GAABind:一个基于几何意识的基于注意力的网络,用于准确的蛋白质-连接体结合姿势和结合亲和力预测
Huishuang Tan1, Zhixin Wang1,2, Guang Hu3,4
1Key Laboratory of Ministry of Education for Protein Science, School of Life Sciences, Tsinghua University, Beijing 100084, China.
Briefings in bioinformatics
|December 15, 2023
概括
一个新的深度学习模型GAABind准确地预测了蛋白质 - 连接体结合姿势和亲和力. 这一进步有助于药物发现,克服了分子相互作用的传统和当前深度学习方法的局限性.
科学领域:
- 计算化学计算化学
- 药物发现 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白质-配体相互作用的高通量分析对药物发现和优化至关重要.
- 由于当前分子建模技术的计算成本和局限性,准确预测结合姿势和亲和力仍然是一个挑战.
研究的目的:
- 开发一种以几何意识为基础的基于注意力的深度学习模型 (GAABind) 来预测蛋白质 - 连接体结合姿势和亲和力.
- 解决现有方法中分子表示学习和分子间相互作用建模的局限性.
主要方法:
- GAABind使用多任务学习框架来预测绑定姿势和亲和力.
- 该模型捕捉了蛋白质和连接体的几何和拓性质,采用了表达性分子表示学习.
- 它模拟了分子间多体相互作用,并使用专业网络模拟了连接体的构造性适应.
主要成果:
- 在基准数据集 (PDBbindv2020,CASF2016) 上,GAABind 实现了最先进的绑定姿势预测 (82.8%的成功率) 和可比的绑定亲和度预测 (皮尔森相关性高达0.803).
- 在SARS-CoV-2主要蛋白酶数据集上,GAABind在与基线方法相比,在姿势预测和优越结合亲和力预测方面显示了76.5%的成功率.
结论:
- GAABind提供了一种有效的深度学习方法,用于预测蛋白质-连接体相互作用,增强计算药物发现.
- 该模型能够整合几何意识和先进的交互建模的能力超过了现有的方法,为更有效的化合物识别铺平了道路.
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