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Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions

  • Rocco Moretti
  • , Sarel J. Fleishman
  • , Rudi Agius
  • , Mieczyslaw Torchala
  • , Paul A. Bates
  • , Panagiotis L. Kastritis
  • , João P.G.L.M. Rodrigues
  • , Mikaël Trellet
  • , Alexandre M.J.J. Bonvin
  • , Meng Cui
  • , Marianne Rooman
  • , Dimitri Gillis
  • , Yves Dehouck
  • , Iain Moal
  • , Miguel Romero-Durana
  • , Laura Perez-Cano
  • , Chiara Pallara
  • , Brian Jimenez
  • , Juan Fernandez-Recio
  • , Samuel Flores
  • Michael Pacella, Krishna Praneeth Kilambi, Jeffrey J. Gray, Petr Popov, Sergei Grudinin, Juan Esquivel-Rodríguez, Daisuke Kihara, Nan Zhao, Dmitry Korkin, Xiaolei Zhu, Omar N.A. Demerdash, Julie C. Mitchell, Eiji Kanamori, Yuko Tsuchiya, Haruki Nakamura, Hasup Lee, Hahnbeom Park, Chaok Seok, Jamica Sarmiento, Shide Liang, Shusuke Teraguchi, Daron M. Standley, Hiromitsu Shimoyama, Genki Terashi, Mayuko Takeda-Shitaka, Mitsuo Iwadate, Hideaki Umeyama, Dmitri Beglov, David R. Hall, Dima Kozakov, Sandor Vajda, Brian G. Pierce, Howook Hwang, Thom Vreven, Zhiping Weng, Yangyu Huang, Haotian Li, Xiufeng Yang, Xiaofeng Ji, Shiyong Liu, Yi Xiao, Martin Zacharias, Sanbo Qin, Huan Xiang Zhou, Sheng You Huang, Xiaoqin Zou, Sameer Velankar, Joël Janin, Shoshana J. Wodak, David Baker
  • University of Washington
  • Weizmann Institute of Science
  • Cancer Research UK
  • Utrecht University
  • Virginia Commonwealth University
  • Université libre de Bruxelles
  • Barcelona Supercomputing Center
  • Uppsala University
  • Johns Hopkins University
  • Institut national de recherche en informatique et en automatique
  • University Grenoble Alpes
  • Purdue University
  • University of Missouri
  • University of Wisconsin-Madison
  • Japan Biological Informatics Consortium (JBIC)
  • Ochanomizu University
  • The University of Osaka
  • Seoul National University
  • Kitasato University
  • Chuo University
  • Boston University
  • University of Massachusetts Medical School
  • Huazhong University of Science and Technology
  • Technical University of Munich
  • Florida State University
  • European Molecular Biology Laboratory
  • Université Paris-Sud
  • University of Toronto

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

85 Citas (Scopus)

Resumen

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.

Idioma originalInglés
Páginas (desde-hasta)1980-1987
Número de páginas8
PublicaciónProteins: Structure, Function and Bioinformatics
Volumen81
N.º11
DOI
EstadoPublicada - nov 2013
Publicado de forma externa

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