Section: New Results

3D Modelling and Interactions

Transmembrane proteins

Transmembrane beta-barrel proteins (TMB) account for 20 to 30% of identified proteins in a genome but, due to difficulties with standard experimental techniques, they are only 2% of the RCSB Protein Data Bank. As TMB perform many vital functions, the prediction of their structure is a challenge for life sciences, while the small number of known structures prohibits knowledge-based methods for structure prediction. We study and design algorithmic solutions addressing the secondary structure, an abstraction of the 3D conformation of a molecule, that only retains the contacts between its residues. As TMBs are strongly structured objects, model based methodologies [18] are an interesting alternative to conventional methods. The efficiently obtained 3D structures provide a good model for further 3D and interaction analyses.

3D Interaction prediction

While protein-RNA complexes provide a wide range of essential functions in the cell, their atomic experimental structure solving is even more difficult than for proteins. Protein-RNA complexes provide a wide range of essential functions in the cell. Docking approaches that have been developed for proteins are often challenging to adapt for RNA because of its inherent flexibility and the structural data available being relatively scarce.We adapted the reference RosettaDock protocol for protein-RNA complexes both at the nucleotide and atomic levels. Using a genetic algorithm-based strategy, and a non-redundant protein-RNA dataset, we derived a RosettaDock scoring scheme able not only to discriminate but also score efficiently docking decoys. The approach proved to be both efficient and robust for generating and identifying suitable structures when applied to two protein-RNA docking benchmarks in both bound and unbound settings. It also compares well to existing strategies. This is the first approach that currently offers a multi-level optimized scoring approach integrated in a full docking suite, leading the way to adaptive fully flexible strategies [28] , [12] . This work is part of the PhD thesis of Adrien Guilhot-Gaudeffroy. While the previously described approaches perform well in a rigid or semi-flexible docking setting, the generation of putative conformations for flexible molecules (sampling) is still a difficult question that has to be addressed in a multi-scale setting involving new algorithms. Docking these sampled conformations will also certainly require improvement in clustering approaches.