ISEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations.

ABSTRACT: Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution, and Energy-based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kc

SUBMITTER: Geng C 

PROVIDER: S-EPMC6587874 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature