Receptor-independent 4D-QSAR analysis of a set of norstatine derived inhibitors of HIV-1 protease

A training set of 27 norstatine derived inhibitors of HIV-1 protease, based on the 3(S)-amino-2(S)-hydroxyl-4-phenylbutanoic acid core (AHPBA), for which the -log IC(50) values were measured, was used to construct 4D-QSAR models. Five unique RI-4D-QSAR models, from two different alignments, were identified (q(2) = 0.86-0.95). These five models were used to map the atom type morphology of the lining of the inhibitor binding site at the HIV protease receptor site as well as predict the inhibition potencies of seven test set compounds for model validation. The five models, overall, predict the -log IC(50) activity values for the test set compounds in a manner consistent with their q(2) values. The models also correctly identify the hydrophobic nature of the HIV protease receptor site, and inferences are made as to further structural modifications to improve the potency of the AHPBA inhibitors of HIV protease. The finding of five unique, and nearly statistically equivalent, RI-4D-QSAR models for the training set demonstrates that there can be more than one way to fit structure-activity data even within a given QSAR methodology. This set of unique, equally good individual models is referred to as the manifold model.     

Structure-based QSAR analysis of a set of 4-hydroxy-5,6-dihydropyrones as inhibitors of HIV-1 protease: an application of the receptor-dependent (RD) 4D-QSAR formalism

Receptor-dependent (RD) 4D-QSAR models were constructed for a set of 39 4-hydroxy-5,6-dihydropyrone analogue HIV-1 protease inhibitors. The receptor model used in this QSAR analysis was derived from the HIV-1 protease (PDB ID ) crystal structure. The bound ligand in the active site of the enzyme, also a 4-hydroxy-5,6-dihydropyrone analogue, was used as the reference ligand for docking the data set compounds. The optimized RD 4D-QSAR models are not only statistically significant (r(2) = 0.86, q(2) = 0.80 for four- and greater-term models) but also possess reasonable predictivity based on test set predictions. The proposed "active" conformations of the docked analogues in the active site of the enzyme are consistent in overall molecular shape with those suggested from crystallographic studies. Moreover, the RD 4D-QSAR models also "capture" the existence of specific induced-fit interactions between the enzyme active site and each specific inhibitor. Hydrophobic interactions, steric shape requirements, and hydrogen bonding of the 4-hydroxy-5,6-dihydropyrone analogues with the HIV-1 protease binding site model dominate the RD 4D-QSAR models in a manner again consistent with experimental conclusions. Some possible hypotheses for the development of new lead HIV-1 protease inhibitors can be inferred from the RD 4D-QSAR models.     

4D-QSAR analysis of a set of ecdysteroids and a comparison to CoMFA modeling.

The ecdysteroid-responsive Drosophila melanogaster B(II) cell line is a prototypical homologous inducible gene expression system. A training set of 71 ecdysteroids, for which the -log(EC(50)) potencies in the ecdysteroid-responsive B(II) cell line were measured, was used to construct 4D-QSAR models. Four nearly equivalent optimum 4D-QSAR models, for two modestly different alignments, were identified (Q(2) = 0.76-0.80). These four models, together with two CoMFA models, were used in consensus modeling to arrive at a three-dimensional pharmacophore. The C-2 and C-22 hydroxyls are identified as hydrogen-bond acceptor sites which enhance activity. A hydrophobic site near C-12 is consistent with increasing activity. The side-chain substituents at C-17 are predicted to adopt semiextended "active" conformations which could fit into a cylinder-shaped binding pocket lined largely with nonpolar residues for enhanced activity. A test set of 20 ecdysteroids was used to evaluate the QSAR models. Two 4D-QSAR models for one alignment were identified to be superior to the others based on having the smallest average residuals of prediction for the prediction set (0.69 and 1.13 -log[EC(50)] units). The correlation coefficients of the optimum 4D-QSAR models (R(2) = 0.87 and 0.88) are nearly the same as those of the best CoMFA model (R(2) = 0.92) determined for the same training set. However, the cross-validation correlation coefficient of the CoMFA model is less significant (Q(2) = 0.59) than those of the 4D-QSAR models (Q(2) = 0.80 and 0.80).     

