A machine learning approach for ranking clusters of docked protein-protein complexes by pairwise cluster comparison.

ABSTRACT: Reliable identification of near-native poses of docked protein-protein complexes is still an unsolved problem. The intrinsic heterogeneity of protein-protein interactions is challenging for traditional biophysical or knowledge based potentials and the identification of many false positive binding sites is not unusual. Often, ranking protocols are based on initial clustering of docked poses followed by the application of an energy function to rank each cluster according to its lowest energy member. Here, we present an approach of cluster ranking based not only on one molecular descriptor (e.g., an energy function) but also employing a large number of descriptors that are integrated in a machine learning model, whereby, an extremely randomized tree classifier based on 109 molecular descripto

SUBMITTER: Pfeiffenberger E 

PROVIDER: S-EPMC5396268 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature