Decoy selection for protein structure prediction via extreme gradient boosting and ranking.

ABSTRACT:

Background

Identifying one or more biologically-active/native decoys from millions of non-native decoys is one of the major challenges in computational structural biology. The extreme lack of balance in positive and negative samples (native and non-native decoys) in a decoy set makes the problem even more complicated. Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity. Recent investigations into energy landscape-based decoy selection approaches show promises. However, lack of generalization over varied test cases remains a bottleneck for these methods.

Results

We propose a novel decoy selection method, ML-Select, a machine

SUBMITTER: Akhter N 

PROVIDER: S-EPMC7724862 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature