生物灵感的mamba用于抗体-抗原相互作用预测
Xuan Liu1, Haitao Fu2, Yuqing Yang3
1School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.
Biomolecules
|June 26, 2025
概括
MambaAAI使用一种新的Mamba架构预测抗体-抗原相互作用和结合部位. 这种方法通过准确识别关键的表位和表位区域来增强免疫治疗的发现.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 免疫学 免疫学 免疫学
背景情况:
- 抗体的发现对免疫疗法发展至关重要,需要识别高亲和度结合候选人.
- 预测抗体-抗原相互作用 (AAI) 和确定结合部位 (表位和位) 是计算生物学中关键但具有挑战性的任务.
研究的目的:
- 介绍MambaAAI,这是一个新的生物灵感模型,利用Mamba架构来预测AAI并识别绑定站点.
- 利用选择性注意力机制,提高AAI预测和绑定站点定位的准确性.
主要方法:
- 采用ESM-2,一个预训练的蛋白质语言模型,从抗体和抗原序列中提取进化表征.
- 开发了一种双视图的Mamba编码器,从抗体和抗原的角度学习残留水平相互作用矩阵的嵌入.
- 使用多层感知子来解码学习嵌入,以预测相互作用概率.
主要成果:
- 与大规模中和数据集的最新基线相比,MambaAAI的预测准确度略高.
- 该模型对未见的抗体和抗原表现出强大的概括能力.
- 选择性注意力机制在SARS-CoV-2抗体示例中成功识别了关键的表位和副位区域.
结论:
- 通过动态选择关键的残留点,MambaAAI在预测AAI和识别结合点方面取得了重大进展.
- 该模型具有很大的潜力,可以加速发现具有较低计算负担的领先免疫疗法候选人.
- 马巴AAI发现关键结合区域的能力为更有针对性的药物开发铺平了道路.
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