The Impact of AI-Based Modeling on the Accuracy of Protein Assembly Prediction: Insights from CASP15.

ABSTRACT: In CASP15, 87 predictors submitted around 11,000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact prediction, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcompl

SUBMITTER: Ozden B 

PROVIDER: S-EPMC10369898 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature