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A coarse‐grained approach to NMR‐data‐assisted modeling of protein structures.

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Title: A coarse‐grained approach to NMR‐data‐assisted modeling of protein structures.
Authors: Lubecka, Emilia A.1 (AUTHOR) emilubec@eti.pg.edu.pl, Liwo, Adam2 (AUTHOR)
Source: Journal of Computational Chemistry. 12/5/2022, Vol. 43 Issue 31, p2047-2059. 13p.
Subjects: Protein structure, Protein models, Flexible structures, Molecular dynamics
Abstract: The ESCASA algorithm for analytical estimation of proton positions from coarse‐grained geometry developed in our recent work has been implemented in modeling protein structures with the highly coarse‐grained UNRES model of polypeptide chains (two sites per residue) and nuclear magnetic resonance (NMR) data. A penalty function with the shape of intersecting gorges was applied to treat ambiguous distance restraints, which automatically selects consistent restraints. Hamiltonian replica exchange molecular dynamics was used to carry out the conformational search. The method was tested with both unambiguous and ambiguous restraints producing good‐quality models with GDT_TS from 7.4 units higher to 14.4 units lower than those obtained with the CYANA or MELD software for protein‐structure determination from NMR data at the all‐atom resolution. The method can thus be applied in modeling the structures of flexible proteins, for which extensive conformational search enabled by coarse‐graining is more important than high modeling accuracy. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computational Chemistry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 159652856
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A coarse‐grained approach to NMR‐data‐assisted modeling of protein structures.
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  Data: <searchLink fieldCode="AR" term="%22Lubecka%2C+Emilia+A%2E%22">Lubecka, Emilia A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> emilubec@eti.pg.edu.pl</i><br /><searchLink fieldCode="AR" term="%22Liwo%2C+Adam%22">Liwo, Adam</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Computational+Chemistry%22">Journal of Computational Chemistry</searchLink>. 12/5/2022, Vol. 43 Issue 31, p2047-2059. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Protein+structure%22">Protein structure</searchLink><br /><searchLink fieldCode="DE" term="%22Protein+models%22">Protein models</searchLink><br /><searchLink fieldCode="DE" term="%22Flexible+structures%22">Flexible structures</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+dynamics%22">Molecular dynamics</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: The ESCASA algorithm for analytical estimation of proton positions from coarse‐grained geometry developed in our recent work has been implemented in modeling protein structures with the highly coarse‐grained UNRES model of polypeptide chains (two sites per residue) and nuclear magnetic resonance (NMR) data. A penalty function with the shape of intersecting gorges was applied to treat ambiguous distance restraints, which automatically selects consistent restraints. Hamiltonian replica exchange molecular dynamics was used to carry out the conformational search. The method was tested with both unambiguous and ambiguous restraints producing good‐quality models with GDT_TS from 7.4 units higher to 14.4 units lower than those obtained with the CYANA or MELD software for protein‐structure determination from NMR data at the all‐atom resolution. The method can thus be applied in modeling the structures of flexible proteins, for which extensive conformational search enabled by coarse‐graining is more important than high modeling accuracy. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Computational Chemistry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1002/jcc.27003
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 13
        StartPage: 2047
    Subjects:
      – SubjectFull: Protein structure
        Type: general
      – SubjectFull: Protein models
        Type: general
      – SubjectFull: Flexible structures
        Type: general
      – SubjectFull: Molecular dynamics
        Type: general
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      – TitleFull: A coarse‐grained approach to NMR‐data‐assisted modeling of protein structures.
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            NameFull: Lubecka, Emilia A.
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            NameFull: Liwo, Adam
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            – D: 05
              M: 12
              Text: 12/5/2022
              Type: published
              Y: 2022
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              Value: 43
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              Value: 31
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            – TitleFull: Journal of Computational Chemistry
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