binding energies) of candidate miniproteins neutralizing the RBD.

lock-and-key metaphor for interacting molecules, Biology is based on the interactions stable conform-
ations make with each other. Turning these intuitive notions into quantitative ones requires delving
into statistical physics, as macroscopic properties are average properties computed over ensembles of
conformations. Developing effective algorithms to perform accurate simulations is especially challenging
for two main reasons. The first one is the high dimension of conformational spaces – see d = 3n above,
typically several tens of thousands, and the non linearity of the energy functionals used. The second one
is the multiscale nature of the phenomena studied: with biologically relevant time scales beyond the
millisecond, and atomic vibrations periods of the order of femto-seconds, simulating such phenomena
typically requires ≫ 1012 conformations/frames, a (brute) tour de force rarely achieved [34].

Computational Structural Biology: three main challenges. The first challenge, sequence-to-structure
prediction, aims to infer the possible structure(s) of a protein from its amino acid sequence. While recent
progress has been made recently using in particular deep learning techniques [33], the models obtained
so far are static and coarse-grained.

The second one is protein function prediction. Given a protein with known structure, i.e., 3D co-
ordinates, the goal is to predict the partners of this protein, in terms of stability and specificity. This
understanding is fundamental to biology and medicine, as illustrated by the example of the SARS-CoV-2
virus responsible of the Covid19 pandemic. To infect a host, the virus first fuses its envelope with the
membrane of a target cell, and then injects its genetic material into that cell. Fusion is achieved by a
so-called class I fusion protein, also found in other viruses (influenza, SARS-CoV-1, HIV, etc). The fusion
process is a highly dynamic process involving large amplitude conformational changes of the molecules.
It is poorly understood, which hinders our ability to design therapeutics to block it.

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Inria Annual Report 2023

Finally, the third one, large assembly reconstruction, aims at solving (coarse-grain) structures of
molecular machines involving tens or even hundreds of subunits. This research vein was promoted
about 15 years back by the work on the nuclear pore complex [22]. It is often referred to as reconstruction
by data integration, as it necessitates to combine coarse-grain models (notably from cryo-electron
microscopy (cryo-EM) and native mass spectrometry) with atomic models of subunits obtained from
X ray crystallography. Fitting the latter into the former requires exploring the conformation space of
subunits, whence the importance of protein dynamics.

As an illustration of these three challenges, consider the problem of designing proteins blocking the
entry of SARS-CoV-2 into our cells (Fig. 1). The first challenge is illustrated by the problem of predicting
the structure of a blocker protein from its sequence of amino-acids – a tractable problem here since the
mini proteins used only comprise of the order of 50 amino-acids (Fig. 1(A), [25]). The second challenge
is illustrated by the calculation of the binding modes and the binding affinity of the designed proteins
for the RBD of SARS-CoV-2 (Fig. 1(B)). Finally, the last challenge is illustrated by the problem of solving
structures of the virus with a cell, to understand how many spikes are involved in the fusion mechanism
leading to infection. In [25], the promising designs suggested by modeling have been assessed by an
array of wet lab experiments (affinity measurements, circular dichroism for thermal stability assessment,
structure resolution by cryo-EM). The hyperstable minibinders identified provide starting points for
SARS-CoV-2 therapeutics [25]. We note in passing that this is truly remarkable work, yet, the designed
proteins stem from a template (the bottom helix from ACE2), and are rather small.

Figure 2: The main challenges of molecular simulation: Finding significant local minima of the energy
landscape, computing statistical weights of catchment basins by integrating Boltzmann’s factor, and
identifying transitions. Practically, d > 100.

Protein dynamics: core CS - maths challenges. To present challenges in structural modeling, let us
recall the following ingredients (Fig. 2). First, a molecular model with n atoms is parameterized over
a conformational space X of dimension d = 3n in Cartesian coordinates, or d = 3n − 6 in internal
coordinate–upon removing rigid motions, also called degree of freedom (d.o.f.). Second, recall that the
potential energy landscape (PEL) is the mapping V (·) from Rd to R providing a potential energy for each
conformation [35, 32]. Example potential energies (PE) are CHARMM, AMBER, MARTINI, etc. Such PE belong
to the realm of molecular mechanics, and implement atomic or coarse-grain models. They may embark a
solvent model, either explicit or implicit. Their definition requires a significant number of parameters
(up to ∼ 1, 000), fitted to reproduce physico-chemical properties of (bio-)molecules [36].

These PE are usually considered good enough to study non covalent interactions – our focus, even
tough they do not cover the modification of chemical bonds. In any case, we take such a function for

XV:potentialenergyProject ABS

granted 1.

5

The PEL codes all structural, thermodynamic, and kinetic properties, which can be obtained by
averaging properties of conformations over so-called thermodynamic ensembles. The structure of a
macromolecular system requires the characterization of active conformations and important intermedi-
ates in functional pathways involving significant basins. In assigning occupation probabilities to these
conformations by integrating Boltzmann’s distribution, one treats thermodynamics. Finally, transitions
between the states, modeled, say, by a master equation (a continuous-time Markov process), corres-
pond to kinetics. Classical simulation methods based on molecular dynamics (MD) and Monte Carlo
sampling (MC) are developed in the lineage of the seminal work by the 2013 recipients of the Nobel prize
in chemistry (Karplus, Levitt, Warshel), which was awarded “for the development of multiscale models
for complex chemical systems”. However, except for highly specialized cases where massive calculations
have been used [34], neither MD nor MC give access to the aforementioned time scales. In fact, the main
limitation of such methods is that they treat structural, thermodynamic and kinetic aspects at once [28].
The absence of specific insights on these three complementary pieces of the puzzle makes it impossible
to optimize simulation methods, and results in general in the inability to obtain converged simulations
on biologically relevant time-scales.

The hardness of structural modeling owes to three intertwined reasons.
First, PELs of biomolecules usually exhibit a number of critical points exponential in the dimen-
sion [23]; fortunately, they enjoy a multi-scale structure [26]. Intuitively, the significant local min-
ima/basins are those which are deep or isolated/wide, two notions which are mathematically qualified by
the concepts of persistence and prominence. Mathematically, problems are plagued with the curse of
dimensionality and measure concentration phenomena. Second, biomolecular processes are inherently
multi-scale, with motions spanning ∼ 15 and ∼ 4 orders of magnitude in time and amplitude respectively
[21]. Developing methods able to exploit this multi-scale structure has remained elusive. Third, macro-
scopic properties of biomolecules, i.e., observables, are average properties computed over ensembles of
conformations, which calls for a multi-scale statistical treatment both of thermodynamics and kinetics.

Validating models. A natural and critical question naturally concerns the validation of models proposed
in structural bioinformatics. For all three types of questions of interest (structures, thermodynamics,
kinetics), there exist experiments to which the models must be confronted – when the experiments can
be conducted.

For structures, the models proposed can readily be compared against experimental results stemming
from X ray crystallography, NMR, or cryo electron microscopy. For thermodynamics, which we illustrate
here with binding affinities, predictions can be compared against measurements provided by calorimetry
or surface plasmon resonance. Lastly, kinetic predictions can also be assessed by various experiments
such as binding affinity measurements (for the prediction of Kon and Ko f f ), or fluorescence based
methods (for kinetics of folding).

3 Research program

Our research program ambitions to develop a comprehensive set of novel concepts and algorithms to
study protein dynamics, based on the modular framework of PEL.

3.1 Modeling the dynamics of proteins

Keywords: Molecular conformations, conformational exploration, energy landscapes, thermodynamics,
kinetics.

As noticed while discussing Protein dynamics: core CS - maths challenges, the integrated nature of
simulation methods such as MD or MC is such that these methods do not in general give access to
biologically relevant time scales. The framework of energy landscapes [35, 32] (Fig. 2) is much more
modular, yet, large biomolecular systems remain out of reach.

1We note passing that the PE model currently implemented in the SBL is a classical one with particle-particle interactions, see
Potential Energy. But it could be easily extended to accommodate dipole - charge interactions for polarizable force fields (amoeba).

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Inria Annual Report 2023

To make a definitive step towards solving the prediction of protein dynamics, we will serialize
the discovery and the exploitation of a PEL [4, 15, 3]. Ideas and concepts from computational geo-
metry/geometric motion planning, machine learning, probabilistic algorithms, and numerical probab-
ility will be used to develop two classes of probabilistic algorithms. The first deals with algorithms to
discover/sketch PELs, i.e., enumerate all significant (persistent or prominent) local minima and their
connections across saddles, a difficult task since the number of all local minima/critical points is generally
exponential in the dimension. To this end, we will develop a hierarchical data structure coding PELs
as well as multi-scale proposals to explore molecular conformations. (NB: in Monte Carlo methods,
a proposal generates a new conformation from an existing one.) The second focuses on methods to
exploit/sample PELs, i.e., compute so-called densities of states, from which all thermodynamic quantities
are given by standard relations [24][31]. This is a hard problem akin to high-dimensional numerical
integration. To solve this problem, we will develop a learning based strategy for the Wang-Landau al-
gorithm [30]–an adaptive Monte Carlo Markov Chain (MCMC) algorithm, as well as a generalization of
multi-phase Monte Carlo methods for convex/polytope volume calculations [29, 27], for non convex
strata of PELs.

3.2 Algorithmic foundations: geometry, optimization, machine learning

Keywords: Geometry, optimization, machine learning, randomized algorithms, sampling, optimization.

As discussed in the previous Section, the study of PEL and protein dynamics raises difficult algorithmic
/ mathematical questions. As an illustration, one may consider our recent work on the comparison of high
dimensional distribution [6], statistical tests / two-sample tests [7, 12], the comparison of clustering [8],
the complexity study of graph inference problems for low-resolution reconstruction of assemblies [11],
the analysis of partition (or clustering) stability in large networks, the complexity of the representation of
simplicial complexes [2]. Making progress on such questions is fundamental to advance the state-of-the
art on protein dynamics.

We will continue to work on such questions, motivated by CSB / theoretical biophysics, both in the
continuous (geometric) and discrete settings. The developments will be based on a combination of ideas
and concepts from computational geometry, machine learning (notably on non linear dimensionality
reduction, the reconstruction of cell complexes, and sampling methods), graph algorithms, probabilistic
algorithms, optimization, numerical probability, and also biophysics.

3.3 Software: the Structural Bioinformatics Library

Keywords: Scientific software, generic programming, molecular modeling.

While our main ambition is to advance the algorithmic foundations of molecular simulation, a major
challenge will be to ensure that the theoretical and algorithmic developments will change the fate of
applications, as illustrated by our case studies. To foster such a symbiotic relationship between theory,
algorithms and simulation, we will pursue high quality software development and integration within the
SBL, and will also take the appropriate measures for the software to be widely adopted.

Software in structural bioinformatics. Software development for structural bioinformatics is especially
challenging, combining advanced geometric, numerical and combinatorial algorithms, with complex
biophysical models for PEL and related thermodynamic/kinetic properties. Specific features of the
proteins studied must also be accommodated. About 50 years after the development of force fields and
simulation methods (see the 2013 Nobel prize in chemistry), the software implementing such methods
has a profound impact on molecular science at large. One can indeed cite packages such as CHARMM,
AMBER, gromacs, gmin, MODELLER, Rosetta, VMD, PyMol, . . . . On the other hand, these packages are
goal oriented, each tackling a (small set of) specific goal(s). In fact, no real modular software design
and integration has taken place. As a result, despite the high quality software packages available, inter-
operability between algorithmic building blocks has remained very limited.

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The SBL. Predicting the dynamics of large molecular systems requires the integration of advanced al-
gorithmic building blocks / complex software components. To achieve a sufficient level of integration, we
undertook the development of the Structural Bioinformatics Library (SBL, SB) [5], a generic C++/python
cross-platform library providing software to solve complex problems in structural bioinformatics. For
end-users, the SBL provides ready to use, state-of-the-art applications to model macro-molecules and
their complexes at various resolutions, and also to store results in perennial and easy to use data formats
(SBL Applications). For developers, the SBL provides a broad C++/python toolbox with modular design
(SBL Doc). This hybrid status targeting both end-users and developers stems from an advanced software
design involving four software components, namely applications, core algorithms, biophysical models,
and modules (SBL Modules). This modular design makes it possible to optimize robustness and the
performance of individual components, which can then be assembled within a goal oriented application.

3.4 Applications: modeling interfaces, contacts, and interactions

Keywords: Protein interactions, protein complexes, structure/thermodynamics/kinetics prediction.

Our methods will be validated on various systems for which flexibility operates at various scales.
Examples of such systems are antibody-antigen complexes, (viral) polymerases, (membrane) transporters.
Even very complex biomolecular systems are deterministic in prescribed conditions (temperature,
pH, etc), demonstrating that despite their high dimensionality, all d.o.f. are not at play at the same time.
This insight suggests three classes of systems of particular interest. The first class consists of systems
defined from (essentially) rigid blocks whose relative positions change thanks to conformational changes
of linkers; a Newton cradle provides an interesting way to envision such as system. We have recently
worked on one such system, a membrane proteins involve in antibiotic resistance (AcrB, see [16]). The
second class consists of cases where relative positions of subdomains do not significantly change, yet,
their intrinsic dynamics are significantly altered. A classical illustration is provided by antibodies, whose
binding affinity owes to dynamics localized in six specific loops [13, 14]. The third class, consisting
of composite cases, will greatly benefit from insights on the first two classes. As an example, we may
consider the spikes of the SARS-CoV-2 virus, whose function (performing infection) involves both large
amplitude conformational changes and subtle dynamics of the so-called receptor binding domain. We
have started to investigate this system, in collaboration with B. Delmas (INRAE).

In ABS, we will investigate systems in these three tiers, in collaboration with expert collaborators, to
hopefully open new perspectives in biology and medicine. Along the way, we will also collaborate on
selected questions at the interface between CSB and systems biology, as it is now clear that the structural
level and the systems level (pathways of interacting molecules) can benefit from one another.

4 Application domains

The main application domain is Computational Structural Biology, as underlined in the Research Program.

5 Social and environmental responsibility

5.1 Footprint of research activities

A tenet of ABS is to carefully analyze the performances of the algorithms designed–either formally or
experimentally, so as to avoid massive calculations. Therefore, the footprint of our research activities has
remained limited.

5.2 Impact of research results

The scientific agenda of ABS is geared towards a fine understanding of complex phenomena at the
atomic/molecular level. While the current focus is rather fundamental, as explained in Research program,
an overarching goal for the current period (i.e. 12 years) is to make significant contributions to important
problems in biology and medicine.

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Inria Annual Report 2023

6 Highlights of the year

The main scientific achievement of the year has been the finalization of sampling techniques to explore
large amplitude conformation changes of flexible loops, see [17, 18], based on Monte Carlo Markov chain
techniques we introduced for the calculation of the volume of polytopes [10].

7 New software, platforms, open data

7.1 New software

7.1.1 SBL

Name: Structural Bioinformatics Library

Keywords: Structural Biology, Biophysics, Software architecture

Functional Description: The SBL is a generic C++/python cross-platform software library targeting
complex problems in structural bioinformatics. Its tenet is based on a modular design offering
a rich and versatile framework allowing the development of novel applications requiring well
specified complex operations, without compromising robustness and performances.

More specifically, the SBL involves four software components (1-4 thereafter). For end-users, the
SBL provides ready to use, state-of-the-art (1) applications to handle molecular models defined by
unions of balls, to deal with molecular flexibility, to model macro-molecular assemblies. These
applications can also be combined to tackle integrated analysis problems. For developers, the SBL
provides a broad C++ toolbox with modular design, involving core (2) algorithms, (3) biophysical
models, and (4) modules, the latter being especially suited to develop novel applications. The SBL
comes with a thorough documentation consisting of user and reference manuals, and a bugzilla
platform to handle community feedback.

Release Contributions: The achievements in 2023 are twofold. First, a structure file reader handling the
PDB and mmCIF formats was integrated, based on the libcifpp library (voir https://github.com/PDB-
REDO/libcifpp). Second, the development of three packages of broad interest for users was fi-
nalized: Loopsampler (sampler for flexible loops, Kpax (structural alignments), and Spectrus
(decomposition of proteins into quasi-rigid domains). These packages will be integrated to the
public release early 2024.

URL: https://sbl.inria.fr/

Publication: hal-01570848

Contact: Frédéric Cazals

8 New results

Participants:

F. Cazals, D. Mazauric, E. Sarti.

8.1 Modeling the dynamics of proteins

Keywords: Protein flexibility, protein conformations, collective coordinates, conformational sampling,
loop closure, kinematics, dimensionality reduction.

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8.1.1 Enhanced conformational exploration of protein loops using a global parameterization of the

backbone geometry

Participants:

F. Cazals, T. O’Donnell.

Flexible loops are paramount to protein functions, with action modes ranging from localized dynamics
contributing to the free energy of the system, to large amplitude conformational changes accounting for
the repositioning whole secondary structure elements or protein domains. However, generating diverse
and low energy loops remains a difficult problem.

This work [18] introduces a novel paradigm to sample loop conformations, in the spirit of the Hit-and-
Run (HAR) Markov chain Monte Carlo technique. The algorithm uses a decomposition of the loop into
tripeptides, and a novel characterization of necessary conditions for Tripeptide Loop Closure to admit
solutions. Denoting m the number of tripeptides, the algorithm works in an angular space of dimension
12m. In this space, the hyper-surfaces associated with the aforementioned necessary conditions are used
to run a HAR-like sampling technique. On classical loop cases up to 15 amino acids, our parameter free
method compares favorably to previous work, generating more diverse conformational ensembles. We
also report experiments on a 30 amino acids long loop, a size not processed in any previous work.

8.2 Algorithmic foundations

Keywords: Computational geometry, computational topology, optimization, graph theory, data analysis,
statistical physics.

8.2.1 Geometric constraints within tripeptides and the existence of tripeptide reconstructions

Participants:

F. Cazals, T. O’Donnell, V. Agashe, IIT Delhi, India.

Designing movesets providing high quality protein conformations remains a hard problem, especially
when it comes to deform a long protein backbone segment, and a key building block to do so is the
so-called tripeptide loop closure (TLC) [17]. Consider a tripeptide whose first and last bonds (N1Cα;1
and Cα;3C3) are fixed, and so are all internal coordinates except the six {(φ, ψ)}i =1,2,3 dihedral angles
associated to the three Cα carbons. Under these conditions, the TLC algorithm provides all possible
values for these six dihedral angles–there exists at most 16 solutions. TLC moves atoms up to ∼ 5Å in one
step and retains low energy conformations, whence its pivotal role to design move sets sampling protein
loop conformations.

In this work [17], we relax the previous constraints, allowing the last bond (Cα;3C3) to freely move in
3D space–or equivalently in a 5D configuration space. We exhibit necessary geometric constraints in this
5D space for TLC to admit solutions. Our analysis provides key insights on the geometry of solutions
for TLC. Most importantly, when using TLC to sample loop conformations based on m consecutive
tripeptides along a protein backbone, we obtain an exponential gain in the volume of the 5m-dimensional
configuration space to be explored.

8.3 Applications in structural bioinformatics and beyond

Keywords: Docking, scoring, interfaces, protein complexes, phylogeny, evolution.

8.3.1 Discriminating physiological from non-physiological interfaces in structures of protein com-

plexes: A community-wide study

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Inria Annual Report 2023

Participant:

F. Cazals.

Community contribution in the scope of the Elixir / 3D Bioinfo project, see the paper and benchmark.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric
state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort
was launched to tackle these challenges [19]. The latest resources on protein complexes and interfaces
were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures,
including a balanced mix of physiological and non-physiological complexes. The non-physiological com-
plexes in the benchmark were selected to bury a similar or larger interface area than their physiological
counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252
functions for scoring protein-protein interfaces previously developed by 13 groups were collected and
evaluated for their ability to discriminate between physiological and non-physiological complexes. A
simple consensus score generated using the best performing score of each of the 13 groups, and a cross-
validated Random Forest (RF) classifier were created. Both approaches showed excellent performance,
with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, out-
performing individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the
physiological dimers with significantly higher accuracy than the non-physiological set, lending support to
the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring
functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

9 Partnerships and cooperations

Participants:

Frédéric Cazals, Edoardo Sarti.

9.1 International research visitors

9.1.1 Visits of international scientists

Inria International Chair

• David Wales, Cambridge University, is endowed chair within 3IA Côte d’Azur / ABS.

9.2 National initiatives

Action Exploratoire Inria. The AEx DEFINE, involving Inria ABS and Laboratory of Computational and
Quantitative Biology (LCQB) from Sorbonne University started in Septembre 2023, for a period of four
years.

ABS develops novel methods to study protein structure and dynamics, using computational geom-
etry/topology and machine learning. LCQB is a leading lab addressing core questions at the heart of mod-
ern biology, with a unique synergy between quantitative models and experiments. The goal of DEFINE is
to provide a synergy between ABS and LCQB, with a focus on the prediction of protein functions, at the
genome scale and for two specific applications (photosynthesis, DNA repair).

