Prediction of protein structure with the coarse-grained UNRES force field assisted by small X-ray scattering data and knowledge-based information
Agnieszka Karczyńska , Magdalena Mozolewska , Paweł Krupa , Artur Giełdoń , Józef Adam Liwo , Cezary Czaplewski
AbstractA new approach to assisted protein–structure prediction has been proposed, which is based on running multiplexed replica exchange molecular dynamics simulations with the coarse-grained UNRES force field with restraints derived from knowledge-based models and distance distribution from small angle X-ray scattering (SAXS) measurements. The latter restraints are incorporated into the target function as a maximum-likelihood term that guides the shape of the simulated structures towards that defined by SAXS. The approach was first verified with the 1KOY protein, for which the distance distribution was calculated from the experimental structure, and subsequently used to predict the structures of 11 data-assisted targets in the CASP12 experiment. Major improvement of the GDT_TS was obtained for 2 targets, minor improvement for other 2 while, for 6 target GDT_TS deteriorated compared with that calculated for predictions without the SAXS data, partly because of assuming a wrong multimeric state (for Ts866) or because the crystal conformation was more compact than the solution conformation (for Ts942). Particularly good results were obtained for Ts909, in which use of SAXS data resulted in the selection of a correctly packed trimer and, subsequently, increased the GDT_TS of monomer prediction. It was found that running simulations with correct oligomeric state is essential for the success in SAXS-data-assisted prediction.
|Journal series||Proteins-Structure Function and Bioinformatics, ISSN 0887-3585, (A 30 pkt)|
|Publication size in sheets||0.55|
|Keywords in English||coarse-grained force fields, maximum likelihood principle, molecular dynamics, protein structure prediction, small-angle X-ray scattering|
|ASJC Classification||; ;|
|Score|| = 25.0, ArticleFromJournal|
= 30.0, ArticleFromJournal
|Publication indicators||= 4; : 2016 = 0.747; : 2017 = 2.274 (2) - 2017=2.328 (5)|
|Citation count*||4 (2019-04-19)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.