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ReFOLD3: refinement of 3D protein models with gradual restraints based on predicted local quality and residue contacts

Adiyaman, R. and McGuffin, L. ORCID: https://orcid.org/0000-0003-4501-4767 (2021) ReFOLD3: refinement of 3D protein models with gradual restraints based on predicted local quality and residue contacts. Nucleic Acids Research, 49 (W1). W589-W596. ISSN 1362-4962

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To link to this item DOI: 10.1093/nar/gkab300

Abstract/Summary

ReFOLD3 is unique in its application of gradual restraints, calculated from local model quality estimates and contact predictions, which are used to guide the refinement of theoretical 3D protein models towards the native structures. ReFOLD3 achieves improved performance by using an iterative refinement protocol to fix incorrect residue contacts and local errors, including unusual bonds and angles, which are identified in the submitted models by our leading ModFOLD8 model quality assessment method. Following refinement, the likely resulting improvements to the submitted models are recognized by ModFOLD8, which produces both global and local quality estimates. During the CASP14 prediction season (May–Aug 2020), we used the ReFOLD3 protocol to refine hundreds of 3D models, for both the refinement and the main tertiary structure prediction categories. Our group improved the global and local quality scores for numerous starting models in the refinement category, where we ranked in the top 10 according to the official assessment. The ReFOLD3 protocol was also used for the refinement of the SARS-CoV-2 targets as a part of the CASP Commons COVID-19 initiative, and we provided a significant number of the top 10 models. The ReFOLD3 web server is freely available at https://www.reading.ac.uk/bioinf/ReFOLD/.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Institute for Cardiovascular and Metabolic Research (ICMR)
Interdisciplinary centres and themes > Reading Systems Biology Network (RSBN)
Life Sciences > School of Biological Sciences > Biomedical Sciences
ID Code:97497
Publisher:Oxford University Press

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