Foldy: An open-source web application for interactive protein structure analysis
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Published:2024-02-02
Issue:2
Volume:20
Page:e1011171
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ISSN:1553-7358
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Container-title:PLOS Computational Biology
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language:en
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Short-container-title:PLoS Comput Biol
Author:
Roberts Jacob B.ORCID,
Nava Alberto A.ORCID,
Pearson Allison N.ORCID,
Incha Matthew R.,
Valencia Luis E.,
Ma Melody,
Rao Abhay,
Keasling Jay D.ORCID
Abstract
Foldy is a cloud-based application that allows non-computational biologists to easily utilize advanced AI-based structural biology tools, including AlphaFold and DiffDock. With many deployment options, it can be employed by individuals, labs, universities, and companies in the cloud without requiring hardware resources, but it can also be configured to utilize locally available computers. Foldy enables scientists to predict the structure of proteins and complexes up to 6000 amino acids with AlphaFold, visualize Pfam annotations, and dock ligands with AutoDock Vina and DiffDock.
In our manuscript, we detail Foldy’s interface design, deployment strategies, and optimization for various user scenarios. We demonstrate its application through case studies including rational enzyme design and analyzing proteins with domains of unknown function. Furthermore, we compare Foldy’s interface and management capabilities with other open and closed source tools in the field, illustrating its practicality in managing complex data and computation tasks. Our manuscript underlines the benefits of Foldy as a day-to-day tool for life science researchers, and shows how Foldy can make modern tools more accessible and efficient.
Funder
Lawrence Berkeley National Laboratory
National Defense Science and Engineering Graduate
National Science Foundation
Publisher
Public Library of Science (PLoS)
Subject
Computational Theory and Mathematics,Cellular and Molecular Neuroscience,Genetics,Molecular Biology,Ecology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics
Reference20 articles.
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5. Scaffolding protein functional sites using deep learning;J Wang;Science,2022