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Energy landscapes for proteins described by the UNRES coarse-grained potential. | LitMetric

Energy landscapes for proteins described by the UNRES coarse-grained potential.

Biophys Chem

Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK. Electronic address:

Published: December 2023

AI Article Synopsis

  • * Coarse-grained models, like the UNres potential, can reduce this computational cost and have been integrated into Cambridge energy landscapes software for better efficiency.
  • * The UNRES model was tested on four proteins with varying secondary structures, showing promising results that align with experimental data and existing all-atom models, paving the way for future studies on larger biomolecules.

Article Abstract

The self-assembly of proteins is encoded in the underlying potential energy surface (PES), from which we can predict structure, dynamics, and thermodynamic properties. However, the corresponding analysis becomes increasingly challenging with larger protein sizes, due to the computational time required, which grows significantly with the number of atoms. Coarse-grained models offer an attractive approach to reduce the computational cost. In this Feature Article, we describe our implementation of the UNited RESidue (UNRES) coarse-grained potential in the Cambridge energy landscapes software. We have applied this framework to explore the energy landscapes of four proteins that exhibit native states involving different secondary structures. Here we have tested the ability of the UNRES potential to represent the global energy landscape of proteins containing up to 100 amino acid residues. The resulting potential energy landscapes exhibit good agreement with experiment, with low-lying minima close to the PDB geometries and to results obtained using the all-atom AMBER force field. The new program interfaces will allow us to investigate larger biomolecules in future work, using the UNRES potential in combination with all the methodology available in the computational energy landscapes framework.

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Source
http://dx.doi.org/10.1016/j.bpc.2023.107107DOI Listing

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