High-accuracy protein complex structure modeling based on sequence-derived structure complementarity

Abstract In living organisms, proteins perform key functions required for life activities by interacting to form complexes. Determining the protein complex structure is crucial for understanding and mastering biological functions. Although AlphaFold2 has made a revolutionary breakthrough in predicting protein monomeric structures, accurately capturing inter-chain interaction signals and modeling the structures of protein complexes remain a formidable challenge. In this work, we report DeepSCFold, a pipeline for improving protein complex structure modeling. DeepSCFold uses sequence-based deep learning models to predict protein-protein structural similarity and interaction probability, providing a foundation for identifying interaction partners and constructing deep paired multiple-sequence alignments (MSAs) for protein complex structure prediction. Benchmark results show that DeepSCFold significantly increases the accuracy of protein complex structure prediction compared with state-of-the-art methods. For multimer targets from CASP15, DeepSCFold achieves an improvement of 11.6% and 10.3% in TM-score compared to AlphaFold-Multimer and AlphaFold3, respectively. Furthermore, when applied to antibody-antigen complexes from the SAbDab database, DeepSCFold enhances the prediction success rate for antibody-antigen binding interfaces by 24.7% and 12.4% over AlphaFold-Multimer and AlphaFold3, respectively. These results demonstrate that DeepSCFold effectively captures intrinsic and conserved protein-protein interaction patterns through sequence-derived structure-aware information, rather than relying solely on sequence-level co-evolutionary signals.