A combined ligand-based and target-based drug design approach for G-protein coupled receptors: application to salvinorin A,a selective kappa opioid receptor agonist

Combined ligand-based and target-based drug design approaches provide a synergistic advantage over either method individually. Therefore, we set out to develop a powerful virtual screening model to identify novel molecular scaffolds as potential leads for the human KOP (hKOP) receptor employing a combined approach. Utilizing a set of recently reported derivatives of salvinorin A, a structurally unique KOP receptor agonist, a pharmacophore model was developed that consisted of two hydrogen bond acceptor and three hydrophobic features. The model was cross-validated by randomizing the data using the CatScramble technique. Further validation was carried out using a test set that performed well in classifying active and inactive molecules correctly. Simultaneously, a bovine rhodopsin based “agonist-bound” hKOP receptor model was also generated. The model provided more accurate information about the putative binding site of salvinorin A based ligands. Several protein structure-checking programs were used to validate the model. In addition, this model was in agreement with the mutation experiments carried out on KOP receptor. The predictive ability of the model was evaluated by docking a set of known KOP receptor agonists into the active site of this model. The docked scores correlated reasonably well with experimental pK _i values. It is hypothesized that the integration of these two independently generated models would enable a swift and reliable identification of new lead compounds that could reduce time and cost of hit finding within the drug discovery and development process, particularly in the case of GPCRs.Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.     

HIV-1 protease cleavage site prediction based on amino acid property

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired is useful for designing specific and efficient HIV protease inhibitors. Recently, several works have approached the HIV-1 protease specificity problem by applying a number of classifier creation and combination methods. The pace in searching for the proper inhibitors of HIV protease will be greatly expedited if one can find an accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this article, we selected HIV-1 protease as the subject of the study. 299 oligopeptides were chosen for the training set, while the other 63 oligopeptides were taken as a test set. The peptides are represented by features constructed by AAIndex (Kawashima et al., Nucleic Acids Res 1999, 27, 368; Kawashima and Kanehisa, Nucleic Acids Res 2000, 28, 374). The mRMR method (Maximum Relevance, Minimum Redundancy; Ding and Peng, Proc Second IEEE Comput Syst Bioinformatics Conf 2003, 523; Peng et al., IEEE Trans Pattern Anal Mach Intell 2005, 27, 1226) combining with incremental feature selection (IFS) and feature forward search (FFS) are applied to find the two important cleavage sites and to select 364 important biochemistry features by jackknife test. Using KNN (K-nearest neighbors) to combine the selected features, the prediction model obtains high accuracy rate of 91.3% for Jackknife cross-validation test and 87.3% for independent-set test. It is expected that our feature selection scheme can be referred to as a useful assistant technique for finding effective inhibitors of HIV protease, especially for the scientists in this field.     

Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation

The 2015 D3R Grand Challenge provided an opportunity to test our new model for the binding free energy of small molecules, as well as to assess our protocol to predict binding poses for protein-ligand complexes. Our pose predictions were ranked 3–9 for the HSP90 dataset, depending on the assessment metric. For the MAP4K dataset the ranks are very dispersed and equal to 2–35, depending on the assessment metric, which does not provide any insight into the accuracy of the method. The main success of our pose prediction protocol was the re-scoring stage using the recently developed Convex-PL potential. We make a thorough analysis of our docking predictions made with AutoDock Vina and discuss the effect of the choice of rigid receptor templates, the number of flexible residues in the binding pocket, the binding pocket size, and the benefits of re-scoring. However, the main challenge was to predict experimentally determined binding affinities for two blind test sets. Our affinity prediction model consisted of two terms, a pairwise-additive enthalpy, and a non pairwise-additive entropy. We trained the free parameters of the model with a regularized regression using affinity and structural data from the PDBBind database. Our model performed very well on the training set, however, failed on the two test sets. We explain the drawback and pitfalls of our model, in particular in terms of relative coverage of the test set by the training set and missed dynamical properties from crystal structures, and discuss different routes to improve it.     

