RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold.

ABSTRACT: While template-free protein structure prediction protocols now produce good quality models for many targets, modelling failure remains common. For these methods to be useful it is important that users can both choose the best model from the hundreds to thousands of models that are commonly generated for a target, and determine whether this model is likely to be correct. We have developed Random Forest Quality Assessment (RFQAmodel), which assesses whether models produced by a protein structure prediction pipeline have the correct fold. RFQAmodel uses a combination of existing quality assessment scores with two predicted contact map alignment scores. These alignment scores are able to identify correct models for targets that are not otherwise captured. Our classifier was trained on a large

SUBMITTER: West CE 

PROVIDER: S-EPMC6802825 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature