Docking-based CoMFA and CoMSIA study of azaindole carboxylic acid derivatives as promising HIV-1 integrase inhibitors

Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed based on a series of azaindole carboxylic acid derivatives that had previously been reported as promising HIV-1 integrase inhibitors. Docking studies to explore the binding mode were performed based on the highly active molecule 36. The best docked conformation of molecule 36 was used as template for alignment. The comparative molecular field analysis (CoMFA) model (including steric and electrostatic fields) yielded the cross validation q ² = 0.655, non-cross validation r ² = 0.989 and predictive r ² _pred = 0.979. The best comparative molecular similarity indices analysis (CoMSIA) model (including steric, electrostatic, hydrophobic and hydrogen-bond acceptor fields) yielded the cross validation q ² = 0.719, non-cross validation r ² = 0.992 and predictive r ² _pred = 0.953. A series of new azaindole carboxylic acid derivatives were designed and the HIV-1 integrase inhibitory activities of these designed compounds were predicted based on the CoMFA and CoMSIA models.     

3D-QSAR (CoMFA,CoMSIA, HQSAR and topomer CoMFA), MD simulations and molecular docking studies on purinylpyridine derivatives as B-Raf inhibitors for the treatment of melanoma cancer

As per the World Health Organization (WHO), cancer is the second most leading cause of death after cardiovascular diseases in worldwide with around 9.88 million total new cases and 1.08 million were observed due to skin cancer in 2018. Amongst two types of skin cancer, progression of melanoma cancer is increasing day by day due to the environmental changes than non-melanoma cancer. Most of B-Raf mutation, specifically B-RafV600E, is responsible for the progression of the melanoma cancer. Here, various 3D-QSAR techniques like comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), molecular hologram QSAR (HQSAR) and topomer CoMFA were used to design novel B-Raf inhibitors by using 28 synthetic B-Raf inhibitors. Except for topomer CoMFA model, remaining models were generated by three different alignment methods in which distil-based alignment method was found best and gave prominent statistical values. After performing N-fold statistical validation, in CoMFA, q², r² and r²_pred values were found to be 0.638, 0.969 and 0.848, respectively. Similarly, q², r² and r²_pred values were found to be 0.796, 0.978 and 0.891 in CoMSIA (SHD) and 0.761, 0.973 and 0.852 in CoMSIA (SH) by N-fold statistical validation. In HQSAR analysis, statistical values were found for q² as 0.984, r² as 0.999 and r²_pred as 0.634 with 97 as best hologram length (BHL). The results of topomer CoMFA showed the q² value of 0.663 and the r² value of 0.967. Important features of purinylpyridine were identified by contour map analysis of all 3D-QSAR techniques, which could be useful to design the novel molecules as B-Raf inhibitors for the treatment of melanoma cancer.

     

Insight into the structural requirements of benzimidazole derivatives as interleukin‐2 inducible t‐cell kinase inhibitors by computational explorations

In the present work, a set of ligand‐ and receptor‐based 3D‐QSAR models were developed to explore the structure–activity relationship of 109 benzimidazole‐based interleukin‐2‐inducible T‐cell kinase (ITK) inhibitors. In order to reveal the requisite 3D structural features impacting the biological activities, a variety of in silico modeling approaches including the comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), docking, and molecular dynamics were applied. The results showed that the ligand‐based CoMFA model (Q² = 0.552, R²_ncv = 0.908, R²_pred = 0.787, SEE = 0.252, SEP = 0.558) and CoMSIA model (Q² = 0.579, R²_ncv = 0.914, R²_pred = 0.893, SEE = 0.240, SEP = 0.538) were superior to other models with greater predictive power. In addition, a combined analysis between the 3D contour maps and docking results showed that: (1) Compounds with bulky or hydrophobic substituents near ring D and electropositive or hydrogen acceptor groups around rings C and D could increase the activity. (2) The key amino acids impacting the receptor–ligand interactions in the binding pocket are Met438, Asp500, Lys391, and Glu439. The results obtained from this work may provide helpful guidelines in design of novel benzimidazole analogs as inhibitors of ITK. © 2013 Wiley Periodicals, Inc.     

Computational studies on N-phenyl pyrrole derivatives as MmpL3 inhibitors in Mycobacterium tuberculosis

The fight against tuberculosis (TB) is a time immemorial one and the emergence of new drug resistant strains of Mycobacterium tuberculosis keeps throwing new challenges to the scientific community immersed in finding mechanisms to control this dreaded disease. Computer aided drug designing (CADD) is one of the several approaches that can assist in identifying the potent actives against Mycobacterium. In this work, a series of 109 known Mycobacterial membrane proteins large 3 (MmpL3) inhibitors were pooled and atom based 3D QSAR analysis was performed to understand the structural features essential for inhibitory activity against the MmpL3, known to be a key player in transporting substances critical for cell wall integrity of Mycobacterium. The data set employed was randomly split into training set and test set molecules. The training set of 74 molecules was used to derive CoMFA and CoMSIA models that were statistically reliable (CoMFA: q²_loo = 0.53; r²_ncv = 0.93 and CoMSIA: q²_loo = 0.60; r²_ncv = 0.93). The derived models also exhibited good external predictive ability (CoMFA: r²_pred = 0.78 and CoMSIA: r²_pred = 0.79). The results are quite encouraging and information derived from these analyses was applied to design new molecules. The designed molecule showed appreciable predicted activity values and reasonably good ADMET profile. The strategy used in designing new molecules can be pursued in the hunt for new chemical entities targeting MmpL3, expanding the existing arsenal against TB.     

