Prediction of protein-ligand binding affinities using 3D machine learning methods

使用 3D 机器学习方法预测蛋白质-配体结合亲和力

• 批准号: 2597615
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2597615
• 关键词: Prediction; protein; ligand; binding; affinities

3D machine learning methods offer the potential to bridge the gap between docking and FEP, providing high throughput predictions of protein-ligand binding affinities with higher accuracy than classical docking scoring functions. Atomic environment vectors (AEVs) have been shown to be a promising representation for both rapid calculation of quantum mechanical conformational energies of small molecules, and in predicting protein-ligand binding affinities. In the 2022 rotation project, we applied these methods to predict the energy of of non-covalent interactions, in particular hydrogen bonds. Future directions include sharing parameters between elements, including charge and hybridization state as features, and transfer learning from the non-covalent interaction task to the protein-ligand binding task. It is also of interest to explore how AEVs can be used in molecular dynamics simulations following upon recent results from Roitberg on simulating a protein-ligand complex using ANI. These methods could be useful for including the entropic contributions to binding affinities as well as the enthalpic contributions from non-covalent interactions. This project would aim to test models on binding affinity and test the extent to which these models robustly learn non-covalent interactions and have improved generalizability over the prevailing ligand-based models for predicting binding affinity. This project falls into the EPSRC Biological informatics research area due to the use of computational modeling of biological systems using chemical and biological data. GSK is a collaborating company.

3D机器学习方法提供了弥合对接和FEP之间的差距的潜力，提供了比经典对接评分函数更高精度的蛋白质-配体结合亲和力的高通量预测。原子环境矢量（Atomic Environment Vectors，AEV）是一种快速计算小分子量子力学构象能和预测蛋白质-配体结合亲和力的有效方法。在2022年的旋转项目中，我们应用这些方法来预测非共价相互作用的能量，特别是氢键。未来的发展方向包括元素之间共享参数，包括电荷和杂交状态作为特征，并将学习从非共价相互作用任务转移到蛋白质-配体结合任务。这也是有趣的，探讨如何AEV可以用于分子动力学模拟后，从罗伊特伯格最近的结果模拟蛋白质-配体复合物使用ANI。这些方法可用于包括熵贡献的结合亲和力，以及从非共价相互作用的疏水贡献。该项目旨在测试结合亲和力的模型，并测试这些模型在多大程度上稳健地学习非共价相互作用，并提高了预测结合亲和力的普遍基于配体的模型的可推广性。这个项目福尔斯属于EPSRC生物信息学研究领域，由于使用化学和生物数据的生物系统的计算建模。GSK是一家合作公司。