{"ID":2853036,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.16674","arxiv_id":"2510.16674","title":"Evaluating protein binding interfaces with PUMBA","abstract":"Protein-protein docking tools help in studying interactions between proteins, and are essential for drug, vaccine, and therapeutic development. However, the accuracy of a docking tool depends on a robust scoring function that can reliably differentiate between native and non-native complexes. PIsToN is a state-of-the-art deep learning-based scoring function that uses Vision Transformers in its architecture. Recently, the Mamba architecture has demonstrated exceptional performance in both natural language processing and computer vision, often outperforming Transformer-based models in their domains. In this study, we introduce PUMBA (Protein-protein interface evaluation with Vision Mamba), which improves PIsToN by replacing its Vision Transformer backbone with Vision Mamba. This change allows us to leverage Mamba's efficient long-range sequence modeling for sequences of image patches. As a result, the model's ability to capture both global and local patterns in protein-protein interface features is significantly improved. Evaluation on several widely-used, large-scale public datasets demonstrates that PUMBA consistently outperforms its original Transformer-based predecessor, PIsToN.","short_abstract":"Protein-protein docking tools help in studying interactions between proteins, and are essential for drug, vaccine, and therapeutic development. However, the accuracy of a docking tool depends on a robust scoring function that can reliably differentiate between native and non-native complexes. PIsToN is a state-of-the-a...","url_abs":"https://arxiv.org/abs/2510.16674","url_pdf":"https://arxiv.org/pdf/2510.16674v1","authors":"[\"Azam Shirali\",\"Giri Narasimhan\"]","published":"2025-10-19T00:34:07Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"q-bio.QM\"]","methods":"[\"Vision Transformer\",\"Transformer\"]","has_code":false}