Combined comparative molecular field analysis,comparative molecular similarity indices analysis,molecular docking and molecular dynamics studies of histone deacetylase 6 inhibitors

Human histone deacetylase isoform 6 (HDAC6) has been shown to have an immense role in cell motility and aggresome formation and is being an attractive selective target for the treatment of multiple tumour types and neurodegenerative conditions. The discovery of selective HDAC6 inhibitors with new chemical functionalities is therefore of utmost interest to researchers. In order to examine the structural requirements for HDAC6‐specific inhibitors and to derive predictive model which can be used for designing new selective HDAC6 inhibitors, a three‐dimensional quantitative structure–activity relationship study was carried out on a diverse set of ligands using common feature‐based pharmacophore alignment followed by employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. The models displayed high correlation of 0.978 and 0.991 for best CoMFA and CoMSIA models, respectively, and a good statistical significance. The model could be used for predicting activities of the test set compounds as well as for deriving useful information regarding steric, electrostatic, hydrophobic properties of the molecules used in this study. Further, the training and test set molecules were docked into the HDAC6 binding site and molecular dynamics was carried out to suggest structural requirements for design of new inhibitors.     

作用于秋水仙碱结合位点的微管蛋白抑制剂药效团的构建

目的:构建作用于秋水仙碱结合位点的微管蛋白抑制剂药效团模型;初步分析该类抑制剂与靶点的作用方式。方法:使用Discovery Studio软件中的HypoGen模块对训练集进行药效团模型的构建。结果:最佳药效团模型的线性回归相关系数最高(0.981),包含1个氢键给体和4个疏水中心,利用测试集验证了该药效团模型的活性预测能力;通过分子与活性位点的对接得到了活性最好的两个化合物与此结合位点的具体作用方式。结论:得到的药效团模型具有较好的预测能力,有利于设计和开发新型微管蛋白抑制剂。     

Combined chemical feature-based assessment and Bayesian model studies to identify potential inhibitors for Factor Xa

In our study, we have described chemical feature-based 3D QSAR pharmacophore models with help of known inhibitors of Factor Xa (FXa). The best model, Hypo1, has validated by various techniques to prove its robustness and statistical significance. The well validated Hypo1 was used as 3D query in the virtual screening to retrieve potential leads for FXa inhibition. The hit molecules were sort out by applying drug-like filters and molecular docking. Bayesian model was developed using training set compounds which provides molecular feature that are favoring or not favoring for FXa inhibition.     

Pharmacophore modeling, virtual screening, and molecular docking studies for discovery of novel Carbonic anhydrase IX inhibitors

Carbonic anhydrase IX (CA IX) is considered as a potential target for cancer therapy. In order to identify new scaffolds compounds and use them for designing novel CA IX inhibitor, herein 3D pharmacophore hypotheses had been established. Hypo 1, the best pharmacophore hypothesis, which had highest cost difference, best correlation coefficient, and lowest root mean square deviation, was validated by test set and Fischer’s randomization methods, and it was used for chemical database virtual screening. The hit compounds were filtered by Lipinski’s rule of five and ADMET properties. Finally, 100 hits with good estimated activity values were used for docking studies. These hits may act as novel leads for CA IX inhibitors designing.     

The imidazolidone analogs as phospholipase D1 inhibitors: analysis of the three-dimensional quantitative structure–activity relationship

Phospholipase D1 (PLD1) is one of the important enzymes in cell proliferation, apoptosis, and tumor progression. Quantitative structure–activity relationship (QSAR) analysis of PLD1 inhibitors was performed to obtain a good predictive model for providing structural rationale for the activity of inhibitors. The 3D-QSAR analysis was carried out on 31 imidazolidone-based analogs of PLD1 inhibitors in the training set. The molecular field analysis (MFA) with G/PLS method was used to generate a statistically significant 3D-QSAR model (r ²?=?0.930) based on molecular field generated by electrostatic and steric probes. The QSAR model was validated using leave-one-out cross-validation, bootstrapping and randomization methods, and finally with an external test set comprising nine inhibitors. The analysis of the best MFA model provided insights into possible modifications for the rational design of imidazolidone analogs as PLD1 inhibitors for better activity.     

