PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries.

ABSTRACT: Accurate modeling of protein-protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations. Here we present PIQLE, a deep graph learning method for protein-protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of the individual interactions between the interfacial residues using a multihead graph attention

SUBMITTER: Shuvo MH 

PROVIDER: S-EPMC9949034 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature