TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment.

ABSTRACT: Even with the significant advances of AlphaFold-Multimer (AF-Multimer) and AlphaFold3 (AF3) in protein complex structure prediction, their accuracy is still not comparable with monomer structure prediction. Efficient and effective quality assessment (QA) or estimation of model accuracy models that can evaluate the quality of the predicted protein-complexes without knowing their native structures are of key importance for protein structure generation and model selection. In this paper, we leverage persistent homology (PH) to capture the atomic-level topological information around residues and design a topological deep learning-based QA method, TopoQA, to assess the accuracy of protein complex interfaces. We integrate PH from topological data analysis into graph neural networks (GNNs) to cha

SUBMITTER: Han B 

PROVIDER: S-EPMC11891663 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature