A coarse‐grained approach to NMR‐data‐assisted modeling of protein structures. Issue 31 (22nd September 2022)

• Record Type: Journal Article
• Authors: Lubecka, Emilia A.; Liwo, Adam
• Is Part Of: Journal of computational chemistry. Volume 43:Issue 31(2022)
• Page Start: 2047
• Page End: 2059
• Publication Date: 2022-09-22
• Subjects: ambiguous restraints -- coarse‐grained models -- NMR‐assisted protein‐structure modeling -- UNRES; Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.27003
• Languages: English
• ISSNs: 0192-8651

Abstract

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 : NMR‐data assisted modeling of protein structures with the UNRES coarse‐grained force field. The positions of the protons are estimated from coarse‐grained geometry by the ESCASA algorithm. Ambiguous restraints are handled through the introduction of a penalty function which has the form of intersecting gorges and takes nearly the same value regardless of whether one only or more components of an ambiguous restraint are satisfied.