Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions
Authors listRocco Moretti Sarel J Fleishman Rudi Agius Mieczyslaw Torchala Paul Bates Panagiotis L Kastritis João PGLM Rodrigues Mikaël Trellet Alexandre MJJ 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 NA 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
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.