Evaluating protein binding interfaces with PUMBA

• URL: http://arxiv.org/abs/2510.16674v1
• Date: Sun, 19 Oct 2025 00:34:07 GMT
• Title: Evaluating protein binding interfaces with PUMBA
• Abstract summary: We introduce PUMBA, which improves PIsToN by replacing its Vision Transformer backbone with Vision Mamba.<n>As a result, the model's ability to capture both global and local patterns in protein-protein interface features is significantly improved.
• Score: 1.089614199781423
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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