Ranking near-native candidate protein structures via random forest classification.

ABSTRACT:

Background

In ab initio protein-structure predictions, a large set of structural decoys are often generated, with the requirement to select best five or three candidates from the decoys. The clustered central structures with the most number of neighbors are frequently regarded as the near-native protein structures with the lowest free energy; however, limitations in clustering methods and three-dimensional structural-distance assessments make identifying exact order of the best five or three near-native candidate structures difficult.

Results

To address this issue, we propose a method that re-ranks the candidate structures via random forest classification using intra- and inter-cluster features from the results of the clustering. Comparative analysis indicated that our metho

SUBMITTER: Wu H 

PROVIDER: S-EPMC6929337 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature