Predicting residue-specific qualities of individual protein models using residual neural networks and graph neural networks.

ABSTRACT: The estimation of protein model accuracy (EMA) or model quality assessment (QA) is important for protein structure prediction. An accurate EMA algorithm can guide the refinement of models or pick the best model or best parts of models from a pool of predicted tertiary structures. We developed two novel methods: MASS2 and LAW, for predicting residue-specific or local qualities of individual models, which incorporate residual neural networks and graph neural networks, respectively. These two methods use similar features extracted from protein models but different architectures of neural networks to predict the local accuracies of single models. MASS2 and LAW participated in the QA category of CASP14, and according to our evaluations based on CASP14 official criteria, MASS2 and LAW are the be

SUBMITTER: Zhao C 

PROVIDER: S-EPMC9796650 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature