Prediction and analysis of binding affinities for chemically diverse HIV-1 PR inhibitors by the modified SAFE_p approach

One of the biggest challenges in the "in silico" screening of enzyme ligands is to have a protocol that could predict the ligand binding free energies. In our group we have developed a very simple screening function (referred to as solvent accessibility free energy of binding predictor, SAFE_p) which we have applied previously to the study of peptidic HIV-1 protease (HIV-1 PR) inhibitors and later to cyclic urea type HIV-1 PR inhibitors. In this work, we have extended the SAFE_p protocol to a chemically diverse set of HIV-1 PR inhibitors with binding constants that differ by several orders of magnitude. The resulting function is able to reproduce the ranking and in many cases the value of the inhibitor binding affinities for the HIV-1 PR, with accuracy comparable with that of costlier protocols. We also demonstrate that the binding pocket SAFE_p analysis can contribute to the understanding of the physical forces that participate in ligand binding. The analysis tools afforded by our protocol have allowed us to identify an induced fit phenomena mediated by the inhibitor and have demonstrated that larger fragments do not necessarily contribute the most to the binding free energy, an outcome partially brought about by the substantial role the desolvation penalty plays in the energetics of binding. Finally, we have revisited the effect of the Asp dyad protonation state on the predicted binding affinities.     

Automated molecular simulation based binding affinity calculator for ligand-bound HIV-1 proteases

The successful application of high throughput molecular simulations to determine biochemical properties would be of great importance to the biomedical community if such simulations could be turned around in a clinically relevant timescale. An important example is the determination of antiretroviral inhibitor efficacy against varying strains of HIV through calculation of drug-protein binding affinities. We describe the Binding Affinity Calculator (BAC), a tool for the automated calculation of HIV-1 protease-ligand binding affinities. The tool employs fully atomistic molecular simulations alongside the well established molecular mechanics Poisson-Boltzmann solvent accessible surface area (MMPBSA) free energy methodology to enable the calculation of the binding free energy of several ligand-protease complexes, including all nine FDA approved inhibitors of HIV-1 protease and seven of the natural substrates cleaved by the protease. This enables the efficacy of these inhibitors to be ranked across several mutant strains of the protease relative to the wildtype. BAC is a tool that utilizes the power provided by a computational grid to automate all of the stages required to compute free energies of binding: model preparation, equilibration, simulation, postprocessing, and data-marshaling around the generally widely distributed compute resources utilized. Such automation enables the molecular dynamics methodology to be used in a high throughput manner not achievable by manual methods. This paper describes the architecture and workflow management of BAC and the function of each of its components. Given adequate compute resources, BAC can yield quantitative information regarding drug resistance at the molecular level within 96 h. Such a timescale is of direct clinical relevance and can assist in decision support for the assessment of patient-specific optimal drug treatment and the subsequent response to therapy for any given genotype.     

3D-QSAR and molecular modeling of HIV-1 integrase inhibitors

Three-dimensional quantitative structure-activity relationship (3D QSAR) methods were applied on a series of inhibitors of HIV-1 integrase with respect to their inhibition of 3-processing and 3-end joining steps in vitro.The training set consisted of 27 compounds belonging to the class of thiazolothiazepines. The predictive ability of each model was evaluated using test set I consisting of four thiazolothiazepines and test set II comprised of seven compounds belonging to an entirely different structural class of coumarins. Maximum Common Substructure (MCS) based method was used to align the molecules and this was compared with other known methods of alignment. Two methods of 3D QSAR: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were analyzed in terms of their predictive abilities. CoMSIA produced significantly better results for all correlations. The results indicate a strong correlation between the inhibitory activity of these compounds and the steric and electrostatic fields around them. CoMSIA models with considerable internal as well as external predictive ability were obtained. A poor correlation obtained with hydrophobic field indicates that the binding of thiazolothiazepines to HIV-1 integrase is mainly enthalpic in nature. Further the most active compound of the series was docked into the active site using the crystal structure of integrase. The binding site was formed by the amino acid residues 64-67, 116, 148, 151-152, 155-156, and 159. The comparison of coefficient contour maps with the steric and electrostatic properties of the receptor shows high level of compatibility.     

Receptor binding affinity based comparative QSAR study of testosterone derivatives

The quantitative structure activity relationship models of 22 testosterone derivatives have been made with the help of topological and quantum chemical parameters. The molecular modeling and geometry optimization have been carried out with CAChe Pro software. The calculations of topological and quantum chemical parameters have been done by MOPAC 2007. The statistical parameters are calculated by STATISTICA and SSP software. The study indicates that the topological parameters better predict the receptor binding affinity of testosterone derivatives, whereas quantum chemical parameters better predict androgenic potency of testosterone derivatives as indicated by correlation coefficient, standard error, standard error of estimation, p value, t value, and degree of freedom of the quantitative structure activity relationship (QSAR) models. The predicted activity values obtained by these QSAR models are close to observed activity. © 2011 Wiley Periodicals, Inc. Int J Quantum Chem, 2012     

Molecular electrostatic potentials as input for the alignment of HIV-1 integrase inhibitors in 3D QSAR

Comparative molecular similarity indices analysis (CoMSIA), a three-dimensional quantitative structure activity relationship (3D QSAR) paradigm, was used to examine the correlations between the calculated physicochemical properties and the in vitro activities (3'-processing and 3'-strand transfer inhibition) of a series of human immunodeficiency virus type 1 (HIV-1) integrase inhibitors. The training set consisted of 34 molecules from five structurally diverse classes: salicylpyrazolinones, dioxepinones, coumarins, quinones, and benzoic hydrazides. The data set was aligned using extrema of molecular electrostatic potentials (MEPs). The predictive ability of the resultant model was evaluated using a test set comprised of 7 molecules belonging to a different structural class of thiazepinediones. A CoMSIA model using an MEP-based alignment showed considerable internal as well external predictive ability (r2(cv) = 0.821, r2(pred) = 0.608 for 3'-processing; and r2(cv) = 0.759, r2(pred.) = 0.660 for 3'-strand transfer).     

Modified ant colony optimization algorithm for variable selection in QSAR modeling: QSAR studies of cyclooxygenase inhibitors

A new version of an ant colony optimization (ACO) algorithm has been proposed. A modified ACO algorithm is proposed to select variables in QSAR modeling and to predict inhibiting action of some diarylimidazole derivatives on cyclooxygenase (COX) enzyme. As a comparison to this method, the evolution algorithm (EA) was also tested. Experimental results have demonstrated that the modified ACO is a useful tool for variable selection that needs few parameters to be adjusted and converges quickly toward the optimal position.     

Inhibitor docking screened by the modified SAFE_p scoring function: application to cyclic urea HIV-1 PR inhibitors

Our laboratory has in the past developed a method for the prediction of ligand binding free energies to proteins, referred to as SAFE_p (Solvent free energy predictor). Previously, we have applied this protocol for the prediction of the binding free energy of peptidic and cyclic urea HIV-1 PR inhibitors, whose X-ray structures bound to enzyme are known. In this work, we present the first account of a docking simulation, where the ligand conformations were screened and inhibitor ranking was predicted on the basis of a modified SAFE_p approach, for a set of cyclic urea-HIV-1 PR complexes whose structures are not known. We show that the optimal dielectric constant for docking is rather high, in line with the values needed to reproduce some protein residue properties, like pKa's. Our protocol is able to reproduce most of the observed binding ranking, even in the case that the components of the equation are not fitted to experimental data. Partition of the binding free energy into pocket and residue contributions sheds light into the importance of the inhibitor's fragments and on the prediction of "hot spots" for resistance mutations.     

HIV-1逆转录酶抑制剂2-氨基6-苯磺酰基苄腈及其类似物活性OSAR研究

首先利用半经验AM I量子化学方法计算了54个2-氨基-6-苯磺酰基苄腈及其类似物的物理化学、电子结构、指示变量等共28个参数,然后使用偏最小二乘,穷举回归和混沌遗传乘法训练的人工神经网络方法建立了这些参数和其抑制H IV-1逆转录酶活性之间的定量构效关系模型,为设计、合成更高生物活性的该类化合物提供了理论参考。     

Use of 3D QSAR to investigate the mode of binding of pyrazinones to HIV-1 RT

Abstract  

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on the docked conformation were performed for 24 pyrazinone derivatives. All compounds were docked into the wild-type HIV-1 RT binding pocket and the lowest-energy docked configurations were used to construct the 3D QSAR models. The CoMFA and CoMSIA models enable good prediction of inhibition by the pyrazinones, with r_\textcv² r_{\text{cv}}^{2}  = 0.703 and 0.735. Results obtained from CoMFA and CoMSIA based on the docking conformation of the pyrazinones are, therefore, powerful means of elucidating the mode of binding of pyrazinones and suggesting the design of new potent NNRTIs.