Improving AlphaFold2-based Protein Tertiary Structure Prediction with MULTICOM in CASP15

Abstract

Since CASP14, AlphaFold2 has become the standard method for protein tertiary structure prediction. One remaining challenge in the field is to further improve the accuracy of AlphaFold2-based protein structure prediction. To address this challenge, we developed a new version of the MULTICOM system to sample diverse multiple sequence alignments (MSAs) and structural templates to improve the input for AlphaFold2 to generate structural models. The models are then ranked by both the pairwise model similarity and AlphaFold2 self-reported model quality score. The top ranked models are further refined by a novel structure alignment-based refinement method powered by Foldseek. Moreover, for a monomer target that is a subunit of a protein assembly (complex), MULTICOM integrates tertiary and quaternary structure prediction together to account for tertiary structural changes induced by protein-protein interaction in the assembly. The MULTICOM system participated in the tertiary structure prediction in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 as server and human predictors. Our best server predictor (MULTICOM_refine) ranked 3rd among 47 CASP15 server predictors and our best human predictor (MULTICOM) ranked 7th among all 132 human and server predictors. The average GDT-TS score and TM-score of the first structural models that MULTICOM_refine predicted for 94 CASP15 domains are [~]0.80 and [~]0.92, 9.6% and 8.2% and higher than [~]0.73 and 0.85 of the standard AlphaFold2 predictor respectively. The results demonstrate that our approach can significantly improve the accuracy of the AlphaFold2-based protein tertiary structure prediction. The source code of MULTICOM is available at: https://github.com/BioinfoMachineLearning/MULTICOM3.