Reddy M Rami, Reddy C Ravikumar, Rathore R S, Erion Mark D, Aparoy P, Reddy R Nageswara, Reddanna P
RR Labs, Inc., 8013 Los Sabalos Street, San Diego, CA 92126, USA.
Curr Pharm Des. 2014;20(20):3323-37. doi: 10.2174/13816128113199990604.
Post-genomic era has led to the discovery of several new targets posing challenges for structure-based drug design efforts to identify lead compounds. Multiple computational methodologies exist to predict the high ranking hit/lead compounds. Among them, free energy methods provide the most accurate estimate of predicted binding affinity. Pathway-based Free Energy Perturbation (FEP), Thermodynamic Integration (TI) and Slow Growth (SG) as well as less rigorous end-point methods such as Linear interaction energy (LIE), Molecular Mechanics-Poisson Boltzmann./Generalized Born Surface Area (MM-PBSA/GBSA) and λ-dynamics have been applied to a variety of biologically relevant problems. The recent advances in free energy methods and their applications including the prediction of protein-ligand binding affinity for some of the important drug targets have been elaborated. Results using a recently developed Quantum Mechanics (QM)/Molecular Mechanics (MM) based Free Energy Perturbation (FEP) method, which has the potential to provide a very accurate estimation of binding affinities to date has been discussed. A case study for the optimization of inhibitors for the fructose 1,6- bisphosphatase inhibitors has been described.
后基因组时代带来了几个新靶点的发现，这给基于结构的药物设计寻找先导化合物的工作带来了挑战。存在多种计算方法来预测高排名的命中/先导化合物。其中，自由能方法能对预测的结合亲和力提供最准确的估计。基于途径的自由能微扰（FEP）、热力学积分（TI）和缓慢增长（SG）以及不太严格的端点方法，如线性相互作用能（LIE）、分子力学-泊松玻尔兹曼/广义玻恩表面积（MM-PBSA/GBSA）和λ动力学，已被应用于各种生物学相关问题。本文阐述了自由能方法及其应用的最新进展，包括对一些重要药物靶点的蛋白质-配体结合亲和力的预测。讨论了使用最近开发的基于量子力学（QM）/分子力学（MM）的自由能微扰（FEP）方法的结果，该方法有潜力提供迄今为止非常准确的结合亲和力估计。描述了一个针对果糖1,6-二磷酸酶抑制剂优化的案例研究。
