Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions
Rocco Moretti
(1, 2)
,
Sarel J Fleishman
(3, 2)
,
Rudi Agius
(2, 4)
,
Mieczyslaw Torchala
(2, 4)
,
Paul A. Bates
(4)
,
Panagiotis L Kastritis
(5)
,
João P. G. L. M. Rodrigues
(5)
,
Mikael Trellet
(5)
,
Alexandre M. J. J. Bonvin
(5)
,
Meng Cui
(6)
,
Marianne Rooman
(7)
,
Dimitri Gillis
(8)
,
Yves Dehouck
(9)
,
Iain Moal
(10)
,
Miguel Romero-Durana
(10)
,
Laura Pérez-Cano
(10)
,
Chiara Pallara
(10)
,
Brian Jimenez
(10)
,
Juan Fernández-Recio
(10)
,
Samuel C Flores
(11)
,
Michael Pacella
(12)
,
Krishna Praneeth Kilambi
(12)
,
Jeffrey J Gray
(12)
,
Petr Popov
(13, 2)
,
Sergei Grudinin
(2, 13)
,
Juan Esquivel-Rodríguez
(14)
,
Daisuke Kihara
(14)
,
Nan Zhao
(15)
,
Dmitry Korkin
(15)
,
Xiaolei Zhu
(16)
,
Omar N A Demerdash
(16)
,
Julie Mitchell
(16)
,
Eiji Kanamori
(17)
,
Yuko Tsuchiya
(18)
,
Haruki Nakamura
(19)
,
Hasup Lee
(20)
,
Hahnbeom Park
(20)
,
Chaok Seok
(20)
,
Jamica Sarmiento
(19)
,
Shide Liang
(19)
,
Shusuke Teraguchi
(19)
,
Daron M Standley
(19)
,
Hiromitsu Shimoyama
(21)
,
Genki Terashi
(21)
,
Mayuko Takeda-Shitaka
(21)
,
Mitsuo Iwadate
(22)
,
Hideaki Umeyama
(22)
,
Dmitri Beglov
(23)
,
David R Hall
(23)
,
Dima Kozakov
(23)
,
Sandor Vajda
(23)
,
Brian G Pierce
(24)
,
Howook Hwang
(24)
,
Thom Vreven
(24)
,
Zhiping Weng
(24)
,
Yangyu Huang
(25)
,
Haotian Li
(25)
,
Xiufeng Yang
(25)
,
Xiaofeng Ji
(25)
,
Shiyong Liu
(25)
,
Yi Xiao
(25)
,
Martin Zacharias
(26)
,
Sanbo Qin
(27)
,
Huan-Xiang Zhou
(27)
,
Sheng-You Huang
(15)
,
Xiaoqin Zou
(15)
,
Sameer Velankar
(28)
,
Joel Janin
(29)
,
Shoshana J Wodak
(30, 31)
,
David Baker
(1)
1
Department of Biochemistry [Washington ]
2 Molecular Carcinogenesis [Sutton]
3 Weizmann Institute of Science [Rehovot, Israël]
4 Biomolecular Modelling laboratory [London]
5 Bijvoet Center of Biomolecular Research [Utrecht]
6 VCU - Virginia Commonwealth University
7 ULB - Département d'Informatique [Bruxelles]
8 ULB - Université libre de Bruxelles
9 3BIO-BIO-INFO - Unite de Bioinformatique genomique et structurale
10 BSC-CNS - Barcelona Supercomputing Center - Centro Nacional de Supercomputacion
11 Department of Cell and Molecular Biology [Uppsala]
12 JHU - Johns Hopkins University
13 NANO-D - Algorithms for Modeling and Simulation of Nanosystems
14 Department of Computer Science [Purdue]
15 Mizzou - University of Missouri [Columbia]
16 Department of Mathematics [Madison]
17 Japan Biological Informatics Consortium [Tokyo]
18 Ochanomizu University
19 Osaka University [Osaka]
20 SNU - Seoul National University [Seoul]
21 Kitasato University
22 Chuo University - Chuo University
23 Department of Biomedical Engineering [Boston]
24 UMASS - University of Massachusetts Medical School [Worcester]
25 HUST - Huazhong University of Science and Technology [Wuhan]
26 TUM - Technische Universität Munchen - Technical University Munich - Université Technique de Munich
27 FSU - Florida State University [Tallahassee]
28 EMBL-EBI - European Bioinformatics Institute [Hinxton]
29 IBBMC - Institut de biochimie et biophysique moléculaire et cellulaire
30 University of Toronto
31 SickKids - The Hospital for sick children [Toronto]
2 Molecular Carcinogenesis [Sutton]
3 Weizmann Institute of Science [Rehovot, Israël]
4 Biomolecular Modelling laboratory [London]
5 Bijvoet Center of Biomolecular Research [Utrecht]
6 VCU - Virginia Commonwealth University
7 ULB - Département d'Informatique [Bruxelles]
8 ULB - Université libre de Bruxelles
9 3BIO-BIO-INFO - Unite de Bioinformatique genomique et structurale
10 BSC-CNS - Barcelona Supercomputing Center - Centro Nacional de Supercomputacion
11 Department of Cell and Molecular Biology [Uppsala]
12 JHU - Johns Hopkins University
13 NANO-D - Algorithms for Modeling and Simulation of Nanosystems
14 Department of Computer Science [Purdue]
15 Mizzou - University of Missouri [Columbia]
16 Department of Mathematics [Madison]
17 Japan Biological Informatics Consortium [Tokyo]
18 Ochanomizu University
19 Osaka University [Osaka]
20 SNU - Seoul National University [Seoul]
21 Kitasato University
22 Chuo University - Chuo University
23 Department of Biomedical Engineering [Boston]
24 UMASS - University of Massachusetts Medical School [Worcester]
25 HUST - Huazhong University of Science and Technology [Wuhan]
26 TUM - Technische Universität Munchen - Technical University Munich - Université Technique de Munich
27 FSU - Florida State University [Tallahassee]
28 EMBL-EBI - European Bioinformatics Institute [Hinxton]
29 IBBMC - Institut de biochimie et biophysique moléculaire et cellulaire
30 University of Toronto
31 SickKids - The Hospital for sick children [Toronto]
Sergei Grudinin
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- Fonction : Auteur correspondant
- PersonId : 175316
- IdHAL : sergei-grudinin
- ORCID : 0000-0002-1903-7220
- IdRef : 186189729
Connectez-vous pour contacter l'auteur
Daisuke Kihara
- Fonction : Auteur
- PersonId : 765070
- ORCID : 0000-0003-4091-6614
- IdRef : 155148435
Nan Zhao
- Fonction : Auteur
- PersonId : 770078
- ORCID : 0000-0003-0517-9452
Chaok Seok
- Fonction : Auteur
- PersonId : 765067
- ORCID : 0000-0002-1419-9888
Dima Kozakov
- Fonction : Auteur
- PersonId : 761929
- ORCID : 0000-0003-0464-4500
Sandor Vajda
- Fonction : Auteur
- PersonId : 761930
- ORCID : 0000-0003-1540-8220
Xiaoqin Zou
- Fonction : Auteur
- PersonId : 765071
- ORCID : 0000-0003-0637-8648
David Baker
- Fonction : Auteur
- PersonId : 760695
- ORCID : 0000-0002-7184-6594
Résumé
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.