AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design
dc.contributor.author
dc.date.accessioned
2023-10-27T07:09:14Z
dc.date.available
2023-10-27T07:09:14Z
dc.date.issued
2023-06-06
dc.identifier.issn
0002-7863
dc.identifier.uri
dc.description.abstract
The recent success of AlphaFold2 (AF2) and other deep learning (DL) tools in accurately predicting the folded three-dimensional (3D) structure of proteins and enzymes has revolutionized the structural biology and protein design fields. The 3D structure indeed reveals key information on the arrangement of the catalytic machinery of enzymes and which structural elements gate the active site pocket. However, comprehending enzymatic activity requires a detailed knowledge of the chemical steps involved along the catalytic cycle and the exploration of the multiple thermally accessible conformations that enzymes adopt when in solution. In this Perspective, some of the recent studies showing the potential of AF2 in elucidating the conformational landscape of enzymes are provided. Selected examples of the key developments of AF2-based and DL methods for protein design are discussed, as well as a few enzyme design cases. These studies show the potential of AF2 and DL for allowing the routine computational design of efficient enzymes
dc.description.sponsorship
We thank the Generalitat de Catalunya for the consolidated group TCBioSys (SGR 2021 00487) and grant projects PID2021-129034NB-I00 and PDC2022-133950-I00 funded by Spanish MICIN. S.O. is grateful for the funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (ERC-2015-StG-679001, ERC-2022-POC-101112805, and ERC-2022-CoG-101088032) and the Human Frontier Science Program (HFSP) for project grant RGP0054/2020. G.C. was supported by a research grant from ERC-StG (ERC-2015-StG-679001) and HFSP RGP0054/2020. C.D. was supported by the Spanish MINECO for a PhD fellowship (PRE2019-089147)
Open Access funding provided thanks to the CRUE-CSIC agreement with American Chemical Society (ACS)
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
American Chemical Society (ACS)
dc.relation
PID2021-129034NB-I00
PDC2022-133950-I00
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Reproducció digital del document publicat a: https://doi.org/10.1021/jacsau.3c00188
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Journal of the American Chemical Society (JACS), 2023, vol. 3, núm. 6, p. 1554-1562
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Articles publicats (D-Q)
dc.rights
Attribution 4.0 International
dc.rights.uri
dc.title
AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.relation.projectID
info:eu-repo/grantAgreement/EC/H2020/679001/EU/Network models for the computational design of proficient enzymes/NetMoDEzyme
info:eu-repo/grantAgreement/EC/HE/101112805/EU/Computational design of industrial enzymes for green chemistry/GREENZYME
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-129034NB-I00/ES/Diseño computacional de enzimas conformacionalmente dirigido para mejorar la actividad aislada o en complejo/
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PDC2022-133950-I00/ES/Evolución computacional de nuevos (bio)catalizadores/EVOCAT
info:eu-repo/grantAgreement/EC/HE/101088032/EU/Fast yet accurate routine rational design of novel enzymes/FASTEN
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
037286
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
dc.relation.FundingProgramme
dc.relation.ProjectAcronym
dc.identifier.eissn
1520-5126