A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces.

ABSTRACT: Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with differ

SUBMITTER: Melo R 

PROVIDER: S-EPMC5000613 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature