Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes
Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes
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DOI：
10.1016/j.bpc.2018.05.010
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发表时间：
2018-09-01
影响因子：
3.8
通讯作者：
de Azevedo, Walter Filgueira
中科院分区：
文献类型：
--
作者：
Bitencourt-Ferreira, Gabriela;de Azevedo, Walter Filgueira
在药物开发和设计的早期阶段，使用蛋白质配体配合物的原子坐标来评估结合亲和力。从计算观点来看，在生物系统的仿真中，创建可靠的评分功能仍然是一个开放的问题，而新一代机器学习模型的开发是一个积极的研究领域。在这项工作中，我们提出了一种新的评分功能，以根据涉及蛋白质和活性配体的复合物的晶体学结构来预测结合的吉布斯自由能（Delta g）。我们利用了可用的能量术语Autodock Vina评分功能，并使用程序Sandres中可用的机器学习方法训练了新功能。我们使用了一个训练集，该训练集由高分辨率晶体结构组成，可为其提供AG数据。我们在这里描述了开发机器学习模型的方法，以使用程序Sandres预测结合亲和力。与Moldock，植物，Autodock 4和Autodock Vina评分功能相比，我们的机器学习模型的统计分析表明，表现出色。我们预计，这种新的机器学习模型可以通过在分析虚拟筛选模拟中应用可靠的评分功能来改善药物设计和开发。
The possibility of using the atomic coordinates of protein-ligand complexes to assess binding affinity has a beneficial impact in the early stages of drug development and design. From the computational view, the creation of reliable scoring functions is still an open problem in the simulation of biological systems, and the development of a new generation machine-learning model is an active research field. In this work, we propose a novel scoring function to predict Gibbs free energy of binding (Delta G) based on the crystallographic structure of complexes involving a protein and an active ligand. We made use of the energy terms available the AutoDock Vina scoring function and trained a novel function using the machine learning methods available in the program SAnDReS. We used a training set composed exclusively of high-resolution crystallographic structures for which the AG data was available. We describe here the methodology to develop a machine-learning model to predict binding affinity using the program SAnDReS. Statistical analysis of our machine-learning model indicated a superior performance when compared to the MolDock, Plants, AutoDock 4, and AutoDock Vina scoring functions. We expect that this new machine-learning model could improve drug design and development through the application of a reliable scoring function in the analysis virtual screening simulations.