Section: Scientific Foundations

RNA and protein structures

RNA

Participants : Julie Bernauer, Alain Denise, Feng Lou, Yann Ponty, Mireille Régnier, Philippe Rinaudo, Jean-Marc Steyaert.

Common activity with P. Clote (Boston College and Digiteo).

From RNA structure to function

Recoding conventional phenomena for the translation of messenger RNA (mRNA) into proteins, including frameshift, readthrough, hopping, where a single mRNA sequence allows the synthesis of (at least) two different polypeptides. Recoding is mandatory for many virus machinery and viability, and this process involves particular motifs and secondary structures in mRNAs. We develop two complementary computational methods that aim to find genes subject to recoding events in genomes. The first one is based on a model for the recoding site ; the second one is based on a comparative genomics approach at a large scale. In both cases, our predictions are subject to experimental biological validation by our collaborators at Igm (Institut de Génétique et Microbiologie), Paris-Sud University. We also study an other biological process that may involve particular motifs and structures in mRNAs: nonstop mRNA decay (NSD) and no-go mRNA (NGD) decay, that are recently identified mechanisms that control the quality of RNA transcription. This work is currently funded by the ANR (project NGD-NSD , ANR BLANC 2010-2014).

Additionally, we are currently developing a combinatorial approach, based on random generation, to design small and structured RNAs. An application of such a methodology to the Gag-Pol HIV-1 frameshifting site will be carried out with our collaborators at Igm . We hope that, upon capturing the hybridization energy at the design stage, one will be able to gain control over the rate of frameshift and consequently fine-tune the expression of Gag/Pol. Our goal is to build these RNA sequences such that their hybridization with existing mRNAs will be favorable to independent folding, and will therefore affect the stability of some secondary structures involved in recoding events. Moreover it has been observed, mainly on bacteria, that some mRNA sequences may adopt an alternate fold. Such an event is called a riboswitch. A common feature of recoding events or riboswitches is that some structural elements on mRNA initiate unusual action of the ribosome or allow for an alternate fold under some environmental conditions. One challenge is to predict genes that might be subject to riboswitches. Additionnally, we are currently developing a combinatorial approach, based on random generation, to design small and structured RNAs. Our goal is to build these RNAs such that their hybridization with existing mRNAs will be favorable to independent folding, and will therefore affect the stability of some secondary structures involved in recoding events. An application of such a methodology to the Gag-Pol HIV-1 frameshifting site will be carried out with our collaborators at Igm . We hope that, upon capturing the hybridization energy at the design stage, one will be able to gain control over the rate of frameshift and consequently fine-tune the expression of Gag/Pol.

Beyond the secondary structure

One of our major challenges is to go beyond secondary structure. Over the past decade, few attempts have been made to predict the 3D structure of RNA from sequence only. So far, few groups have taken this leap. Despite the promises shown by their preliminary results, these approaches currently suffer to a limiting scale due to either their high algorithmic complexity or their difficult automation. Using our expertise in algorithmics and modeling, we plan to design original methods, notably within the AMIS-ARN project (ANR BLANC 2008-2012) in collaboration with PRISM at Versailles University and E.Westhof's group at Strasbourg.

1. Ab initio modeling: Starting from the predicted RNA secondary structure, we aim to detect local structural motifs in it, giving local 3D conformations. We use the resulting partial structure as a flexible scaffold for a multi-scale reconstruction, notably using game theory. We believe the latter paradigm offers a more realistic view of biological processes than global optimization, used by our competitors, and constitutes a real originality of our project.

2. Comparative modeling: we investigate new algorithms for predicting 3D structures by a comparative approach. This involves comparing multiple RNA sequences and structures at a large scale, that is not possible with current algorithms. Successful methods must rely both on new graph algorithms and on biological expertise on sequence-structure relations in RNA molecules.

RNA 3D structure evaluation

The biological function of macromolecules such as proteins and nucleic acids relies on their dynamic structural nature and their ability to interact with many different partners. Their function is mainly determined by the structure those molecules adopt as protein and nucleic acids differ from polypeptides and polynucleotides by their spatial organization. This is specially challenging for RNA where structure flexibility is key.

To address those issues, one has to explore the biologically possible spatial configurations of a macromolecule. The two most common techniques currently used in computational structural biology are Molecular Dynamics (MD) and Monte Carlo techniques (MC). Those techniques require the evaluation of a potential or force-field, which for computational biology are often empirical. They mainly consist of a summation of bonded forces associated with chemical bonds, bond angles, and bond dihedrals, and non-bonded forces associated with van der Waals forces and electrostatic charge. Even if there exists implicit solvent models, they are yet not very well performing and still require a lot of computation time.

Our goal, in collaboration with the Levitt lab at Stanford University (Associate Team GNAPI http://www.lix.polytechnique.fr/~bernauer/EA_GNAPI/ ) is to develop knowledge-based potentials, based on measurements on known RNA 3D structure. Such potential are quick to evaluate during a simulation and can be used without having to explicitly address the solvent problem. They can be developed at various level of representation: atom, base, nucleotide, domain and could allow the modelling of a wide size range: from an hairpin to the whole ribosome. We also intend to combine these knowledge-based potentials with other potentials (hybrid modelling) and template-based techniques, allowing accurate modelling and dynamics study of very large RNA molecules. Such studies are still a challenge.

PROTEINS

Participants : Jérôme Azé, Julie Bernauer, Adrien Guilhot-Gaudeffroy, Saad Sheikh, Jean-Marc Steyaert, Thuong Van Du Tran.

Docking and evolutionary algorithms

As mentioned above, the function of many proteins depends on their interaction with one or many partners. Docking is the study of how molecules interact. Despite the improvements due to structural genomics initiatives, the experimental solving of complex structures remains a difficult problem. The prediction of complexes, docking, proceeds in two steps: a configuration generation phase or exploration and an evaluation phase or scoring. As the verification of a predicted conformation is time consuming and very expensive, it is a real challenge to reduce the time dedicated to the analysis of complexes by the biologists. Various algorithms and techniques have been used to perform exploration and scoring [43] . The recent rounds of the Capri challenge show that real progress has been made using new techniques [40] . Our group has strong experience in cutting edge geometric modelling and scoring techniques using machine learning strategies for protein-protein complexes. In a collaboration with A. Poupon, Inra -Tours, a method that sorts the various potential conformations by decreasing probability of being real complexes has been developed. It relies on a ranking function that is learnt by an evolutionary algorithm. The learning data are given by a geometric modelling of each conformation obtained by the docking algorithm proposed by the biologists. Objective tests are needed for such predictive approaches. The Critical Assessment of Predicted Interaction, Capri , a community wide experiment modelled after Casp was set up in 2001 to achieve this goal (http://www.ebi.ac.uk/msd-srv/capri/ ). First results achieved for Capri'02 suggested that it is possible to find good conformations by using geometric information for complexes. This approach has been followed (see section New results). As this new algorithm will produce a huge amount of conformations, an adaptation of the ranking function learning step is needed to handle them. In the near future, we intend to extend our approach to protein-RNA complexes.

Computational Protein Design

A protein amino acid sequence determines its structure and biological function, but no concise and systematic set of rules has been stated up to now to describe the functions associated to a sequence; experimental methods are time (and money) consuming. Massive genome sequencing has revealed the sequences of millions of proteins, whereas roughly $55.000$ 3D protein structures, only, are known yet. Structure prediction in silico attempts to fill up the gap. It consists in finding a tentative spatial (3D) conformation that a given nucleotidic or aminoacid sequence is likely to adopt, using the modelling by homology. A second problem of interest is inverse protein folding or computational protein design (CPD): the prediction of (the most favorable) amino-acid sequences that adopt a particular target tertiary structure. One main question is to map the millions of protein sequences extracted from the genomes onto the tens of thousand known 3D structures. This problem has many implications such as protein folding and stability, structure prediction (fold recognition), or protein evolution. Moreover, it is a mandatory step towards the design of new, artificial proteins. The engineering of protein-ligand interactions also has great biological and technological value. For example, the recent engineering of aminoacyl-tRNA synthetase (aaRS) enzymes has led to organisms with a modified genetic code, expanded to include nonnatural aminoacids.

Another novel ingredient is the use of negative design: the ability to select against sequences that have undesired properties, such as a tendency to fold into alternate, undesired structures. It can be critical for attaining specificity when competing states are close in (stability) structure space. There are also current efforts to enlarge this thermodynamical point of view by a new knowledge on natural proteins with known conformations.

Transmembrane proteins

Our goal is to predict the structure of different classes of barrel proteins. Those proteins contain the two large classes of transmembrane proteins, which carry out important functions. Nevertheless, their structure is yet difficult to determine by standard experimental methods such as X-ray cristallography or NMR. Most existing methods only address single-domain protein structures. Therefore, for large proteins, a preprocessing to determine the protein domains is necessary. Then, a suitable model of energy functions needs to be designed for each specific class. We have designed a pseudo-energy minimization method for the prediction of the super-secondary structure of $\beta$-barrel or $\alpha$-helical-barrel proteins with structural knowledge-based enhancement. The method relies on graph based modelling and also deals with various topological constraints such as Greek key or Jelly roll conformations.