用Mamba替代Transformer，提升蛋白质对接界面评估精度。
Evaluating protein binding interfaces with PUMBA
- 以Vision Mamba替代原模型的Vision Transformer，增强序列建模能力。
- 在多个大型公开数据集上表现优于原版PIsToN，尤其在长距离依赖捕捉上更优。
- 适合蛋白质相互作用研究、药物设计领域的算法开发者使用。
蛋白质-蛋白质对接工具在药物、疫苗和治疗开发中至关重要，但其准确性依赖于能可靠区分天然与非天然复合物的评分函数。PIsToN是基于深度学习的先进评分函数，采用视觉变换器架构。近期，Mamba架构在自然语言处理和计算机视觉领域表现出色，常优于传统Transformer模型。本研究提出PUMBA（Protein-protein interface evaluation with Vision Mamba），通过将PIsToN的视觉变换器主干替换为视觉Mamba，利用Mamba在图像块序列上的高效长程建模能力，显著提升对蛋白质-蛋白质界面特征中全局与局部模式的捕捉能力。在多个广泛使用的大型公开数据集上的评估表明，PUMBA在各项指标上均持续优于其基于Transformer的前身PIsToN。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。