Co-supervised PhD thesis Inria-INRAE. The PhD thesis of Ercan Seckin started in october 2023 is
co-supervised by Etienne Danchin (supervisor) and Dominique Colinet at the INRAE GAME team and
Edoardo Sarti at ABS.

Project ABS

11

The thesis title is: Détection, histoire évolutive et relations structure - fonction des gènes orphelins
chez les bioagresseurs des plantes. The two teams are closely collaborating for advancing current know-
ledge on the emergence of orphan genes/proteins in the Meloidogyne genus as well as their structural and
functional characterization. Notably, the ABS team will focus on the structural and functional inference,
and the interplay between structure and function in the process of gene formation.

10 Dissemination

Participants:

Frédéric Cazals, Dorian Mazauric, Edoardo Sarti.

10.1 Promoting scientific activities

10.1.1 Scientific events: organisation

• Frédéric Cazals was involved in the organization of:

• Winter School Algorithms in Structural Bioinformatics: From structure resolution to dynamical
modeling in cryo-electron microscopy, Institute for Scientific Study of Cargese (IESC), November
20-24th. Web: AlgoSB.

General chair, scientific chair

• Energy Landscapes, 2023. F. Cazals was the general chair of the workshop Energy Landscapes
(Eland), the premier meeting for scientists (physicists, chemical physicists, bio-physicists, biologists,
computer scientists) working on the problem of computing (potential, free) energies for bio-
molecular systems.

Member of the organizing committees Edoardo Sarti participated to the following organizing commit-
tee:

• Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM), June 27-30th. Web:

JOBIM2023

Member of the conference program committees • Frédéric Cazals participated to the following pro-
gram committees:

• Symposium on Solid and Physical Modeling

• Intelligent Systems for Molecular Biology (ISMB)

• Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM)

• Dorian Mazauric participated to the following program committee:

• Rencontres Francophones sur les Aspects Algorithmiques des Télécommunications (AlgoTel 2023)

10.1.2 Invited talks

• Frédéric Cazals gave the following invited talks:

• Studying complex molecular mechanisms via rigid domains detection and conformational sampling

of flexible linkers, Integrative Structural Biology congress, Marseille, November 2023.

• Sampling protein conformations, Thematic meeting Probabilistic sampling for physics, Institut

Blaise Pascal, Paris-Saclay, September 2023.

• Subspace-Embedded Spherical Clusters: a novel cluster model for compact clusters of arbitrary

dimension, DataShape workshop, Porquerolles, May 2023.

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Inria Annual Report 2023

10.1.3 Leadership within the scientific community

• Frédéric Cazals:

• 2010-. . . : Member of the steering committee of the GDR Bioinformatique Moléculaire, for the

Structure and macro-molecular interactions theme.

• 2017-. . . : Co-chair, with Yann Ponty, of the working group / groupe de travail (GT MASIM - Méthodes
Algorithmiques pour les Structures et Interactions Macromoléculaires), within the GDR de BIoinfor-
matique Moléculaire (GDR BIM, GDR BIM).

10.1.4 Research administration

• Frédéric Cazals

• 2020-. . . : Member of the bureau of the EUR Life, Université Côte d’Azur.

• Dorian Mazauric

• 2019-. . . : Member of the comité Plateformes.

• Edoardo Sarti

• 2020-. . . : Member of the Commission de Développement Technologique at Inria Université Côte

d’Azur

10.2 Teaching - Supervision - Juries

10.2.1 Teaching

• 2014–. . . : Master Data Sciences Program (M2), Department of Applied Mathematics, Ecole Centrale-
Supélec; Foundations of Geometric Methods in Data Analysis; F. Cazals and M. Carrière, Inria Sophia
/ (ABS, DataShape). Web: FGMDA.

• 2021–. . . : Master Data Sciences & Artificial Intelligence (M1), Université Côte d’Azur; Introduction

to machine learning (course leader); E. Sarti; Web: IntroML

• 2021–. . . : Master Data Sciences & Artificial Intelligence (M2), Université Côte d’Azur; Geometric and
topological methods in machine learning; F. Cazals, J-D. Boissonnat and M. Carrière, Inria Sophia /
(ABS, DataShape, DataShape); Web: GTML.

• 2021–. . . : Master Cancérologie et Recherche Translationnelle (M2), Université Côte d’Azur; Binding
affinity maturation and protein interaction network analysis: two examples of bioinformatics
applications in medicine; F. Cazals.

• 2020–. . . : Master Sciences du Vivant (M2), parcours Biologie, Informatique, Mathématiques, Uni-

versité Côte d’Azur; Introduction to statistical physics of biomolecules; F. Cazals.

• 2022–2023. . . : Master : Algorithmique et Complexité, 42h Cours et TD, niveau M1, Polytech Nice

Sophia, Université Côte d’Azur, filière Sciences Informatiques, France; D. Mazauric.

• 2022–2023. . . : Master : Algorithmique avancée, 24h Cours et TD, niveau M1, Polytech Nice Sophia,
Université Côte d’Azur, filière Sciences Informatiques, France; D. Mazauric (avec Éric Pascual)

• 2022–. . . : Bachelor Sciences de la Vie (L2), Université Côte d’Azur; Introduction à la programmation

(course leader), E. Sarti; Web: IntroInfo

• 2021–2023: Bachelor Informatique (L1), Université Côte d’Azur; Introduction aux Systemes Unix

(practicals), E. Sarti

• Dizaine de formations (pour les enseignantes et enseignants, personnels de médiathèque, d’associations,

etc.)