Three-dimensional quantitative structure-activity relationship analysis of a set of Plasmodium falciparum dihydrofolate reductase inhibitors using a pharmacophore generation approach

A 3D pharmacophore model able to quantitatively predict inhibition constants was derived for a series of inhibitors of Plasmodium falciparum dihydrofolate reductase (PfDHFR), a validated target for antimalarial therapy. The data set included 52 inhibitors, with 23 of these comprising the training set and 29 an external test set. The activity range, expressed as Ki, of the training set molecules was from 0.3 to 11 300 nM. The 3D pharmacophore, generated with the HypoGen module of Catalyst 4.7, consisted of two hydrogen bond donors, one positive ionizable feature, one hydrophobic aliphatic feature, and one hydrophobic aromatic feature and provided a 3D-QSAR model with a correlation coefficient of 0.954. Importantly, the type and spatial location of the chemical features encoded in the pharmacophore were in full agreement with the key binding interactions of PfDHFR inhibitors as previously established by molecular modeling and crystallography of enzyme-inhibitor complexes. The model was validated using several techniques, namely, Fisher's randomization test using CatScramble, leave-one-out test to ensure that the QSAR model is not strictly dependent on one particular compound of the training set, and activity prediction in an external test set of compounds. In addition, the pharmacophore was able to correctly classify as active and inactive the dihydrofolate reductase and aldose reductase inhibitors extracted from the MDDR database, respectively. This test was performed in order to challenge the predictive ability of the pharmacophore with two classes of inhibitors that target very different binding sites. Molecular diversity of the data sets was finally estimated by means of the Tanimoto approach. The results obtained provide confidence for the utility of the pharmacophore in the virtual screening of libraries and databases of compounds to discover novel PfDHFR inhibitors.     

Novel chemical scaffolds of the tumor marker AKR1B10 inhibitors discovered by 3D QSAR pharmacophore modeling

Aim:

Recent evidence suggests that aldo-keto reductase family 1 B10 (AKR1B10) may be a potential diagnostic or prognostic marker of human tumors, and that AKR1B10 inhibitors offer a promising choice for treatment of many types of human cancers. The aim of this study was to identify novel chemical scaffolds of AKR1B10 inhibitors using in silico approaches.

Methods:

The 3D QSAR pharmacophore models were generated using HypoGen. A validated pharmacophore model was selected for virtual screening of 4 chemical databases. The best mapped compounds were assessed for their drug-like properties. The binding orientations of the resulting compounds were predicted by molecular docking. Density functional theory calculations were carried out using B3LYP. The stability of the protein-ligand complexes and the final binding modes of the hit compounds were analyzed using 10 ns molecular dynamics (MD) simulations.

Results:

The best pharmacophore model (Hypo 1) showed the highest correlation coefficient (0.979), lowest total cost (102.89) and least RMSD value (0.59). Hypo 1 consisted of one hydrogen-bond acceptor, one hydrogen-bond donor, one ring aromatic and one hydrophobic feature. This model was validated by Fischer''s randomization and 40 test set compounds. Virtual screening of chemical databases and the docking studies resulted in 30 representative compounds. Frontier orbital analysis confirmed that only 3 compounds had sufficiently low energy band gaps. MD simulations revealed the binding modes of the 3 hit compounds: all of them showed a large number of hydrogen bonds and hydrophobic interactions with the active site and specificity pocket residues of AKR1B10.

Conclusion:

Three compounds with new structural scaffolds have been identified, which have stronger binding affinities for AKR1B10 than known inhibitors.     

Ligand‐based 3D‐QSAR Studies of Physostigmine Analogues as Acetylcholinesterase Inhibitors

Natural alkaloid Physostigmine is one of the most potent pseudo‐irreversible inhibitor of Acetylcholinesterase. It was found to accelerate long‐term memory process, but due to its short half life and variable bioavailability, has inconsistent clinical efficacy. 3D‐QSAR studies based on the comparative molecular field analysis and comparative molecular similarity indices analysis were applied to a set of 40 Physostigmine derivatives which are divided into two classes: A and B. The study was conducted to obtain a highly reliable and extensive dynamic QSAR model based on alignment procedure with co‐crystallized Ganstigmine as template. The strategy yielded significant 3D‐QSAR models with the cross‐validated q² values 0.762 and 0.754 for comparative molecular field analysis and comparative molecular similarity indices analysis, respectively. Resulted models were validated by external set of eight compounds yielding high correlation coefficient r² values of 0.730 and 0.720 for comparative molecular field analysis and comparative molecular similarity indices analysis, respectively. Furthermore, the analysis of comparative molecular field analysis and comparative molecular similarity indices analysis contour maps within the active site of AChE were conducted in order to understand the interactions between the receptor and the Physostigmine derivatives. This study will facilitate the rational design of more potent Physostigmine compounds which might have better activity and reduce toxicity for the treatment of Alzheimer disease.