3D-QSAR and molecular docking study of LRRK2 kinase inhibitors by CoMFA and CoMSIA methods

Three-dimensional quantitative structure–activity relationship (3D-QSAR) modelling was conducted on a series of leucine-rich repeat kinase 2 (LRRK2) antagonists using CoMFA and CoMSIA methods. The data set, which consisted of 37 molecules, was divided into training and test subsets by using a hierarchical clustering method. Both CoMFA and CoMSIA models were derived using a training set on the basis of the common substructure-based alignment. The optimum PLS model built by CoMFA and CoMSIA provided satisfactory statistical results (q² = 0.589 and r² = 0.927 and q² = 0.473 and r² = 0.802, respectively). The external predictive ability of the models was evaluated by using seven compounds. Moreover, an external evaluation set with known experimental data was used to evaluate the external predictive ability of the porposed models. The statistical parameters indicated that CoMFA (after region focusing) has high predictive ability in comparison with standard CoMFA and CoMSIA models. Molecular docking was also performed on the most active compound to investigate the existence of interactions between the most active inhibitor and the LRRK2 receptor. Based on the obtained results and CoMFA contour maps, some features were introduced to provide useful insights for designing novel and potent LRRK2 inhibitors.     

Experimental and computational correlation and prediction on herbicide resistance for acetohydroxyacid synthase mutants to Bispyribac

Bispyribac is a widely used herbicide that targets the acetohydroxyacid synthase(AHAS) enzyme.Mutations in AHAS have caused serious herbicide resistance that threatened the continued use of the herbicide.So far,a unified model to decipher herbicide resistance in molecular level with good prediction is still lacking.In this paper,we have established a new QSAR method to construct a prediction model for AHAS mutation resistance to herbicide Bispyribac.A series of AHAS mutants concerned with the herbicide resistance were constructed,and the inhibitory properties of Bispyribac against these mutants were measured.The 3D-QSAR method has been transformed to process the AHAS mutants and proposed as mutation-dependent biomacromolecular QSAR(MB-QSAR).The excellent correlation between experimental and computational data gave the MB-QSAR/CoMFA model(q2=0.615,r2=0.921,r2 pred=0.598) and the MB-QSAR/CoMSIA model(q2=0.446,r2=0.929,r2 pred=0.612),which showed good prediction for the inhibition properties of Bispyribac against AHAS mutants.Such MB-QSAR models,containing the three-dimensional molecular interaction diagram,not only disclose to us for the first time the detailed three-dimensional information about the structure-resistance relationships,but may also provide further guidance to resistance mutation evolution.Also,the molecular interaction diagram derived from MB-QSAR models may aid the resistance-evading herbicide design.     

Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors

Obesity is one of the most provoking health burdens in the developed countries. One of the strategies to prevent obesity is the inhibition of pancreatic lipase enzyme. The aim of this study was to build QSAR models for natural lipase inhibitors by using the Monte Carlo method. The molecular structures were represented by the simplified molecular input line entry system (SMILES) notation and molecular graphs. Three sets – training, calibration and test set of three splits – were examined and validated. Statistical quality of all the described models was very good. The best QSAR model showed the following statistical parameters: r² = 0.864 and Q² = 0.836 for the test set and r² = 0.824 and Q² = 0.819 for the validation set. Structural attributes for increasing and decreasing the activity (expressed as pIC₅₀) were also defined. Using defined structural attributes, the design of new potential lipase inhibitors is also presented. Additionally, a molecular docking study was performed for the determination of binding modes of designed molecules.     

Holographic quantitative structure-activity relationship for prediction of the toxicity of polybrominated diphenyl ether congeners

Polybrominated diphenyl ether congeners (PBDEs) might activate the AhR (aromatic hydrocarbon receptor) signal transduction, and thus might have an adverse effect on the health of humans and wildlife. Because of the limited experimental data, it is important and necessary to develop structure-based models for prediction of the toxicity of the compounds. In this study, a new molecular structure representation, molecular hologram, was employed to investigate the quantitative relationship between toxicity and molecular structures for 18 PBDEs. The model with the significant correlation and robustness (r ² = 0.991, q ² _LOO = 0.917) was developed. To verify the robustness and prediction capacity of the derived model, 14 PBDEs were randomly selected from the database as the training set, while the rest were used as the test set. The results generated under the same modeling conditions as the optimal model are as follows: r ² = 0.988, q ² _LOO = 0.598, r ² _pred = 0.955, and RMSE (root-mean-square of errors) = 0.155, suggesting the excellent ability of the derived model to predict the toxicity of PBDEs. Furthermore, the structural features and molecular mechanism related to the toxicity of PBDEs were explored using HQSAR color coding.     

Drug discovery studies on quinoline-based derivatives as potential antimalarial agents

Molecular modelling studies were performed to identify the essential structural requirements of quinoline-based derivatives for improving their antimalarial activity. The developed CoMFA, CoMSIA and HQSAR models for a training set comprising 37 derivatives showed good statistical significance in terms of internal cross validation (q²) 0.70, 0.69 and 0.80 and non-cross validation (r²) 0.80, 0.79 and 0.80. Also, the predicted r² values (r²_pred) of 0.63, 0.61 and 0.72 for a test set consisting of 12 compounds suggested significant predicting ability of the models. Structural features were correlated in terms of steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor interactions. Furthermore, the bioactive conformation was explored and explained by docking compounds #28, 32 and 40 into the active binding site of lactate dehydrogenase of Plasmodium falciparum. The QSAR models, contour map and docking binding affinity obtained could be successfully utilized as a guiding tool for the design and discovery of novel quinoline-based derivatives with potent antimalarial activity.     

Docking-based 3D-QSAR study of pyridyl aminothiazole derivatives as checkpoint kinase 1 inhibitors

Checkpoint kinase 1 (Chk1) is a promising target for the design of novel anticancer agents. In the present work, molecular docking simulations and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on pyridyl aminothiazole derivatives as Chk1 inhibitors. AutoDock was used to determine the probable binding conformations of all the compounds inside the active site of Chk1. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were developed based on the docking conformations and alignments. The CoMFA model produced statistically significant results with a cross-validated correlation coefficient (q²) of 0.608 and a coefficient of determination (r²) of 0.972. The reliable CoMSIA model with q² of 0.662 and r² of 0.970 was obtained from the combination of steric, electrostatic and hydrogen bond acceptor fields. The predictive power of the models were assessed using an external test set of 14 compounds and showed reasonable external predictabilities (r²_pred) of 0.668 and 0.641 for CoMFA and CoMSIA models, respectively. The models were further evaluated by leave-ten-out cross-validation, bootstrapping and progressive scrambling analyses. The study provides valuable information about the key structural elements that are required in the rational design of potential drug candidates of this class of Chk1 inhibitors.     

A combined CoMFA and CoMSIA 3D-QSAR study of benzamide type antibacterial inhibitors of the FtsZ protein in drug-resistant Staphylococcus aureus

A major problem today is bacterial resistance to antibiotics and the small number of new therapeutic agents approved in recent years. The development of new antibiotics capable of acting on new targets is urgently required. The filamenting temperature-sensitive Z (FtsZ) bacterial protein is a key biomolecule for bacterial division and survival. This makes FtsZ an attractive new pharmacological target for the development of antibacterial agents. There have been several attempts to develop ligands able to inhibit FtsZ. Despite the large number of synthesized compounds that inhibit the FtsZ protein, there are no quantitative structure–activity relationships (QSAR) that allow for the rational design and synthesis of promising new molecules. We present the first 3D-QSAR study of a large and diverse set of molecules that are able to inhibit the FtsZ bacterial protein. We summarize a set of chemical changes that can be made in the steric, electrostatic, hydrophobic and donor/acceptor hydrogen-bonding properties of the pharmacophore, to generate new bioactive molecules against FtsZ. These results provide a rational guide for the design and synthesis of promising new antibacterial agents, supported by the strong statistical parameters obtained from CoMFA (r²_pred = 0.974) and CoMSIA (r²_pred = 0.980) analyses.     

Computational Analysis of CRTh2 receptor antagonist: A Ligand-based CoMFA and CoMSIA approach

CRTh2 receptor is an important mediator of inflammatory effects and has attracted much attention as a therapeutic target for the treatment of conditions such as asthma, COPD, allergic rhinitis and atopic dermatitis. In pursuit of better CRTh2 receptor antagonist agents, 3D-QSAR studies were performed on a series of 2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl) acetic acids. There is no crystal structure information available on this protein; hence in this work, ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed by atom by atom matching alignment using systematic search and simulated annealing methods. The 3D-QSAR models were generated with 10 different combinations of test and training set molecules, since the robustness and predictive ability of the model is very important. We have generated 20 models for CoMFA and 100 models for CoMSIA based on two different alignments. Each model was validated with statistical cut off values such as q² > 0.4, r² > 0.5 and r²_pred > 0.5. Based on better q² and r²_pred values, the best predictions were obtained for the CoMFA (model 5 q² = 0.488, r²_pred = 0.732), and CoMSIA (model 45 q² = 0.525, r²_pred = 0.883) from systematic search conformation alignment. The high correlation between the cross-validated/predicted and experimental activities of a test set revealed that the CoMFA and CoMSIA models were robust. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. The generated models suggest that steric, electrostatic, hydrophobic, H-bond donor and acceptor parameters are important for activity. Our study serves as a guide for further experimental investigations on the synthesis of new CRTh2 antagonist.