Project ABS

10.2.2 Supervision

PhD thesis:

13

• Ongoing, October 2023-. . . : Guillaume Carrière. Attention mechanisms for graphical models, with

applications to protein structure analysis. Advisor: F. Cazals.

• Ongoing, October 2023-. . . : Ercan Seckin. Détection, histoire évolutive et relations structure –
fonction des gènes orphelins chez les bioagresseurs des plantes. Advisor: Etienne Danchin (INRAE),
Co-advisors: Dominique Colinet (INRAE), Edoardo Sarti.

• Ongoing, May 2023-. . . : Sebastián Gallardo Diaz. Optimizing newspaper aesthetics preserving
style under visual constraints: a computational approach of layouting.. Advisor: P. Kornprobst, D.
Mazauric.

10.2.3 Juries

• Frédéric Cazals participated to the following committees:

1. Conor Thomas Cafolla, Cambridge University, November 2023. Rapporteur for the PhD thesis On

Critical Care Data and Machine Learning Loss Function Landscapes. Advisor: David Wales.

2. Jeanne Trinquier, Sorbonne Université. September 2023. Rapporteur for the thesis Data-driven
generative modeling of protein sequence landscapes and beyond. Advisors: Martin Weigt, Francesco
Zamponi.

10.3 Popularization

10.3.1 Internal or external Inria responsibilities

• Dorian Mazauric

• 2019-. . . : Coordinator of Terra Numerica – vers une Cité du Numérique, an ambitious scientific
popularisation project. Its main goal is to create a "Dedicated Digital space" in the south of
France, (in the spirit of the "Cité des Sciences" or "Palais de la découverte" in Paris). To do so,
Terra Numerica is developing and structuring popularisation activities, supports which are spread
in different antennas throughout the territory (e.g., Espace Terra Numerica - Valbonne Sophia
Antipolis, Maison de l’Intelligence Artificielle (MIA), in schools, exhibition extensions...). This large-
scale project involves (brings together) all the actors of research, education, industry, associations
and collectivities... It is actually composed of more than one hundred people.

• Supervision of a bachelor student (apprenti) and two Master internships, in the scope of Terra

Numerica.

• 2018-. . . : Member of the Conseil d’Administration de l’association les Petits Débrouillards.

• 2017-. . . : Member of projet de médiation Galéjade : Graphes et ALgorithmes : Ensemble de Jeux À

Destination des Ecoliers... (mais pas que).

10.3.2 Interventions

Dorian Mazauric participated and/or organized 379 popularization events in 2023 (including 407 classes
and 11 000 young students). See Terra Numerica website.

14

Inria Annual Report 2023

11 Scientific production

11.1 Major publications

[1]

[2]

[3]

J.-C. Bermond, D. Mazauric, V. Misra and P. Nain. ‘Distributed Link Scheduling in Wireless Net-
works’. In: Discrete Mathematics, Algorithms and Applications 12.5 (2020), pp. 1–38. DOI: 10.1142
/S1793830920500585ïż¿. URL: https://hal.inria.fr/hal-01977266.

J.-D. Boissonnat and D. Mazauric. ‘On the complexity of the representation of simplicial complexes
by trees’. In: Theoretical Computer Science 617 (29th Feb. 2016), p. 17. DOI: 10.1016/j.tcs.2015
.12.034. URL: https://hal.inria.fr/hal-01259806.

J. Carr, D. Mazauric, F. Cazals and D. J. Wales. ‘Energy landscapes and persistent minima’. In: The
Journal of Chemical Physics 144.5 (2016), p. 4. DOI: 10.1063/1.4941052. URL: https://www.rep
ository.cam.ac.uk/handle/1810/253412.

[4] F. Cazals, T. Dreyfus, D. Mazauric, A. Roth and C. Robert. ‘Conformational Ensembles and Sampled
Energy Landscapes: Analysis and Comparison’. In: J. of Computational Chemistry 36.16 (2015),
pp. 1213–1231. DOI: 10.1002/jcc.23913. URL: https://hal.archives-ouvertes.fr/hal-0
1076317.

[5] F. Cazals and T. Dreyfus. ‘The Structural Bioinformatics Library: modeling in biomolecular science
and beyond’. In: Bioinformatics 33.8 (1st Apr. 2017). DOI: 10.1093/bioinformatics/btw752.
URL: https://hal.inria.fr/hal-01570848.

[6] F. Cazals and A. Lhéritier. ‘Beyond Two-sample-tests: Localizing Data Discrepancies in High-
dimensional Spaces’. In: IEEE/ACM International Conference on Data Science and Advanced Ana-
lytics. IEEE/ACM International Conference on Data Science and Advanced Analytics. IEEE/ACM
International Conference on Data Science and Advanced Analytics. Paris, France, Mar. 2015, p. 29.
URL: https://hal.inria.fr/hal-01245408.

[7] F. Cazals and A. Lhéritier. ‘Low-Complexity Nonparametric Bayesian Online Prediction with Uni-
versal Guarantees’. In: NeurIPS 2019 - Thirty-third Conference on Neural Information Processing
Systems. Vancouver, Canada, 8th Dec. 2019. URL: https://hal.inria.fr/hal-02425602.

[8] F. Cazals, D. Mazauric, R. Tetley and R. Watrigant. ‘Comparing Two Clusterings Using Matchings
between Clusters of Clusters’. In: ACM Journal of Experimental Algorithmics 24.1 (17th Dec. 2019),
pp. 1–41. DOI: 10.1145/3345951. URL: https://hal.inria.fr/hal-02425599.

[9] A. Chevallier and F. Cazals. ‘Wang-Landau Algorithm: an adapted random walk to boost conver-
gence’. In: Journal of Computational Physics 410 (2020), p. 109366. DOI: 10.1016/j.jcp.2020.10
9366. URL: https://hal.science/hal-01919860.

[10] A. Chevallier, F. Cazals and P. Fearnhead. ‘Efficient computation of the volume of a polytope
in high-dimensions using Piecewise Deterministic Markov Processes’. In: AISTATS 2022 - 25th
International Conference on Artificial Intelligence and Statistics. Virtual, France, 28th Mar. 2022.
URL: https://inria.hal.science/hal-03918039.

[11] N. Cohen, F. Havet, D. Mazauric, I. Sau Valls and R. Watrigant. ‘Complexity dichotomies for the
Minimum F -Overlay problem’. In: Journal of Discrete Algorithms 52-53 (Sept. 2018), pp. 133–142.
DOI: 10.1016/j.jda.2018.11.010. URL: https://hal.inria.fr/hal-01947563.

[12] A. Lhéritier and F. Cazals. ‘A Sequential Non-Parametric Multivariate Two-Sample Test’. In: IEEE
Transactions on Information Theory 64.5 (May 2018), pp. 3361–3370. URL: https://hal.inria.f
r/hal-01968190.

[13] S. Marillet, P. Boudinot and F. Cazals. High Resolution Crystal Structures Leverage Protein Binding

Affinity Predictions. RR-8733. Inria, Mar. 2015. URL: https://hal.inria.fr/hal-01159641.

[14]

S. Marillet, M.-P. Lefranc, P. Boudinot and F. Cazals. ‘Novel Structural Parameters of Ig–Ag Complexes
Yield a Quantitative Description of Interaction Specificity and Binding Affinity’. In: Frontiers in
Immunology 8 (9th Feb. 2017), p. 34. DOI: 10.3389/fimmu.2017.00034. URL: https://hal.arc
hives-ouvertes.fr/hal-01675467.

Project ABS

15

[15] A. Roth, T. Dreyfus, C. Robert and F. Cazals. ‘Hybridizing rapidly growing random trees and basin
hopping yields an improved exploration of energy landscapes’. In: J. Comp. Chem. 37.8 (2016),
pp. 739–752. DOI: 10.1002/jcc.24256. URL: https://hal.inria.fr/hal-01191028.

[16] M. Simsir, I. Broutin, I. Mus-Veteau and F. Cazals. ‘Studying dynamics without explicit dynamics:
A structure-based study of the export mechanism by AcrB’. In: Proteins - Structure, Function and
Bioinformatics (22nd Sept. 2020). DOI: 10.1002/prot.26012. URL: https://hal.archives-ou
vertes.fr/hal-03006981.

11.2 Publications of the year

International journals

[17] T. O’donnell, V. Agashe and F. Cazals. ‘Geometric constraints within tripeptides and the existence
of tripeptide reconstructions’. In: Journal of Computational Chemistry 44.13 (31st Mar. 2023),
pp. 1236–1249. DOI: 10.1002/jcc.27074. URL: https://inria.hal.science/hal-03917931.

[18] T. O’donnell and F. Cazals. ‘Enhanced conformational exploration of protein loops using a global
parameterization of the backbone geometry’. In: Journal of Computational Chemistry (2023). DOI:
10.1101/2022.06.21.497022. URL: https://inria.hal.science/hal-03917940.

[19] H. Schweke, Q. Xu, G. Tauriello, L. Pantolini, T. Schwede, F. Cazals, A. Lhéritier, J. Fernandez-Recio,
L. A. Rodríguez-Lumbreras, O. Schueler-Furman, J. K. Varga, B. Jiménez-García, M. F. Réau, A. M. J. J.
Bonvin, C. Savojardo, P.-l. Martelli, R. Casadio, J. Tubiana, H. J. Wolfson, R. Oliva, D. Barradas-
Bautista, T. Ricciardelli, L. Cavallo, ˇC. Venclovas, K. Olechnoviˇc, R. Guerois, J. Andreani, J. Martin,
X. Wang, G. Terashi, D. Sarkar, C. Christoffer, T. Aderinwale, J. Verburgt, D. Kihara, A. Marchand, B. E.
Correia, R. Duan, L. Qiu, X. Xu, S. Zhang, X. Zou, S. Dey, R. L. Dunbrack, E. D. Levy and S. J. Wodak.
‘Discriminating physiological from non-physiological interfaces in structures of protein complexes:
A community-wide study’. In: Proteomics 23.17 (Sept. 2023). DOI: 10.1002/pmic.202200323. URL:
https://hal.science/hal-04274728.

Reports & preprints

[20] S. Gallardo, M. C. Riff, D. Mazauric and P. Kornprobst. Newspaper Magnification with Preserved

Entry Points. 15th Sept. 2023. URL: https://hal.science/hal-04210840.

11.3 Cited publications

[21]

S. Adcock and A. McCammon. ‘Molecular dynamics: survey of methods for simulating the activity
of proteins’. In: Chemical reviews 106.5 (2006), pp. 1589–1615.

