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

Emilia Lubecka; Adam Liwo

doi:10.1002/jcc.27003

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

Abstrakt

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.

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Autorzy (2)

Emilia Lubecka dr hab. inż.
Adam Liwo

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Wersja publikacji: Accepted albo Published Version
DOI:: Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1002/jcc.27003
Licencja: Copyright (2022 Wiley Periodicals LLC.)

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Słowa kluczowe

AMBIGUOUS RESTRAINTS, COARSE-GRAINED MODELS, NMR-ASSISTED PROTEIN-STRUCTURE MODELING, UNRES

Informacje szczegółowe

Kategoria:

Publikacja w czasopiśmie

Typ:

artykuły w czasopismach

Opublikowano w:

JOURNAL OF COMPUTATIONAL CHEMISTRY nr 43, strony 2047 - 2059,
ISSN: 0192-8651

Język:

angielski

Rok wydania:

2022

Opis bibliograficzny:

Lubecka E., Liwo A.: A coarse‐grained approach to NMR ‐data‐assisted modeling of protein structures// JOURNAL OF COMPUTATIONAL CHEMISTRY -Vol. 43,iss. 31 (2022), s.2047-2059

DOI: