基于多种治疗靶点和不同配体集的配体结合能的计算预测——以 BACE1、TYK2、HSP90 和 PERK 蛋白为例。

In Silico Prediction of Ligand Binding Energies in Multiple Therapeutic Targets and Diverse Ligand Sets-A Case Study on BACE1, TYK2, HSP90, and PERK Proteins.

机构信息

Cloud Pharmaceuticals, Inc. , 6 Davis Drive, Research Triangle Park, North Carolina 27709, United States.

出版信息

J Phys Chem B. 2017 Aug 31;121(34):8142-8148. doi: 10.1021/acs.jpcb.7b07224. Epub 2017 Aug 17.

Abstract

We present here the use of QM/MM LIE (linear interaction energy) based binding free energy calculations that greatly improve the precision and accuracy of predicting experimental binding affinities, in comparison to most current binding free energy methodologies, while maintaining reasonable computational times. Calculations are done for four sets of ligand-protein complexes, chosen on the basis of diversity of protein types and availability of experimental data, totaling 140 ligands binding to therapeutic protein targets BACE1, TYK2, HSP90, and PERK. This method allows calculations for a diverse set of ligands and multiple protein targets without the need for parametrization or extra calculations. The accuracy achieved with this method can be used to guide small molecule computational drug design programs.

摘要

我们在此展示了使用 QM/MM LIE(线性相互作用能)的结合自由能计算,与大多数当前的结合自由能方法相比,该方法大大提高了预测实验结合亲和力的精度和准确性,同时保持了合理的计算时间。我们对四组配体-蛋白质复合物进行了计算,这些复合物是根据蛋白质类型的多样性和实验数据的可用性选择的,总共包含 140 种配体与 BACE1、TYK2、HSP90 和 PERK 等治疗蛋白靶标结合。该方法允许对多种配体和多个蛋白质靶标进行计算,而无需参数化或额外计算。该方法的准确性可用于指导小分子计算药物设计程序。

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