Authors
Minglei Dong, Dongjiang Niu, Yuanxing Peng, Hongle Li, Minghao Li, Zhiqiang Wei, Zhen Li
Published in
Molecular diversity. Aug 30, 2026. Epub Aug 30, 2026.
Abstract
Accurate identification of protein-small molecule binding sites is a fundamental problem in computational biology and drug discovery. Existing sequence-based methods lack explicit spatial awareness, while structure-based approaches often struggle to integrate long-range functional dependencies and semantic information, leading to limited generalization on low-similarity or sparsely annotated proteins. To address these challenges, we propose DSC-BSite, a dynamic-static collaborative multimodal graph learning framework for residue-level binding site prediction. First, a Static Global Sequence Encoding module captures multi-scale local patterns and long-range contextual dependencies from protein sequences. Second, a Gated Dual-Graph Dynamic Propagation (GDDP) module jointly models spatial geometric interactions and sequence-derived functional correlations using a dynamic spatial graph and an attention-guided sequence graph, enabling adaptive residue interaction modeling. Third, a PPI-guided Structural-Semantic Alignment (PSSA) pre-training strategy aligns structural representations with function-aware semantic embeddings, enhancing the biological expressiveness of structural features without requiring PPI information during inference. Experimental results on the UniProtSMB and SJC benchmark datasets demonstrate that DSC-BSite achieves competitive performance across multiple evaluation metrics, with particularly strong results in Recall on UniProtSMB and Precision and MCC on SJC.
PMID:
42669106
Bibliographic data and abstract were imported from PubMed on 31 Aug 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 5
- Comments 0