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An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Authors :
Shah U
Lim Heo
Renzhi Cao
Chaok Seok
Gaurav Chopra
Soma Ghosh
Phanourios Tamamis
Maghrabi Aha
Sergey Ovchinnikov
Hailong Li
Chris A. Kieslich
Dhanasekaran Bk
Milot Mirdita
Rafał Ślusarz
Adam Liwo
Kim De
Gyu Rie Lee
Michael Levitt
James Smadbeck
Blake L
Adam K. Sieradzan
Seth Cooper
Andrzej Kloczkowski
Zoran Popović
Rodrigo Antonio Faccioli
Cezary Czaplewski
Yuxin Yin
Jie Hou
Brian Koepnick
Shah A
Jilong Li
Maciej Baranowski
Chen Keasar
Yang Zhang
Delbem Acb
Magdalena A. Mozolewska
Christodoulos A. Floudas
Agnieszka G. Lipska
Badri Adhikari
Yi He
Dimas I
Leandro Oliveira Bortot
Liam J. McGuffin
Paweł Krupa
Bartłomiej Zaborowski
David Baker
Alexandre Defelicibus
Eshel Faraggi
Melis Onel
Johannes Söding
Tomasz K Wirecki
Jeff Flatten
Jianlin Cheng
Firas Khatib
Dong Xu
Silvia Crivelli
Stanisław Ołdziej
Saraswathi Vishveshwara
Debswapna Bhattacharya
Golon L
George A. Khoury
Harold A. Scheraga
Artur Giełdoń
Jaume Bacardit
Chapman N
Björn Wallner
Shokoufeh Mirzaei
Khan M
Magdalena J. Ślusarz
Tomer Sidi
Trieber N
Robert Ganzynkowicz
Source :
Scientific reports, vol 8, iss 1, Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual), Universidade de São Paulo (USP), instacron:USP, Scientific Reports, Scientific Reports, Vol 8, Iss 1, Pp 1-18 (2018)
Publication Year :
2018
Publisher :
eScholarship, University of California, 2018.

Abstract

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research. Funding Agencies|Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]; U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internship (SULI) program; U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Visiting Faculty Program (VFP); United States-Israel Binational Science Foundation (BSF) [2009432]; Israel Science Foundation (ISF) [1122/14]; National Institute of General Medical Sciences [R01GM093123, GM083107, GM116960]; Purdue University start-up funds; Ralph W. and Grace M. Showalter Trust Award; Jim and Diann Robbers Cancer Research Grant for New Investigators Award; Brazilian agency: FAPESP; Brazilian agency: CAPES; Brazilian agency: CNPq; NIH [GM-14312]; NSF [MCB-10-19767]; National Institutes of Medicine [GM11574901]; Swedish Research Council [2012-5270, 2016-05369]; Swedish e-Science Research Center; Polish National Science Center [UMO-2013/10/M/ST4/00640]; IISc Mathematical Initiative Assistantship; National Academy of Sciences, India; National Institutes of Health [R01-GM100701, R01GM052032]; National Science Foundation; National Science Foundation Graduate Research Fellowship [DGE-1148900]; Princeton Institute for Computational Science and Engineering (PICSciE); Princeton University Office of Information Technology; UK Engineering and Physical Sciences Research Council [EP/M020576/1, EP/N031962/1]; National Research Foundation of Korea [2016R1A2A1A05005485]

Details

ISSN :
20452322
Database :
OpenAIRE
Journal :
Scientific reports, vol 8, iss 1, Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual), Universidade de São Paulo (USP), instacron:USP, Scientific Reports, Scientific Reports, Vol 8, Iss 1, Pp 1-18 (2018)
Accession number :
edsair.doi.dedup.....ea265f961e83f6d8e56d90a5eb07cede