Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation.

ABSTRACT: Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empiri

SUBMITTER: Berliner N 

PROVIDER: S-EPMC4170975 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature