{"id":18,"date":"2011-07-21T07:17:33","date_gmt":"2011-07-21T05:17:33","guid":{"rendered":"http:\/\/team.inria.fr\/deptdkm1\/?page_id=18"},"modified":"2025-05-19T10:50:49","modified_gmt":"2025-05-19T08:50:49","slug":"seminar","status":"publish","type":"page","link":"https:\/\/dept-dkm.irisa.fr\/fr\/seminar\/","title":{"rendered":"(English) Seminar"},"content":{"rendered":"
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La page de pr\u00e9sentation<\/h2>\r\nExemple : abs<\/a><\/blockquote>\r\n

Overall objectives<\/h3>

Biomolecules and their function(s).<\/h3>

\u00a0Computational 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 bioinformatics supply experimental data, which are of two main kinds. On the one hand, genome sequencing projects give supply protein sequences, and ~200 millions of sequences have been archived in UniProtKB<\/hi>\/TrEMBL<\/hi>\u00a0\u2013 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 \u2013 atomic coordinates. Alas, only ~150,000 structures have been solved and deposited in the Protein Data Bank (PDB), a number to be compared against the \u223c<\/mo>10<\/mn>8<\/mn><\/msup><\/mrow><\/math><\/formula> sequences found in UniProtKB<\/hi>\/TrEMBL<\/hi>. 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<\/mi><\/math><\/formula> atoms has d<\/mi>=<\/mo>3<\/mn>n<\/mi><\/mrow><\/math><\/formula> Cartesian coordinates, and fixing these (up to rigid motions) defines a conformation. As conveyed by the iconic lock-and-key<\/hi> metaphor for interacting molecules, Biology is based on the interactions stable conformations 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 \u2013 see d<\/mi>=<\/mo>3<\/mn>n<\/mi><\/mrow><\/math><\/formula> 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 \u226b<\/mo>10<\/mn>12<\/mn><\/msup><\/mrow><\/math><\/formula> conformations\/frames, a (brute) tour de force<\/hi> rarely achieved\u00a038<\/ref>.<\/p> <\/subsection>

Computational Structural Biology: three main challenges.<\/h3>

\u00a0The first challenge, sequence-to-structure prediction<\/hi>, 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 37<\/ref>, the models obtained so far are static and coarse-grained.<\/p>

The second one is protein function prediction<\/hi>. Given a protein with known structure, i.e.<\/hi>, 3D coordinates, 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.<\/p>
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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 29<\/ref>.<\/hi> 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)<\/hi> Design of a helical protein (orange) mimicking a region of the ACE2 protein. (B)<\/hi> Assessment of binding modes (conformation, binding energies) of candidate miniproteins neutralizing the RBD.<\/caption><\/object>

Finally, the third one, large assembly reconstruction<\/hi>, 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 26<\/ref>. It is often referred to as reconstruction by data integration<\/hi>, 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.<\/p>

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<\/ref>). The first challenge is illustrated by the problem of predicting the structure of a blocker protein from its sequence of amino-acids \u2013 a tractable problem here since the mini proteins used only comprise of the order of 50 amino-acids (Fig. 1<\/ref>(A), 29<\/ref>). 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<\/ref>(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 29<\/ref>, 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<\/hi> minibinders identified provide starting points for SARS-CoV-2 therapeutics\u00a029<\/ref>. We note in passing that this is truly remarkable work, yet, the designed proteins stem from a template (the bottom<\/hi> helix from ACE2), and are rather small.<\/p>
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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<\/mi>><\/mo>100<\/mn><\/mrow><\/math><\/formula>.<\/caption><\/object> <\/subsection>

Protein dynamics: core CS – maths challenges.<\/h3>

To present challenges in structural modeling, let us recall the following ingredients (Fig. 2<\/ref>). First, a molecular model with n<\/mi><\/math><\/formula> atoms is parameterized over a conformational space \ud835\udcb3<\/mi><\/math><\/formula> of dimension d<\/mi>=<\/mo>3<\/mn>n<\/mi><\/mrow><\/math><\/formula> in Cartesian coordinates, or d<\/mi>=<\/mo>3<\/mn>n<\/mi>–<\/mo>6<\/mn><\/mrow><\/math><\/formula> in internal coordinate\u2013upon removing rigid motions, also called degree of freedom (d.o.f.<\/hi>). Second, recall that the potential energy landscape<\/hi> (PEL) is the mapping V<\/mi>(<\/mo>\u00b7<\/mo>)<\/mo><\/mrow><\/math><\/formula> from \u211d<\/mi>d<\/mi><\/msup><\/math><\/formula> to \u211d<\/mi><\/math><\/formula> providing a potential energy for each conformation\u00a039<\/ref>, 36<\/ref>. Example potential energies (PE) are CHARMM<\/hi>, AMBER<\/hi>, MARTINI<\/hi>, 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 \u223c<\/mo>1<\/mn>,<\/mo>000<\/mn><\/mrow><\/math><\/formula>), fitted to reproduce physico-chemical properties of (bio-)molecules\u00a040<\/ref>.<\/p>

These PE are usually considered good enough to study non covalent interactions \u2013 our focus, even though they do not cover the modification of chemical bonds. In any case, we take such a function for granted 1<\/ref>.<\/p>

The PEL codes all structural, thermodynamic<\/hi>, and kinetic<\/hi> properties, which can be obtained by averaging properties of conformations over so-called thermodynamic ensembles<\/hi>. The structure<\/hi> of a macromolecular system requires the characterization of active conformations and important intermediates in functional pathways involving significant basins. In assigning occupation probabilities to these conformations by integrating Boltzmann’s distribution, one treats thermodynamics<\/hi>. Finally, transitions between the states, modeled, say, by a master equation (a continuous-time Markov process), correspond to kinetics<\/hi>. 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 \u201cfor the development of multiscale models for complex chemical systems<\/hi>\u201d. However, except for highly specialized cases where massive calculations have been used 38<\/ref>, 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 32<\/ref>. 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.<\/p>

The hardness of structural modeling owes to three intertwined reasons.<\/p>

First, PELs of biomolecules usually exhibit a number of critical points exponential in the dimension\u00a027<\/ref>; fortunately, they enjoy a multi-scale structure\u00a030<\/ref>. Intuitively, the significant local minima\/basins are those which are deep<\/hi> or isolated\/wide<\/hi>, 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 \u223c<\/mo><\/math><\/formula> 15 and \u223c<\/mo><\/math><\/formula> 4 orders of magnitude in time and amplitude respectively 25<\/ref>. Developing methods able to exploit this multi-scale structure has remained elusive. Third, macroscopic properties of biomolecules, i.e.<\/hi>, observables, are average properties computed over ensembles of conformations, which calls for a multi-scale statistical treatment both of thermodynamics and kinetics.<\/p> <\/subsection>

Validating models.<\/h3>

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 \u2013 when the experiments can be conducted.<\/p>

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 K<\/mi>o<\/mi>n<\/mi><\/mrow><\/msub><\/math><\/formula> and K<\/mi>o<\/mi>f<\/mi>f<\/mi><\/mrow><\/msub><\/math><\/formula>), or fluorescence based methods (for kinetics of folding).<\/p> <\/subsection>

Last activity report : 2025 <\/h3>