[22] F. Alber, S. Dokudovskaya, L. Veenhoff, W. Zhang, J. Kipper, D. Devos, A. Suprapto, O. Karni-Schmidt,
R. Williams, B. Chait, A. Sali and M. Rout. ‘The molecular architecture of the nuclear pore complex’.
In: Nature 450.7170 (2007), pp. 695–701.

[23] K. Ball and R. Berry. ‘Dynamics on statistical samples of potential energy surfaces’. In: The Journal

of chemical physics 111.5 (1999), pp. 2060–2070.

[24] H. Callen. Thermodynamics and an Introduction to Thermostatistics. Wiley, 1985.

[25] L. Cao, I. Goreshnik, B. Coventry, J. Case, L. Miller, L. Kozodoy, R. Chen, L. Carter, A. Walls, Y.-J. Park,
E.-M. Strauch, L. Stewart, M. Diamond, D. Veesler and D. Baker. ‘De novo design of picomolar
SARS-CoV-2 miniprotein inhibitors’. In: Science 370.6515 (2020), pp. 426–431.

[26]

J. Carr, D. Mazauric, F. Cazals and D. J. Wales. ‘Energy landscapes and persistent minima’. In: The
Journal of Chemical Physics 144.5 (2016), p. 4. DOI: 10.1063/1.4941052. URL: https://www.rep
ository.cam.ac.uk/handle/1810/253412.

[27] B. Cousins and S. Vempala. ‘A practical volume algorithm’. In: Mathematical Programming Compu-

tation 8.2 (2016), pp. 133–160.

[28] D. Frenkel and B. Smit. Understanding molecular simulation. Academic Press, 2002.

16

Inria Annual Report 2023

[29] R. Kannan, L. Lovász and M. Simonovits. ‘Random walks and an O

∗

(n5) volume algorithm for

convex bodies’. In: Random Structures & Algorithms 11.1 (1997), pp. 1–50.

[30] D. Landau and K. Binder. A guide to Monte Carlo simulations in statistical physics. Cambridge

university press, 2014.

[31] T. Lelièvre, G. Stoltz and M. Rousset. Free energy computations: A mathematical perspective. World

Scientific, 2010.

[32] C. Schön and M. Jansen. ‘Prediction, determination and validation of phase diagrams via the global

study of energy landscapes’. In: Int. J. of Materials Research 100.2 (2009), p. 135.

[33] A. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. Žídek, A. Nelson, A.
Bridgland, H. Penedones, S. Petersen, K. Simonyan, S. Crossan, K. Pushmeet, D. Jones, D. Silver,
K. Kavukcuoglu and D. Hassabis. ‘Improved protein structure prediction using potentials from
deep learning’. In: Nature (2020), pp. 1–5.

[34] D. E. Shaw, P. Maragakis, K. Lindorff-Larsen, S. Piana, R. O. Dror, M. P. Eastwood, J. A. Bank, J. M.
Jumper, J. K. Salmon, Y. Shan and W. Wriggers. ‘Atomic-level characterization of the structural
dynamics of proteins.’ In: Science 330.6002 (2010), pp. 341–346. URL: http://dx.doi.org/10.11
26/science.1187409.

[35] D. J. Wales. Energy Landscapes. Cambridge University Press, 2003.

[36] L.-P. Wang, T. J. Martinez and V. S. Pande. ‘Building force fields: an automatic, systematic, and

reproducible approach’. In: The journal of physical chemistry letters 5.11 (2014), pp. 1885–1891.
<scratchpad>
- Main topic: Computational Structural Biology (CSB)
- Key points:
  - CSB is the scientific domain concerned with developing algorithms and software to understand and predict the structure and function of biological macromolecules.
  - The field is inherently multi-disciplinary, combining biology, biophysics, and computer science.
  - The main challenges in CSB are sequence-to-structure prediction, protein function prediction, and large assembly reconstruction.
  - The research program aims to develop a comprehensive set of novel concepts and algorithms to study protein dynamics.
- Structure: The document is organized into sections, including Research Program, Project ABS, Partnerships and Cooperations, Dissemination, and Scientific Production.
- Potential artifacts to ignore: HTML tags, links, and web-related elements.
</scratchpad>

<final_summary>
The document discusses the field of Computational Structural Biology (CSB), which aims to understand and predict the structure and function of biological macromolecules. The main challenges in CSB include sequence-to-structure prediction, protein function prediction, and large assembly reconstruction. To address these challenges, the research program develops novel concepts and algorithms to study protein dynamics. The program focuses on developing probabilistic algorithms to explore energy landscapes and sample protein conformations. The document also highlights the importance of partnerships and collaborations in advancing the field of CSB. Overall, the research program seeks to advance the understanding of complex biological systems through the development of innovative computational tools and methods.
The document discusses the field of Computational Structural Biology (CSB), which aims to understand and predict the structure and function of biological macromolecules. The main challenges in CSB include sequence-to-structure prediction, protein function prediction, and large assembly reconstruction. To address these challenges, the research program develops novel concepts and algorithms to study protein dynamics. The program focuses on developing probabilistic algorithms to explore energy landscapes and sample protein conformations. The document also highlights the importance of partnerships and collaborations in advancing the field of CSB. Overall, the research program seeks to advance the understanding of complex biological systems through the development of innovative computational tools and methods.