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Genes 2018, 9(12), 601; https://doi.org/10.3390/genes9120601

Reoptimized UNRES Potential for Protein Model Quality Assessment

1
Research and Information Systems, LLC, Indianapolis, IN 46240, USA
2
Department of Physics, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA
3
Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH 43215, USA
4
Institute of Physics, Polish Academy of Sciences, Al. Lotnikow 32/46, PL-02-668 Warsaw, Poland
5
Institute of Computer Science, Polish Academy of Sciences, ul. Jana Kazimierza 5, 01-248 Warszawa, Poland
6
Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland
7
Center for In Silico Protein Structure and School of Computational Sciences, Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu, Seoul 130-722, Korea
8
Department of Pediatrics, The Ohio State University, Columbus, OH 43215, USA
9
Kavli Institute for Theoretical Physics China, Chinese Academy of Sciences, Beijing 100190, China
10
Future Value Creation Research Center, Graduate School of Informatics, Nagoya University, Nagoya 464-8601, Japan
*
Author to whom correspondence should be addressed.
Received: 15 October 2018 / Revised: 25 November 2018 / Accepted: 27 November 2018 / Published: 3 December 2018
(This article belongs to the Special Issue Novel Approaches in Protein Structure Prediction)
PDF [1112 KB, uploaded 11 December 2018]

Abstract

Ranking protein structure models is an elusive problem in bioinformatics. These models are evaluated on both the degree of similarity to the native structure and the folding pathway. Here, we simulated the use of the coarse-grained UNited RESidue (UNRES) force field as a tool to choose the best protein structure models for a given protein sequence among a pool of candidate models, using server data from the CASP11 experiment. Because the original UNRES was optimized for Molecular Dynamics simulations, we reoptimized UNRES using a deep feed-forward neural network, and we show that introducing additional descriptive features can produce better results. Overall, we found that the reoptimized UNRES performs better in selecting the best structures and tracking protein unwinding from its native state. We also found a relatively poor correlation between UNRES values and the model’s Template Modeling Score (TMS). This is remedied by reoptimization. We discuss some cases where our reoptimization procedure is useful. The reoptimized version of UNRES (OUNRES) is available at http://mamiris.com and http://www.unres.pl.
Keywords: protein energy; optimization; model ranking; UNRES; Seder; GENN; OUNRES protein energy; optimization; model ranking; UNRES; Seder; GENN; OUNRES
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Faraggi, E.; Krupa, P.; Mozolewska, M.A.; Liwo, A.; Kloczkowski, A. Reoptimized UNRES Potential for Protein Model Quality Assessment. Genes 2018, 9, 601.

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