Integration of element specific persistent homology and machine learning for protein-ligand binding affinity prediction
Integration of element specific persistent homology and machine learning for protein-ligand binding affinity prediction
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DOI：
10.1002/cnm.2914
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发表时间：
2018-02-01
影响因子：
2.1
通讯作者：
Wei, Guo-Wei
中科院分区：
文献类型：
--
作者：
Cang, Zixuan;Wei, Guo-Wei
蛋白质-配体结合是一个基本的生物学过程，对许多其他生物学过程至关重要，如信号转导、代谢途径、酶构建、细胞分泌和基因表达。蛋白质-配体结合亲和力的准确预测对于合理的药物设计以及蛋白质-配体结合和结合诱导功能的理解至关重要。现有的结合亲和力预测方法充斥着几何细节，涉及过高的维度，这削弱了它们对大量结合数据的预测能力。拓扑提供了最终的抽象层次，因此导致了几何信息的大量减少。持久同调将几何信息嵌入到拓扑不变量中，弥补了复杂几何与抽象拓扑之间的差距。然而，它过度简化了生物信息。本工作引入元素特异性持续同源性（ESPH）或多组分持续同源性来保留拓扑简化过程中的重要生物信息。ESPH和机器学习的结合为大分子分析提供了强大的范例。在2个大数据集上的测试表明，所提出的基于拓扑的机器学习范式在蛋白质-配体结合亲和力预测方面优于其他现有方法。ESPH揭示了蛋白质-配体结合机制，这是其他常规技术所不能达到的。本方法揭示了蛋白质-配体疏水相互作用延伸到40埃远离结合位点，这对药物和蛋白质设计具有重要的衍生物。
Protein-ligand binding is a fundamental biological process that is paramount to many other biological processes, such as signal transduction, metabolic pathways, enzyme construction, cell secretion, and gene expression. Accurate prediction of protein-ligand binding affinities is vital to rational drug design and the understanding of protein-ligand binding and binding induced function. Existing binding affinity prediction methods are inundated with geometric detail and involve excessively high dimensions, which undermines their predictive power for massive binding data. Topology provides the ultimate level of abstraction and thus incurs too much reduction in geometric information. Persistent homology embeds geometric information into topological invariants and bridges the gap between complex geometry and abstract topology. However, it oversimplifies biological information. This work introduces element specific persistent homology (ESPH) or multicomponent persistent homology to retain crucial biological information during topological simplification. The combination of ESPH and machine learning gives rise to a powerful paradigm for macromolecular analysis. Tests on 2 large data sets indicate that the proposed topology-based machine-learning paradigm outperforms other existing methods in protein-ligand binding affinity predictions. ESPH reveals protein-ligand binding mechanism that can not be attained from other conventional techniques. The present approach reveals that protein-ligand hydrophobic interactions are extended to 40 angstrom away from the binding site, which has a significant ramification to drug and protein design.