Exploring structural interactomes in the light of evolution

Macromolecular interactions are central to most biological processes. Protein docking aims to predict the most likely structural binding modes of interacting protein partners. Understanding how binding partners coevolved can provide essential clues to improve the structural prediction of protein interfaces. In the past few years, our team has contributed to the improvement of protein docking methods by combining evolutionary information with more traditional approaches. I will present how we analyzed the way interface structure coevolved [1] and developed InterEvDock, a server for protein-protein docking designed to integrate evolutionary information in the docking process [2, 3]. InterEvDock uses one of the most successful interface sampling algorithms to date, which relies on fast Fourier transforms, followed by a consensus scoring scheme to discriminate correct from incorrect interfaces. InterEvDock was benchmarked on a large dataset of docking targets based on unbound homology models [4]. Finally, I will discuss how we successfully applied this pipeline to targets of the international CAPRI assembly prediction challenge [5] and to various biological applications [6, 7].

1.         Andreani, Faure & Guerois. Versatility and invariance in the evolution of homologous heteromeric interfaces. PLoS Comput Biol, 2012. 8(8): e1002677. http://doi.org/10.1371/journal.pcbi.1002677

2.         Yu, Vavrusa, Andreani, Rey, Tuffery & Guerois. InterEvDock: a docking server to predict the structure of protein-protein interactions using evolutionary information. Nucleic Acids Res, 2016. 44(W1): W542-9. http://doi.org/10.1093/nar/gkw340

3.         Quignot, Rey, Yu, Tuffery, Guerois & Andreani. InterEvDock2: an expanded server for protein docking using evolutionary and biological information from homology models and multimeric inputs. Nucleic Acids Res, 2018. 46(W1): W408-W416. http://doi.org/10.1093/nar/gky377

4.         Yu & Guerois. PPI4DOCK: large scale assessment of the use of homology models in free docking over more than 1000 realistic targets. Bioinformatics, 2016. 32(24): 3760-3767. http://doi.org/10.1093/bioinformatics/btw533

5.         Yu, Andreani, Ochsenbein & Guerois. Lessons from (co-)evolution in the docking of proteins and peptides for CAPRI Rounds 28-35. Proteins, 2017. 85(3): 378-390. http://doi.org/10.1002/prot.25180

6.         Lisboa, Andreani, Sanchez, Boudes, Collinet, Liger, van Tilbeurgh, Guerois & Quevillon-Cheruel. Molecular determinants of the DprA-RecA interaction for nucleation on ssDNA. Nucleic Acids Res, 2014. 42(11): 7395-408. http://doi.org/10.1093/nar/gku349

7.         Berto, Yu, Morchoisne-Bolhy, Bertipaglia, Vallee, Dumont, Ochsenbein, Guerois & Doye. Disentangling the molecular determinants for Cenp-F localization to nuclear pores and kinetochores. EMBO Rep, 2018. 19(5). http://doi.org/10.15252/embr.201744742