Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks
A new program, TALOS-N, is introduced for predicting protein backbone torsion angles from NMR chemical shifts. The program relies far more extensively on the use of trained artificial neural networks than its predecessor, TALOS+. Validation on an independent set of proteins indicates that backbone t...
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| Veröffentlicht in: | Journal of biomolecular NMR 2013-07, Vol.56 (3), p.227-241 |
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| container_title | Journal of biomolecular NMR |
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| creator | Shen, Yang Bax, Ad |
| description | A new program, TALOS-N, is introduced for predicting protein backbone torsion angles from NMR chemical shifts. The program relies far more extensively on the use of trained artificial neural networks than its predecessor, TALOS+. Validation on an independent set of proteins indicates that backbone torsion angles can be predicted for a larger, ≥90 % fraction of the residues, with an error rate smaller than ca 3.5 %, using an acceptance criterion that is nearly two-fold tighter than that used previously, and a root mean square difference between predicted and crystallographically observed (
ϕ
,
ψ
) torsion angles of ca 12º. TALOS-N also reports sidechain χ
1
rotameric states for about 50 % of the residues, and a consistency with reference structures of 89 %. The program includes a neural network trained to identify secondary structure from residue sequence and chemical shifts. |
| doi_str_mv | 10.1007/s10858-013-9741-y |
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ϕ
,
ψ
) torsion angles of ca 12º. TALOS-N also reports sidechain χ
1
rotameric states for about 50 % of the residues, and a consistency with reference structures of 89 %. The program includes a neural network trained to identify secondary structure from residue sequence and chemical shifts.</description><identifier>ISSN: 0925-2738</identifier><identifier>EISSN: 1573-5001</identifier><identifier>DOI: 10.1007/s10858-013-9741-y</identifier><identifier>PMID: 23728592</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Algorithms ; Biochemistry ; Biological and Medical Physics ; Biophysics ; Models, Molecular ; Neural Networks (Computer) ; Nuclear Magnetic Resonance, Biomolecular ; Physics ; Physics and Astronomy ; Protein Conformation ; Proteins - chemistry ; Reproducibility of Results ; Software ; Spectroscopy/Spectrometry</subject><ispartof>Journal of biomolecular NMR, 2013-07, Vol.56 (3), p.227-241</ispartof><rights>Springer Science+Business Media Dordrecht (outside the USA) 2013</rights><rights>Springer Science+Business Media Dordrecht 2013</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c617t-f3b9b5c232d018152c239c36f3aad5281f75ff89b200ed4d5ee338c734164af43</citedby><cites>FETCH-LOGICAL-c617t-f3b9b5c232d018152c239c36f3aad5281f75ff89b200ed4d5ee338c734164af43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10858-013-9741-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10858-013-9741-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,776,780,881,27903,27904,41467,42536,51298</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23728592$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shen, Yang</creatorcontrib><creatorcontrib>Bax, Ad</creatorcontrib><title>Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks</title><title>Journal of biomolecular NMR</title><addtitle>J Biomol NMR</addtitle><addtitle>J Biomol NMR</addtitle><description>A new program, TALOS-N, is introduced for predicting protein backbone torsion angles from NMR chemical shifts. The program relies far more extensively on the use of trained artificial neural networks than its predecessor, TALOS+. Validation on an independent set of proteins indicates that backbone torsion angles can be predicted for a larger, ≥90 % fraction of the residues, with an error rate smaller than ca 3.5 %, using an acceptance criterion that is nearly two-fold tighter than that used previously, and a root mean square difference between predicted and crystallographically observed (
ϕ
,
ψ
) torsion angles of ca 12º. TALOS-N also reports sidechain χ
1
rotameric states for about 50 % of the residues, and a consistency with reference structures of 89 %. 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The program relies far more extensively on the use of trained artificial neural networks than its predecessor, TALOS+. Validation on an independent set of proteins indicates that backbone torsion angles can be predicted for a larger, ≥90 % fraction of the residues, with an error rate smaller than ca 3.5 %, using an acceptance criterion that is nearly two-fold tighter than that used previously, and a root mean square difference between predicted and crystallographically observed (
ϕ
,
ψ
) torsion angles of ca 12º. TALOS-N also reports sidechain χ
1
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| subjects | Algorithms Biochemistry Biological and Medical Physics Biophysics Models, Molecular Neural Networks (Computer) Nuclear Magnetic Resonance, Biomolecular Physics Physics and Astronomy Protein Conformation Proteins - chemistry Reproducibility of Results Software Spectroscopy/Spectrometry |
| title | Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks |
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