Download PDFOpen PDF in browserEvaluating Protein Binding Interfaces With PUMBA10 pages•Published: April 19, 2026AbstractProtein–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-basedmodels 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. Keyphrases: protein binding interfaces prediction, scoring functions, vision mamba In: Jernej Masnec, Hamid Reza Karimian, Parisa Kordjamshidi and Yan Li (editors). Proceedings of AI for Accelerated Research Symposium, vol 3, pages 177-186.
|
