Machine learning in protein structure prediction

Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases. While progress has historically ebbed and flowed, the past two years saw dramatic advances driven by the i...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Current opinion in chemical biology 2021-12, Vol.65, p.1-8
1. Verfasser: AlQuraishi, Mohammed
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 8
container_issue
container_start_page 1
container_title Current opinion in chemical biology
container_volume 65
creator AlQuraishi, Mohammed
description Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases. While progress has historically ebbed and flowed, the past two years saw dramatic advances driven by the increasing “neuralization” of structure prediction pipelines, whereby computations previously based on energy models and sampling procedures are replaced by neural networks. The extraction of physical contacts from the evolutionary record; the distillation of sequence–structure patterns from known structures; the incorporation of templates from homologs in the Protein Databank; and the refinement of coarsely predicted structures into finely resolved ones have all been reformulated using neural networks. Cumulatively, this transformation has resulted in algorithms that can now predict single protein domains with a median accuracy of 2.1 Å, setting the stage for a foundational reconfiguration of the role of biomolecular modeling within the life sciences.
doi_str_mv 10.1016/j.cbpa.2021.04.005
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2531215037</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S1367593121000508</els_id><sourcerecordid>2531215037</sourcerecordid><originalsourceid>FETCH-LOGICAL-c515t-348652dde28b68a49560c59b526ce9e075ea96ee028e4ea7e466a1ac829572273</originalsourceid><addsrcrecordid>eNp9kM1OwzAQhC0EoqXwAhxQj1wS1o7t2BIXVJUfqYgLnC3H2YKrNCl2gsTb46iFI6dZrWZGux8hlxRyClTebHJX7WzOgNEceA4gjsiUqlJnwIEdp7mQZSZ0QSfkLMYNAEimxCmZFByoKLmeEni27sO3OG_Qhta373Pfzneh6zFp7MPg-iFg2mDtXe-79pycrG0T8eKgM_J2v3xdPGarl4enxd0qc4KKPiu4koLVNTJVSWW5FhKc0JVg0qFGKAVaLRGBKeRoS-RSWmqdYlqUjJXFjFzve9MxnwPG3mx9dNg0tsVuiIaJgjIqoBitbG91oYsx4Nrsgt_a8G0omJGU2ZiRlBlJGeAmkUqhq0P_UG2x_ov8okmG270B05dfHoOJzmPrEoiArjd15__r_wHHbXho</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2531215037</pqid></control><display><type>article</type><title>Machine learning in protein structure prediction</title><source>MEDLINE</source><source>Access via ScienceDirect (Elsevier)</source><creator>AlQuraishi, Mohammed</creator><creatorcontrib>AlQuraishi, Mohammed</creatorcontrib><description>Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases. While progress has historically ebbed and flowed, the past two years saw dramatic advances driven by the increasing “neuralization” of structure prediction pipelines, whereby computations previously based on energy models and sampling procedures are replaced by neural networks. The extraction of physical contacts from the evolutionary record; the distillation of sequence–structure patterns from known structures; the incorporation of templates from homologs in the Protein Databank; and the refinement of coarsely predicted structures into finely resolved ones have all been reformulated using neural networks. Cumulatively, this transformation has resulted in algorithms that can now predict single protein domains with a median accuracy of 2.1 Å, setting the stage for a foundational reconfiguration of the role of biomolecular modeling within the life sciences.</description><identifier>ISSN: 1367-5931</identifier><identifier>EISSN: 1879-0402</identifier><identifier>DOI: 10.1016/j.cbpa.2021.04.005</identifier><identifier>PMID: 34015749</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Algorithms ; Alphafold ; Biophysics ; Computational Biology - methods ; Databases, Protein ; Deep learning ; Machine Learning ; Protein Conformation ; Protein design ; Protein Folding ; Protein modeling ; Protein structure ; Protein structure prediction ; Proteins - chemistry</subject><ispartof>Current opinion in chemical biology, 2021-12, Vol.65, p.1-8</ispartof><rights>2021 The Author(s)</rights><rights>Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c515t-348652dde28b68a49560c59b526ce9e075ea96ee028e4ea7e466a1ac829572273</citedby><cites>FETCH-LOGICAL-c515t-348652dde28b68a49560c59b526ce9e075ea96ee028e4ea7e466a1ac829572273</cites><orcidid>0000-0001-6817-1322</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.cbpa.2021.04.005$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34015749$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>AlQuraishi, Mohammed</creatorcontrib><title>Machine learning in protein structure prediction</title><title>Current opinion in chemical biology</title><addtitle>Curr Opin Chem Biol</addtitle><description>Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases. While progress has historically ebbed and flowed, the past two years saw dramatic advances driven by the increasing “neuralization” of structure prediction pipelines, whereby computations previously based on energy models and sampling procedures are replaced by neural networks. The extraction of physical contacts from the evolutionary record; the distillation of sequence–structure patterns from known structures; the incorporation of templates from homologs in the Protein Databank; and the refinement of coarsely predicted structures into finely resolved ones have all been reformulated using neural networks. Cumulatively, this transformation has resulted in algorithms that can now predict single protein domains with a median accuracy of 2.1 Å, setting the stage for a foundational reconfiguration of the role of biomolecular modeling within the life sciences.</description><subject>Algorithms</subject><subject>Alphafold</subject><subject>Biophysics</subject><subject>Computational Biology - methods</subject><subject>Databases, Protein</subject><subject>Deep learning</subject><subject>Machine Learning</subject><subject>Protein Conformation</subject><subject>Protein design</subject><subject>Protein Folding</subject><subject>Protein modeling</subject><subject>Protein structure</subject><subject>Protein structure prediction</subject><subject>Proteins - chemistry</subject><issn>1367-5931</issn><issn>1879-0402</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kM1OwzAQhC0EoqXwAhxQj1wS1o7t2BIXVJUfqYgLnC3H2YKrNCl2gsTb46iFI6dZrWZGux8hlxRyClTebHJX7WzOgNEceA4gjsiUqlJnwIEdp7mQZSZ0QSfkLMYNAEimxCmZFByoKLmeEni27sO3OG_Qhta373Pfzneh6zFp7MPg-iFg2mDtXe-79pycrG0T8eKgM_J2v3xdPGarl4enxd0qc4KKPiu4koLVNTJVSWW5FhKc0JVg0qFGKAVaLRGBKeRoS-RSWmqdYlqUjJXFjFzve9MxnwPG3mx9dNg0tsVuiIaJgjIqoBitbG91oYsx4Nrsgt_a8G0omJGU2ZiRlBlJGeAmkUqhq0P_UG2x_ov8okmG270B05dfHoOJzmPrEoiArjd15__r_wHHbXho</recordid><startdate>202112</startdate><enddate>202112</enddate><creator>AlQuraishi, Mohammed</creator><general>Elsevier Ltd</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-6817-1322</orcidid></search><sort><creationdate>202112</creationdate><title>Machine learning in protein structure prediction</title><author>AlQuraishi, Mohammed</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c515t-348652dde28b68a49560c59b526ce9e075ea96ee028e4ea7e466a1ac829572273</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Alphafold</topic><topic>Biophysics</topic><topic>Computational Biology - methods</topic><topic>Databases, Protein</topic><topic>Deep learning</topic><topic>Machine Learning</topic><topic>Protein Conformation</topic><topic>Protein design</topic><topic>Protein Folding</topic><topic>Protein modeling</topic><topic>Protein structure</topic><topic>Protein structure prediction</topic><topic>Proteins - chemistry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>AlQuraishi, Mohammed</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Current opinion in chemical biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>AlQuraishi, Mohammed</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Machine learning in protein structure prediction</atitle><jtitle>Current opinion in chemical biology</jtitle><addtitle>Curr Opin Chem Biol</addtitle><date>2021-12</date><risdate>2021</risdate><volume>65</volume><spage>1</spage><epage>8</epage><pages>1-8</pages><issn>1367-5931</issn><eissn>1879-0402</eissn><abstract>Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases. While progress has historically ebbed and flowed, the past two years saw dramatic advances driven by the increasing “neuralization” of structure prediction pipelines, whereby computations previously based on energy models and sampling procedures are replaced by neural networks. The extraction of physical contacts from the evolutionary record; the distillation of sequence–structure patterns from known structures; the incorporation of templates from homologs in the Protein Databank; and the refinement of coarsely predicted structures into finely resolved ones have all been reformulated using neural networks. Cumulatively, this transformation has resulted in algorithms that can now predict single protein domains with a median accuracy of 2.1 Å, setting the stage for a foundational reconfiguration of the role of biomolecular modeling within the life sciences.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>34015749</pmid><doi>10.1016/j.cbpa.2021.04.005</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-6817-1322</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1367-5931
ispartof Current opinion in chemical biology, 2021-12, Vol.65, p.1-8
issn 1367-5931
1879-0402
language eng
recordid cdi_proquest_miscellaneous_2531215037
source MEDLINE; Access via ScienceDirect (Elsevier)
subjects Algorithms
Alphafold
Biophysics
Computational Biology - methods
Databases, Protein
Deep learning
Machine Learning
Protein Conformation
Protein design
Protein Folding
Protein modeling
Protein structure
Protein structure prediction
Proteins - chemistry
title Machine learning in protein structure prediction
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T14%3A36%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Machine%20learning%20in%20protein%20structure%20prediction&rft.jtitle=Current%20opinion%20in%20chemical%20biology&rft.au=AlQuraishi,%20Mohammed&rft.date=2021-12&rft.volume=65&rft.spage=1&rft.epage=8&rft.pages=1-8&rft.issn=1367-5931&rft.eissn=1879-0402&rft_id=info:doi/10.1016/j.cbpa.2021.04.005&rft_dat=%3Cproquest_cross%3E2531215037%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2531215037&rft_id=info:pmid/34015749&rft_els_id=S1367593121000508&rfr_iscdi=true