GEMME: a simple and fast global epistatic model predicting mutational effects.

ABSTRACT: The systematic and accurate description of protein mutational landscapes is a question of utmost importance in biology, bioengineering and medicine. Recent progress has been achieved by leveraging on the increasing wealth of genomic data and by modelling inter-site dependencies within biological sequences. However, state-of-the-art methods remain time consuming. Here, we present GEMME (www.lcqb.upmc.fr/GEMME), an original and fast method that predicts mutational outcomes by explicitly modelling the evolutionary history of natural sequences. This allows accounting for all positions in a sequence when estimating the effect of a given mutation. GEMME uses only a few biologically meaningful and interpretable parameters. Assessed against 50 high- and low-throughput mutational experiments, it ov

SUBMITTER: Laine E 

PROVIDER: S-EPMC6805226 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature