3-D QSAR studies on histone deacetylase inhibitors. A GOLPE/GRID approach on different series of compounds

Docking simulation and three-dimensional quantitative structure-activity relationships (3D-QSARs) analyses were conducted on four series of HDAC inhibitors. The studies were performed using the GRID/GOLPE combination using structure-based alignment. Twelve 3-D QSAR models were derived and discussed. Compared to previous studies on similar inhibitors, the present 3-D QSAR investigation proved to be of higher statistical value, displaying for the best global model r2, q2, and cross-validated SDEP values of 0.94, 0.83, and 0.41, respectively. A comparison of the 3-D QSAR maps with the structural features of the binding site showed good correlation. The results of 3D-QSAR and docking studies validated each other and provided insight into the structural requirements for anti-HDAC activity. To our knowledge this is the first 3-D QSAR application on a broad molecular diversity training set of HDACIs.     

A structure-activity relationship study of catechol-O-methyltransferase inhibitors combining molecular docking and 3D QSAR methods

A panel of 92 catechol-O-methyltransferase (COMT) inhibitors was used to examine the molecular interactions affecting their biological activity. COMT inhibitors are used as therapeutic agents in the treatment of Parkinson's disease, but there are limitations in the currently marketed compounds due to adverse side effects. This study combined molecular docking methods with three-dimensional structure-activity relationships (3D QSAR) to analyse possible interactions between COMT and its inhibitors, and to incite the design of new inhibitors. Comparative molecular field analysis (CoMFA) and GRID/GOLPE models were made by using bioactive conformations from docking experiments, which yielded q2 values of 0.594 and 0.636, respectively. The docking results, the COMT X-ray structure, and the 3D QSAR models are in agreement with each other. The models suggest that an interaction between the inhibitor's catechol oxygens and the Mg2+ ion in the COMT active site is important. Both hydrogen bonding with Lys144, Asn170 and Glu199, and hydrophobic contacts with Trp38, Pro174 and Leu198 influence inhibitor binding. Docking suggests that a large R1 substituent of the catechol ring can form hydrophobic contacts with side chains of Val173, Leu198, Met201 and Val203 on the COMT surface. Our models propose that increasing steric volume of e.g. the diethylamine tail of entacapone is favourable for COMT inhibitory activity.     

Development of biologically active compounds by combining 3D QSAR and structure-based design methods

One of the major challenges in computational approaches to drug design is the accurate prediction of the binding affinity of novel biomolecules. In the present study an automated procedure which combines docking and 3D-QSAR methods was applied to several drug targets. The developed receptor-based 3D-QSAR methodology was tested on several sets of ligands for which the three-dimensional structure of the target protein has been solved – namely estrogen receptor, acetylcholine esterase and protein-tyrosine-phosphatase 1B. The molecular alignments of the studied ligands were determined using the docking program AutoDock and were compared with the X-ray structures of the corresponding protein-ligand complexes. The automatically generated protein-based ligand alignment obtained was subsequently taken as basis for a comparative field analysis applying the GRID/GOLPE approach. Using GRID interaction fields and applying variable selection procedures, highly predictive models were obtained. It is expected that concepts from receptor-based 3D QSAR will be valuable tools for the analysis of high-throughput screening as well as virtual screening data     

A comparative molecular field analysis of cytochrome P450 2A5 and 2A6 inhibitors

Structure-activity relationships of 23 P450 2A5 and 2A6 inhibitors were analysed using the CoMFA [1] and GOLPE/GRID with smart region definition (SRD) [2]. The predictive power of the resulting models was validated using five compounds not belonging to the model set. All models have high internal and external predictive power and resulting 3D-QSAR models are supporting each other. Both Sybyl and GOLPE highlight properties near lactone moiety to be important for 2A5 and 2A6 inhibition. Another important feature for pIC₅₀ was the size of the substituent in the 7-positon of coumarin. The models suggest that the 2A5 binding site is larger that that of 2A6 due to larger steric regions in the CoMFA coefficient maps and corresponding GOLPE maps. In addition, the maps reveal that 2A6 disfavours negative charge near the lactone moiety of coumarin.     

