Homology modeling in the time of collective and artificial intelligence

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dc.contributor.authorHameduh, Tareqcs
dc.contributor.authorHaddad, Yazan Abdulmajeed Eyadhcs
dc.contributor.authorAdam, Vojtěchcs
dc.contributor.authorHeger, Zbyněkcs
dc.coverage.issue1cs
dc.coverage.volume18cs
dc.date.accessioned2021-04-01T10:54:32Z
dc.date.available2021-04-01T10:54:32Z
dc.date.issued2021-02-01cs
dc.description.abstractHomology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.en
dc.formattextcs
dc.format.extent3494-3506cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationComputational and Structural Biotechnology Journal. 2021, vol. 18, issue 1, p. 3494-3506.en
dc.identifier.doi10.1016/j.csbj.2020.11.007cs
dc.identifier.issn2001-0370cs
dc.identifier.other168894cs
dc.identifier.urihttp://hdl.handle.net/11012/196490
dc.language.isoencs
dc.publisherElseviercs
dc.relation"European Union (EU)" & "Horizon 2020"
dc.relation.ispartofComputational and Structural Biotechnology Journalcs
dc.relation.projectIdinfo:eu-repo/grantAgreement/EC/H2020/759585/EU//ToMeTuM
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S2001037020304748cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2001-0370/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectHomology modelingen
dc.subjectMachine learningen
dc.subjectProtein 3D structureen
dc.subjectStructural bioinformaticsen
dc.subjectCollective intelligenceen
dc.subjectArtificial intelligenceen
dc.titleHomology modeling in the time of collective and artificial intelligenceen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-168894en
sync.item.dbtypeVAVen
sync.item.insts2021.04.01 12:54:32en
sync.item.modts2021.04.01 12:14:22en
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Chytré nanonástrojecs
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