@article {2020,
	title = {Protein Interaction Energy Landscapes are Shaped by Functional and also Non-functional Partners.},
	journal = {J Mol Biol},
	volume = {432},
	year = {2020},
	month = {2020 Feb 14},
	pages = {1183-1198},
	abstract = {<p>In the crowded cell, a strong selective pressure operates on the proteome to limit the competition between functional and non-functional protein-protein interactions. We developed an original theoretical framework in order to interrogate how this competition constrains the behavior of proteins with respect to their partners or random encounters. Our theoretical framework relies on a two-dimensional (2D) representation of interaction energy landscapes, with 2D energy maps, which reflect in a synthetic way the spatial distribution of the interaction propensity of a protein surface for another protein. We realized the interaction propensity mapping of proteins\&$\#$39; surfaces in interaction with functional and arbitrary partners and asked whether the distribution of their interaction propensity is conserved during evolution. Therefore, we performed several thousands of cross-docking simulations to systematically characterize the energy landscapes of 103 proteins interacting with different sets of homologs, corresponding to their functional partner\&$\#$39;s family or arbitrary protein families. Then, we systematically compared the energy maps resulting from the docking of each protein with the different protein families of the dataset. Strikingly, we show that the interaction propensity not only of the binding sites but also of the rest of the surface is conserved for docking partners belonging to the same protein family. Interestingly, this observation holds for docked proteins corresponding to true but also arbitrary partners. Our theoretical framework enables the characterization of the energy behavior of a protein in interaction with hundreds of proteins and opens the way for the characterization of the behavior of proteins in a specific environment.</p>
},
	issn = {1089-8638},
	doi = {10.1016/j.jmb.2019.12.047},
	author = {Schweke, Hugo and Mucchielli, Marie-H{\'e}l{\`e}ne and S Sacquin-Mora and Bei, Wanying and Lopes, Anne}
}
@article {2018|2044,
	title = {Meet-U: Educating through research immersion},
	journal = {PLOS Computational Biology},
	volume = {14},
	year = {2018},
	month = {03},
	pages = {1-10},
	abstract = {<p>We present a new educational initiative called Meet-U that aims to train students for collaborative work in computational biology and to bridge the gap between education and research. Meet-U mimics the setup of collaborative research projects and takes advantage of the most popular tools for collaborative work and of cloud computing. Students are grouped in teams of 4\&ndash;5 people and have to realize a project from A to Z that answers a challenging question in biology. Meet-U promotes \&quot;coopetition,\&quot; as the students collaborate within and across the teams and are also in competition with each other to develop the best final product. Meet-U fosters interactions between different actors of education and research through the organization of a meeting day, open to everyone, where the students present their work to a jury of researchers and jury members give research seminars. This very unique combination of education and research is strongly motivating for the students and provides a formidable opportunity for a scientific community to unite and increase its visibility. We report on our experience with Meet-U in two French universities with master\&rsquo;s students in bioinformatics and modeling, with protein\&ndash;protein docking as the subject of the course. Meet-U is easy to implement and can be straightforwardly transferred to other fields and/or universities. All the information and data are available at www.meet-u.org.</p>
},
	doi = {10.1371/journal.pcbi.1005992},
	url = {https://doi.org/10.1371/journal.pcbi.1005992},
	author = {Abdollahi, Nika and Albani, Alexandre and Anthony, Eric and Baud, Agnes and Cardon, M{\'e}lissa and Clerc, Robert and Czernecki, Dariusz and Conte, Romain and David, Laurent and Delaune, Agathe and Djerroud, Samia and Fourgoux, Pauline and Guiglielmoni, Nad{\`e}ge and Laurentie, Jeanne and Lehmann, Nathalie and Lochard, Camille and Montagne, R{\'e}mi and Myrodia, Vasiliki and Opuu, Vaitea and Parey, Elise and Polit, L{\'e}lia and Priv{\'e}, Sylvain and Quignot, Chlo{\'e} and Ruiz-Cuevas, Maria and Sissoko, Mariam and Sompairac, Nicolas and Vallerix, Audrey and Verrecchia, Violaine and Delarue, Marc and Gu{\'e}rois, Raphael and Ponty, Yann and S Sacquin-Mora and Carbone, Alessandra and Froidevaux, Christine and Le Crom, St{\'e}phane and Lespinet, Olivier and Weigt, Martin and Abboud, Samer and Bernardes, Juliana and Bouvier, Guillaume and Dequeker, Chlo{\'e} and Ferr{\'e}, Arnaud and Fuchs, Patrick and Lelandais, Ga{\"e}lle and Poulain, Pierre and Richard, Hugues and Schweke, Hugo and Laine, Elodie and Lopes, Anne}
}
@article {2013|1961,
	title = {Protein-protein interactions in a crowded environment: an analysis via cross-docking simulations and evolutionary information},
	journal = {Plos Comput. Biol.},
	volume = {9},
	number = {12},
	year = {2013},
	month = {dec},
	pages = {e1003369},
	doi = {10.1371/journal.pcbi.1003369},
	url = {http://hal.inria.fr/hal-00875116},
	author = {Lopes, Anne and S Sacquin-Mora and Dimitrova, Viktoriya and Laine, Elodie and Ponty, Yann and Carbone, Alessandra}
}