HIV蛋白酶抑制剂——利迪链菌素的分子对接研究

用分子对接的方法, 对利迪链菌素的抗HIV蛋白酶活性进行了研究. 为了更准确地反映利迪链菌素分子与酶蛋白结合的情况, 充分考虑受体活性部位的柔性, 采用了FlexX(初步对接)和Flexidock(精确对接)分两步将配体与受体进行对接. 在初步对接中, 设计了不同的受体活性部位来考察是否有结合水分子参与抑制剂与酶的结合. 对一种作用方式已知的非肽类HIV蛋白酶抑制剂Aha006进行的对接研究显示, 分子模拟的结果与实际情况吻合得较好, 证明了本文所采用的方法的可靠性. 利迪链菌素与蛋白酶活性部位的对接结果显示, 配体分子与受体之间的结合没有结合水分子的参与, 两者通过5对氢键作用结合成为稳定的复合物. 利迪链菌素占据结合腔, 覆盖了蛋白酶的活性三联体Asp25-Thr26-Gly27, 从而起到抑制其生物活性的作用.     

3C-like蛋白酶抑制剂的构效关系、分子对接和分子动力学

3C-like蛋白酶是中东呼吸综合征冠状病毒（MERS-CoV）等其它冠状病毒的繁殖过程中极为重要的蛋白酶。它已成为人类在抗冠状病毒领域中的研究热点。本文基于计算生物学方法对与MERS-CoV同属的蝙蝠冠状病毒HKU4（HKU4-CoV）的43个肽类3C-like蛋白酶抑制剂分子,建立三维定量构效关系（3D-QSAR）模型。在基于配体叠合的基础上,发现比较分子相似性指数分析法（CoMSIA）中的四个场组合（位阻场、静电场、氢键供体场与氢键受体场）为最优的模型（Q²=0.522,R_ncv²=0.996,R_pre²=0.904;Q²：交叉验证相关系数,R_ncv²：非交叉验证相关系数,R_pre²：验证集分子的预测值相关系数）,并借助该模型通过分子对接（docking）与分子动力学（MD）方法阐明了配受体结合作用。实验结果表明：（1）基于最优的CoMSIA模型基础上的三维等势图形象地说明了分子基团的位阻作用、静电作用、氢键供体与氢键受体作用对分子生物活性的影响;（2）分子对接研究结果显示了疏水性以及结晶水、氨基酸His166和Glu169在配体和受体结合过程中产生重要作用;（3）分子动力学模拟进一步验证了分子对接模型的可靠性,并发现了两个新的关键氨基酸Ser24与Gln192,它们与配体产生了两个较强的氢键。此外,根据这些结果,一些新的具有潜在抑制活性的肽类化合物作为3C-like蛋白酶抑制剂被获得。以上结果能够帮助深入了解3C-like蛋白酶与肽类抑制剂的作用机理,并且能够为今后的抗MERS-CoV药物设计提供有价值的参考。     

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.     

Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge

Induced fit or protein flexibility can make a given structure less useful for docking and/or scoring. The 2015 Drug Design Data Resource (D3R) Grand Challenge provided a unique opportunity to prospectively test optimal strategies for virtual screening in these type of targets: heat shock protein 90 (HSP90), a protein with multiple ligand-induced binding modes; and mitogen-activated protein kinase kinase kinase kinase 4 (MAP4K4), a kinase with a large flexible pocket. Using previously known co-crystal structures, we tested predictions from methods that keep the receptor structure fixed and used (a) multiple receptor/ligand co-crystals as binding templates for minimization or docking (“close”), (b) methods that align or dock to a single receptor (“cross”), and (c) a hybrid approach that chose from multiple bound ligands as initial templates for minimization to a single receptor (“min-cross”). Pose prediction using our “close” models resulted in average ligand RMSDs of 0.32 and 1.6 Å for HSP90 and MAP4K4, respectively, the most accurate models of the community-wide challenge. On the other hand, affinity ranking using our “cross” methods performed well overall despite the fact that a fixed receptor cannot model ligand-induced structural changes,. In addition, “close” methods that leverage the co-crystals of the different binding modes of HSP90 also predicted the best affinity ranking. Our studies suggest that analysis of changes on the receptor structure upon ligand binding can help select an optimal virtual screening strategy.     

Homology model-guided 3D-QSAR studies of HIV-1 integrase inhibitors

In the present study, we report the exploration of binding modes of potent HIV-1 integrase (IN) inhibitors MK-0518 (raltegravir) and GS-9137 (elvitegravir) as well as chalcone and related amide IN inhibitors we recently synthesized and the development of 3D-QSAR models for integrase inhibition. Homology models of DNA-bound HIV-1 IN were constructed on the basis of the X-ray crystal structure of the foamy virus IN-DNA complex (PDB ID: 3L2T ) and used for docking. The binding modes of raltegravir and elvitegravir in our homology models are in accordance with those in the foamy virus structure revealing interactions important for inhibitor-IN binding. To gain further insights into the structural requirements for IN inhibition, three-dimensional quantitative structure activity relationship (3D-QSAR) studies were conducted using raltegravir, elvitegravir, and their analogs; our synthesized 3-keto salicylic acid IN inhibitor series; as well as other structurally related HIV-1 IN inhibitors. In the first part of the study with 103 compounds, atom-fit alignments, I and II, and docking-based alignment, III, were used to develop 3D-QSAR models 1, 2, and 3, respectively, each comprising comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSARs. This initial analysis indicated that the docking-based (structure-based) model 3 performed better than the atom-fit (ligand-based) models 1 and 2, in terms of statistical significance and robustness. Thus, the docking-based alignment was then subsequently used with an expanded data set of 296 compounds for building a more comprehensive 3D-QSAR, model 4. Model 4 afforded good q2 values of 0.70 and 0.75 for CoMFA and CoMSIA 3D-QSARs, respectively, and showed good predictive performance on an external validation test set of 59 compounds with predictive r2 values up to 0.71. The HIV IN-DNA homology model of biological relevance and the comprehensive 3D-QSAR models developed in the present study provide insights and new predictive tools for structure-based design and optimization of IN inhibitors.     

Binding of novel fullerene inhibitors to HIV-1 protease: insight through molecular dynamics and molecular mechanics Poisson–Boltzmann surface area calculations

The objectives of this study include the design of a series of novel fullerene-based inhibitors for HIV-1 protease (HIV-1 PR), by employing two strategies that can also be applied to the design of inhibitors for any other target. Additionally, the interactions which contribute to the observed exceptionally high binding free energies were analyzed. In particular, we investigated: (1) hydrogen bonding (H-bond) interactions between specific fullerene derivatives and the protease, (2) the regions of HIV-1 PR that play a significant role in binding, (3) protease changes upon binding and (4) various contributions to the binding free energy, in order to identify the most significant of them. This study has been performed by employing a docking technique, two 3D-QSAR models, molecular dynamics (MD) simulations and the molecular mechanics Poisson–Boltzmann surface area (MM–PBSA) method. Our computed binding free energies are in satisfactory agreement with the experimental results. The suitability of specific fullerene derivatives as drug candidates was further enhanced, after ADMET (absorption, distribution, metabolism, excretion and toxicity) properties have been estimated to be promising. The outcomes of this study revealed important protein–ligand interaction patterns that may lead towards the development of novel, potent HIV-1 PR inhibitors.     

Mangiferin, an anti-HIV-1 agent targeting protease and effective against resistant strains

The anti-HIV-1 activity of mangiferin was evaluated. Mangiferin can inhibit HIV-1(Ⅲ)(B) induced syncytium formation at non-cytotoxic concentrations, with a 50% effective concentration (EC??) at 16.90 μM and a therapeutic index (TI) above 140. Mangiferin also showed good activities in other laboratory-derived strains, clinically isolated strains and resistant HIV-1 strains. Mechanism studies revealed that mangiferin might inhibit the HIV-1 protease, but is still effective against HIV peptidic protease inhibitor resistant strains. A combination of docking and pharmacophore methods clarified possible binding modes of mangiferin in the HIV-1 protease. The pharmacophore model of mangiferin consists of two hydrogen bond donors and two hydrogen bond acceptors. Compared to pharmacophore features found in commercially available drugs, three pharmacophoric elements matched well and one novel pharmacophore element was observed. Moreover, molecular docking analysis demonstrated that the pharmacophoric elements play important roles in binding HIV-1 protease. Mangiferin is a novel nonpeptidic protease inhibitor with an original structure that represents an effective drug development strategy for combating drug resistance.     

An extensive test of 14 scoring functions using the PDBbind refined set of 800 protein-ligand complexes

Fourteen popular scoring functions, i.e., X-Score, DrugScore, five scoring functions in the Sybyl software (D-Score, PMF-Score, G-Score, ChemScore, and F-Score), four scoring functions in the Cerius2 software (LigScore, PLP, PMF, and LUDI), two scoring functions in the GOLD program (GoldScore and ChemScore), and HINT, were tested on the refined set of the PDBbind database, a set of 800 diverse protein-ligand complexes with high-resolution crystal structures and experimentally determined Ki or Kd values. The focus of our study was to assess the ability of these scoring functions to predict binding affinities based on the experimentally determined high-resolution crystal structures of proteins in complex with their ligands. The quantitative correlation between the binding scores produced by each scoring function and the known binding constants of the 800 complexes was computed. X-Score, DrugScore, Sybyl::ChemScore, and Cerius2::PLP provided better correlations than the other scoring functions with standard deviations of 1.8-2.0 log units. These four scoring functions were also found to be robust enough to carry out computation directly on unaltered crystal structures. To examine how well scoring functions predict the binding affinities for ligands bound to the same target protein, the performance of these 14 scoring functions were evaluated on three subsets of protein-ligand complexes from the test set: HIV-1 protease complexes (82 entries), trypsin complexes (45 entries), and carbonic anhydrase II complexes (40 entries). Although the results for the HIV-1 protease subset are less than desirable, several scoring functions are able to satisfactorily predict the binding affinities for the trypsin and the carbonic anhydrase II subsets with standard deviation as low as 1.0 log unit (corresponding to 1.3-1.4 kcal/mol at room temperature). Our results demonstrate the strengths as well as the weaknesses of current scoring functions for binding affinity prediction.     

FILO (Field Interaction Ligand Optimization): A simplex strategy for searching the optimal ligand interaction field in drug design

A method (FILO, Field Interaction Ligand Optimization) for obtaining the optimal molecular interaction field was developed on the basis of the Simplex optimization procedure applied to a matrix of interaction energies obtained by performing a GRID computation on a suitable data set. The FILO procedure was tested on a set of nine HIV-1 protease inhibitors with known crystal structures. The results of FILO consist of the optimal molecular interaction field of a putative new ligand with optimal binding affinity. The final FILO model yields R ² and R ² _CV values of 0.993 and 0.936, respectively, and finds eight negative and four positive interaction nodes for the OH probe taken as an example. The eight H bonding interactions pointed out by FILO identified well the binding site AA-residues Gly A27, Asp A29, water 501, Gly B48 and Asp A25 of HIV-1 protease.     

胰蛋白酶和苯酰氨类抑制剂结合自由能的预测

用基于线性响应近似的自由能预测方法计算胰蛋白酶和苯酰氨类抑制剂的结合 自由能。计算结果表明,单参数,双参数和三参数模型具有相似的线性回归系数, 但三参数和双参数模型的交互验证回归系数要明显优于单参数模型。从预测能力来 看,双参数模型和三参数模型都能够很好地预测测试集中抑制剂的结合自由能,其 中双参数模型预测的结果要略优于三参数模型的预测结果。对测试集中的抑制剂, 双参数模型预测得到的预测自由能和实际自由能之间平均绝对误差仅为1.15 kJ/mol。自由能计算模型以及分子动力学轨迹能很好地解释抑制剂结构和活性的 关系,为药物设计提供了重要的结构信息。