MAHOMES II: A webserver for predicting if a metal binding site is enzymatic.

ABSTRACT: Recent advances have enabled high-quality computationally generated structures for proteins with no solved crystal structures. However, protein function data remains largely limited to experimental methods and homology mapping. Since structure determines function, it is natural that methods capable of using computationally generated structures for functional annotations need to be advanced. Our laboratory recently developed a method to distinguish between metalloenzyme and non-enzyme sites. Here we report improvements to this method by upgrading our physicochemical features to alleviate the need for structures with sub-angstrom precision and using machine learning to reduce training data labeling error. Our improved classifier identifies protein bound metal sites as enzymatic or non-enzyma

SUBMITTER: Feehan R 

PROVIDER: S-EPMC10028950 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature