Optimization and Evaluation of Site-Identification by Ligand Competitive Saturation (SILCS) as a Tool for Target-Based Ligand Optimization
Optimization and Evaluation of Site-Identification by Ligand Competitive Saturation (SILCS) as a Tool for Target-Based Ligand Optimization
复制标题
DOI：
10.1021/acs.jcim.9b00210
复制
发表时间：
2019-04
影响因子：
5.6
通讯作者：
Vincent D. Ustach;S. K. Lakkaraju;Sunhwan Jo;Wenbo Yu;Wenjuan Jiang;Alexander D. MacKerell
中科院分区：
文献类型：
--
作者：
Vincent D. Ustach;S. K. Lakkaraju;Sunhwan Jo;Wenbo Yu;Wenjuan Jiang;Alexander D. MacKerell
化学碎片共溶剂取样技术已成为一种通用的工具，在配体-蛋白质结合预测。通过配体竞争饱和的位点识别（SILCS）是这样一种方法，其将蛋白质上的化学片段的分布映射为称为FragMaps的自由能场。然后通过Monte Carlo技术在FragMaps（SILCS-MC）领域中模拟配体，以预测它们与靶蛋白的结合构象和相对亲和力。SILCS-MC的应用程序使用了许多不同的评分方案和MC采样协议对多个蛋白质的目标进行评估和优化的预测能力的方法。使用具有广泛化学变异性的7种蛋白质靶标和551种配体来评估和优化模型，以最大化Pearson相关系数、Pearlman预测指数、校正相对结合亲和力以及相对于绝对实验结合亲和力的均方根误差。在整个蛋白质-配体组中，对于最高的总体SILCS方案，配体的相对亲和力平均在69%的时间内被正确预测。使用贝叶斯机器学习（ML）算法训练FragMap加权因子导致平均75%的相对正确亲和力预测增加。此外，一旦确定了特定蛋白质-配体系统的最佳方案，平均可预测性达到76%。由于使用物理上正确的FragMap权重作为先验，ML算法在小训练数据集（30种或更多化合物）上是成功的。值得注意的是，76%的正确相对预测率类似于或优于自由能微扰方法，后者在计算上比SILCS昂贵得多。结果进一步支持SILCS作为一个强大的和计算可访问的工具，以支持药物发现的铅优化和开发的效用。
Chemical fragment cosolvent sampling techniques have become a versatile tool in ligand-protein binding prediction. Site-identification by ligand competitive saturation (SILCS) is one such method that maps the distribution of chemical fragments on a protein as free energy fields called FragMaps. Ligands are then simulated via Monte Carlo techniques in the field of the FragMaps (SILCS-MC) to predict their binding conformations and relative affinities for the target protein. Application of SILCS-MC using a number of different scoring schemes and MC sampling protocols against multiple protein targets was undertaken to evaluate and optimize the predictive capability of the method. Seven protein targets and 551 ligands with broad chemical variability were used to evaluate and optimize the model to maximize Pearson's correlation coefficient, Pearlman's predictive index, correct relative binding affinity, and root-mean-square error versus the absolute experimental binding affinities. Across the protein-ligand sets, the relative affinities of the ligands were predicted correctly an average of 69% of the time for the highest overall SILCS protocol. Training the FragMap weighting factors using a Bayesian machine learning (ML) algorithm led to an increase to an average 75% relative correct affinity predictions. Furthermore, once the optimal protocol is identified for a specific protein-ligand system average predictabilities of 76% are achieved. The ML algorithm is successful with small training sets of data (30 or more compounds) due to the use of physically correct FragMap weights as priors. Notably, the 76% correct relative prediction rate is similar to or better than free energy perturbation methods that are significantly computationally more expensive than SILCS. The results further support the utility of SILCS as a powerful and computationally accessible tool to support lead optimization and development in drug discovery.