Insight into the structural requirements of narlaprevir-type inhibitors of NS3/NS4A protease based on HQSAR and molecular field analyses

In the life cycle of hepatitis C virus (HCV), NS3/NS4A protease has been proved to play a vital role in the replication of the HCV virus. Narlaprevir and its derivatives, the inhibitors of NS3/NS4A, would be potentially developed as important anti-HCV drugs in the future. In this study, quantitative structure-activity relationship (QSAR) analyses for 190 narlaprevir derivatives were conducted using comparative molecular field analysis (CoMFA), comparative molecular indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR) techniques. Both of the best CoMFA and HQSAR models showed statistical significance for the training set and good predictive accuracy for the test set, which strongly manifested the robustness of the CoMFA and HQSAR models. The CoMFA contour maps and the HQSAR contribution maps were both presented. Furthermore, based on the essential factors for ligand binding derived from the QSAR models, sixteen new derivatives were designed and some of them showed higher inhibitory activities confirmed by our models and molecular docking studies. General speaking, this study provides useful suggestions for the design of potential anti-HCV drugs.     

3-D QSAR for intrinsic activity of 5-HT1A receptor ligands by the method of comparative molecular field analysis

The affinity of a ligand for a receptor is usually expressed in terms of the dissociation constant (K_i) of the drug-receptor complex, conveniently measured by the inhibition of radioligand binding. However, a ligand can be an antagonist, a partial agonist, or a full agonist, a property largely independent of its receptor affinity. This property can be quantitated as intrinsic activity (1A), which can range from 0 for a full antagonist to 1 for a full agonist. Although quantitative structure–activity relationship (QSAR) methods have been applied to the prediction of receptor affinity with considerable success, the prediction of IA, even qualitatively, has rarely been attempted. Because most traditional QSAR methods are limited to congeneric series, and there are often major structural differences between agonists and antagonists, this lack of success in predicting IA is understandable. To overcome this limitation, we used the method of comparative molecular field analysis (CoMFA), which, unlike traditional Hansch analysis, permits the inclusion of structurally dissimilar compounds in a single QSAR model. A structurally diverse set of 5-hydroxytryptamine_1A (5-HT_1A) receptor ligands, with literature IA data (determined by the inhibition of 5-HT sensitive forskolin-stimulated adenylate cyclase), was used to develop a 3-D QSAR model correlating intrinsic activity with molecular structure properties of 5HT_1A receptor ligands. This CoMFA model had a crossvalidated r² of 0.481, five components and final conventional r² of 0.943. The receptor model suggests that agonist and antagonist ligands can share parts of a common binding site on the receptor, with a primary agonist binding region that is also occupied by antagonists and a secondary binding site accommodating the excess bulk present in the sidechains of many antagonists and partial agonists. The CoMFA steric field graph clearly shows that agonists tend to be “flatter” (more coplanar) than antagonists, consistent with the difference between the 5-HT_1A agonist and antagonist pharmacophores proposed by Hibert and coworkers. The CoMFA electrostatic field graph suggests that, in the region surrounding the essential protonated aliphatic amino group, the positive molecular electrostatic potential may be weaker in antagonists as compared to agonists. Together, the steric and electrostatic maps suggest that in the secondary binding site region increased hydrophobic binding may enhance antagonist activity. These results demonstrate that CoMFA is capable of generating a statistically crossvalidated 3-D QSAR model that can successfully distinguish between agonist and antagonist 5-HT_1A ligands. To the best of our knowledge, this is the first time this or any other QSAR method has been successfully applied to the correlation of structure with IA rather than potency or affinity. The analysis has suggested various structural features associated with agonist and antagonist behaviors of 5-HT_1A ligands and thus should assist in the future design of drugs that act via 5-HT_1A receptors. © 1993 John Wiley & Sons, Inc.     

Structure-based 3D QSAR and design of novel acetylcholinesterase inhibitors

The paper describes the construction, validation and application of a structure-based 3D QSAR model of novel acetylcholinesterase (AChE) inhibitors. Initial use was made of four X-ray structures of AChE complexed with small, non-specific inhibitors to create a model of the binding of recently developed aminopyridazine derivatives. Combined automated and manual docking methods were applied to dock the co-crystallized inhibitors into the binding pocket. Validation of the modelling process was achieved by comparing the predicted enzyme-bound conformation with the known conformation in the X-ray structure. The successful prediction of the binding conformation of the known inhibitors gave confidence that we could use our model to evaluate the binding conformation of the aminopyridazine compounds. The alignment of 42 aminopyridazine compounds derived by the docking procedure was taken as the basis for a 3D QSAR analysis applying the GRID/GOLPE method. A model of high quality was obtained using the GRID water probe, as confirmed by the cross-validation method (q² _LOO=0.937, q² _{L50% O}=0.910). The validated model, together with the information obtained from the calculated AChE-inhibitor complexes, were considered for the design of novel compounds. Seven designed inhibitors which were synthesized and tested were shown to be highly active. After performing our modelling study the X-ray structure of AChE complexed with donepezil, an inhibitor structurally related to the developed aminopyirdazines, has been made available. The good agreement found between the predicted binding conformation of the aminopyridazines and the one observed for donepezil in the crystal structure further supports our developed model.     

3D QSAR and molecular docking studies of benzimidazole derivatives as hepatitis C virus NS5B polymerase inhibitors

The urgent need for novel HCV antiviral agents has provided an impetus for understanding the structural requisites of NS5B polymerase inhibitors at the molecular level. Toward this objective, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) of 67 HCV NS5B polymerase inhibitors were performed using two methods. First, ligand-based 3D QSAR studies were performed based on the lowest energy conformations employing the atom fit alignment method. Second, receptor-based 3D QSAR models were derived from the predicted binding conformations obtained by docking all NS5B inhibitors at the allosteric binding site of NS5B (PDB ID: 2dxs). Results generated from the ligand-based model were found superior (r2cv values of 0.630 for CoMFA and 0.668 for CoMSIA) to those obtained by the receptor-based model (r2cv values of 0.536 and 0.561 for CoMFA and CoMSIA, respectively). The predictive ability of the models was validated using a structurally diversified test set of 22 compounds that had not been included in a preliminary training set of 45 compounds. The predictive r2 values for the ligand-based CoMFA and CoMSIA models were 0.734 and 0.800, respectively, while the corresponding predictive r2 values for the receptor-based CoMFA and CoMSIA models were 0.538 and 0.639, respectively. The greater potency of the tryptophan derivatives over that of the tyrosine derivatives was interpreted based on CoMFA steric and electrostatic contour maps. The CoMSIA results revealed that for a NS5B inhibitor to have appreciable inhibitory activity it requires hydrogen bond donor and acceptor groups at the 5-position of the indole ring and an R substituent at the chiral carbon, respectively. Interpretation of the CoMFA and CoMSIA contour maps in context of the topology of the allosteric binding site of NS5B provided insight into NS5B-inhibitor interactions. Taken together, the present 3D QSAR models were found to accurately predict the HCV NS5B polymerase inhibitory activity of structurally diverse test set compounds and to yield reliable clues for further optimization of the benzimidazole derivatives in the data set.     

3D QSAR selectivity analyses of carbonic anhydrase inhibitors: insights for the design of isozyme selective inhibitors

A 3D QSAR selectivity analysis of carbonic anhydrase (CA) inhibitors using a data set of 87 CA inhibitors is reported. After ligand minimization in the binding pockets of CA I, CA II, and CA IV isoforms, selectivity CoMFA and CoMSIA 3D QSAR models have been derived by taking the affinity differences (DeltapKi) with respect to two CA isozymes as independent variables. Evaluation of the developed 3D QSAR selectivity models allows us to determine amino acids in the respective CA isozymes that possibly play a crucial role for selective inhibition of these isozymes. We further combined the ligand-based 3D QSAR models with the docking program AUTODOCK in order to screen for novel CA inhibitors. Correct binding modes are predicted for various CA inhibitors with respect to known crystal structures. Furthermore, in combination with the developed 3D QSAR models we could successfully estimate the affinity of CA inhibitors even in cases where the applied scoring function failed. This novel strategy to combine AUTODOCK poses with CoMFA/CoMSIA 3D QSAR models can be used as a guideline to assess the relevance of generated binding modes and to accurately predict the binding affinity of newly designed CA inhibitors that could play a crucial role in the treatment of pathologies such as tumors, obesity, or glaucoma.     

3D QSAR studies on protein tyrosine phosphatase 1B inhibitors: comparison of the quality and predictivity among 3D QSAR models obtained from different conformer-based alignments

A set of 65 flexible peptidomimetic competitive inhibitors (52 in the training set and 13 in the test set) of protein tyrosine phosphatase 1B (PTP1B) has been used to compare the quality and predictive power of 3D quantitative structure-activity relationship (QSAR) comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for the three most commonly used conformer-based alignments, namely, cocrystallized conformer-based alignment (CCBA), docked conformer-based alignment (DCBA), and global minima energy conformer-based alignment (GMCBA). These three conformers of 5-[(2S)-2-({(2S)-2-[(tert-butoxycarbonyl)amino]-3-phenylpropanoyl}amino)3-oxo-3-pentylamino)propyl]-2-(carboxymethoxy)benzoic acid (compound number 66) were obtained from the X-ray structure of its cocrystallized complex with PTP1B (PDB ID: 1JF7), its docking studies, and its global minima by simulated annealing. Among the 3D QSAR models developed using the above three alignments, the CCBA provided the optimal predictive CoMFA model for the training set with cross-validated r2 (q2)=0.708, non-cross-validated r2=0.902, standard error of estimate (s)=0.165, and F=202.553 and the optimal CoMSIA model with q2=0.440, r2=0.799, s=0.192, and F=117.782. These models also showed the best test set prediction for the 13 compounds with predictive r2 values of 0.706 and 0.683, respectively. Though the QSAR models derived using the other two alignments also produced statistically acceptable models in the order DCBA>GMCBA in terms of the values of q2, r2, and predictive r2, they were inferior to the corresponding models derived using CCBA. Thus, the order of preference for the alignment selection for 3D QSAR model development may be CCBA>DCBA>GMCBA, and the information obtained from the CoMFA and CoMSIA contour maps may be useful in designing specific PTP1B inhibitors.     

Structural investigations of anthranilimide derivatives by CoMFA and CoMSIA 3D-QSAR studies reveal novel insight into their structures toward glycogen phosphorylase inhibition

In the present work, three-dimensional quantitative structure–activity relationship (3-D QSAR) studies on a set of 70 anthranilimide compounds has been performed using docking-based as well as substructure-based molecular alignments. This resulted in the selection of more statistically relevant substructure-based alignment for further studies. Further, molecular models with good predictive power were derived using CoMFA (r ^2?=?0.997; Q ^2?=?0.578) and CoMSIA (r ^2?=?0.976; Q ^2?=?0.506), for predicting the biological activity of new compounds. The so-developed contour plots identified several key features of the compounds explaining wide activity ranges. Based on the information derived from the CoMFA contour maps, novel leads were proposed which showed better predicted activity with respect to the already reported systems. Thus, the present study not only offers a highly significant predictive QSAR model for anthranilimide derivatives as glycogen phosphorylase (GP) inhibitors which can eventually assist and complement the rational drug-design attempts, but also proposes a highly predictive pharmacophore model as a guide for further development of selective and more potent GP inhibitors as anti-diabetic agents.     

Development of a unique 3D interaction model of endogenous and synthetic peripheral benzodiazepine receptor ligands

Different classes of Peripheral-type Benzodiazepine Receptor (PBR) ligands were examined and common structural elements were detected and used to develop a rational binding model based on energetically allowed ligand conformations. Two lipophilic regions and one electrostatic interaction site are essential features for high affinity ligand binding, while a further lipophilic region plays an important modulator role. A comparative molecular field analysis, performed over 130 PBR ligands by means of the GRID/GOLPE methodology, led to a PLS model with both high fitting and predictive values (r² = 0.898, Q² = 0.761). The outcome from the 3D QSAR model and the GRID interaction fields computed on the putative endogenous PBR ligands DBI (Diazepam Binding Inhibitor) and TTN (Tetracontatetraneuropeptide) was used to identify the amino acids most probably involved in PBR binding. Three amino acids, bearing lipophilic side chains, were detected in DBI (Phe49, Leu47 and Met46) and in TTN (Phe33, Leu31 and Met30) as likely residues underlying receptor binding. Moreover, a qualitative comparison of the molecular electrostatic potentials of DBI, TTN and selected synthetic ligands indicated also similar electronic properties. Convergent results from the modeling studies of synthetic and endogenous ligands suggest a common binding mode to PBRs. This may help the rational design of new high affinity PBR ligands.     

Comparative Molecular Field Analysis （CoMFA） of Curcumin-related Compounds for Anticancer Activity

Three-dimensional （3D） quantitative structure-activity relationship （QSAR） studies of 44 curcumin-related compounds have been carried out based on our previously reported result for their anticancer activity against pancreas cancer Panc-I cells and colon cancer HT-29 cells. The established 3D-QSAR models from the comparative molecular field analysis （CoMFA） in training set showed not only significant statistical quality, but also satisfying predictive ability, with high correlation coefficient values （R12= 0.911, R22= 0.985） and cross-validation coefficient values （q2= 0.580, q22= 0.722）. Based on the CoMFA contour maps, some key structural factors responsible for anticancer activity of these series of compounds were revealed. The results provide some useful theoretical references for understanding the mechanism of action, designing new curcumin-related compounds with anticancer activity and predicting their activities prior to synthesis.     

环氧酮肽蛋白酶体抑制剂的构效关系及基于分子相似性的分子设计

针对56个环氧酮肽衍生物,分别采用比较分子场分析(comparative molecular field analysis,CoMFA)、比较分子相似性形状指数分析(comparative molecular similarity indices analysis,CoMSIA)、Topomer CoMFA、Holo-gram QSAR(HQSAR)以及基于一维和二维描述符的支持向量机(support vector machine,SVM)方法进行了细致的构效关系研究。研究显示:通过引入一维和二维描述符的SVM建模方法,避免了柔性分子在三维构效关系研究中的构象选择和叠合难题,亦可有效避免过拟合现象的发生。所建最优SVM模型的决定系数R2、均方根误差(RMS)、交互验证系数Q2和外部预测R2pred分别为0.681,0.436,0.572和0.641。分析结果显示:电性、拓扑特征、疏水性和分子体积是影响环氧酮肽蛋白酶体抑制活性的主要因素。在此基础上,以活性最高样本分子(CID:42638286的)为模板,基于相似性评价方法对其侧链进行设计,结合Lipinski"5规则"类药性筛选,共得到12个新颖目标分子,且预测活性均达到纳摩尔水平。     

3D QSAR Modeling and Molecular Docking Studies on a Series of Triazole Analogues as Antibacterial Agents

The 3D QSAR analysis using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques is performed on novel nalidixic acid based 1,2,4-triazole derivatives suggested earlier as antibacterial agents. The CoMFA and CoMSIA models employed for a training set of 28 compounds gives reliable values of Q² (0.53 and 0.52, respectively) and R² (0.79 and 0.85, respectively). The contour maps produced by the CoMFA and CoMSIA models are used to determine a three-dimensional quantitative structure-activity relationship. Based on the 3D QSAR contours new molecules with high predicted activities are designed. In addition, surflex-docking is performed to confirm the stability of predicted molecules in the receptor.