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

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.

Citations

3

CrossRef
0

Web of Science
3

Scopus

Authors (2)

Emilia Lubecka dr hab. inż.
Adam Liwo

Cite as

Full text

download paper

downloaded 41 times

Publication version: Accepted or Published Version
DOI:: Digital Object Identifier (open in new tab) 10.1002/jcc.27003
License: Copyright (2022 Wiley Periodicals LLC.)

full content of the article see on external site open in new tab

Keywords

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

Details

Category:

Articles

Type:

artykuły w czasopismach

Published in:

JOURNAL OF COMPUTATIONAL CHEMISTRY no. 43, pages 2047 - 2059,
ISSN: 0192-8651

Language:

English

Publication year:

2022

Bibliographic description:

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: