Reoptimized UNRES potential for protein model quality assessment
Eshel Faraggi , Paweł Krupa , Magdalena Mozolewska , Józef Adam Liwo , Andrzej Kloczkowski
AbstractRanking 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.
|Journal series||Genes, ISSN 2073-4425, (A 25 pkt)|
|Publication size in sheets||0.8|
|Keywords in English||protein energy; optimization; model ranking; UNRES; Seder; GENN; OUNRES|
|License||Journal (articles only); published final; ; with publication|
|Score||= 25.0, 24-07-2019, ArticleFromJournal|
|Publication indicators||: 2016 = 0.753; : 2017 = 3.191 (2) - 2017=3.286 (5)|
|Citation count*||1 (2019-08-22)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.