Structure-based Quantitative Structure-activity Relationship Research Articles

Molecular Generation, QSAR, and Molecular Dynamic Simulation Studies for Virtual Screening of DNA Polymerase Theta Inhibitors.

The machine learning-based QSAR modeling procedure, molecular generations, and molecular dynamic simulations were applied to virtually screen the DNA polymerase theta inhibitors. The DNA polymerase theta (Polθ or POLQ) is an attractive target for treatments of homologous recombination deficient (such as BRCA deficient) cancers. There are no approved drugs for targeting POLQ, and only one inhibitor is in Phase Ⅱclinical trials; thus, it is necessary to develop novel POLQ inhibitors. To build machine learning models that predict the bioactivities of POLQ inhibitors. To build molecular generation models that generate diverse molecules. To virtually screen the generated molecules by the machine learning models. To analyze the binding modes of the screening results by molecular dynamic simulations. In the present work, 325 inhibitors with POLQ polymerase domain bioactivities were Collected. Two machine learning methods, random forest and deep neural network, were used for building the ligand- and structure-based quantitative structure-activity relationship (QSAR) models. The substructure replacement-based method and transfer learning-based deep recurrent neural network method were used for molecular generations. Molecular docking and consensus QSAR models were carried out for virtual screening. The molecular dynamic simulations and MM/GBSA binding free energy calculation and decomposition were used to further analyze the screening results. The MCC values of the best ligand- and structure-based consensus QSAR models reached 0.651 and 0.361 for the test set, respectively. The machine learning-based docking scores had better-predicted ability to distinguish the highly and weakly active poses than the original docking scores. The 96490 molecules were generated by both molecular generation methods, and 10 molecules were retained by virtual screening. Four favorable interactions were concluded by molecular dynamic simulations. We hope that the screening results and the binding modes are helpful for designing the highly active POLQ polymerase inhibitors and the models of the molecular design workflow can be used as reliable tools for drug design.

Structure-based QSAR models to predict repeat dose toxicity points of departure

Human health risk assessment for environmental chemical exposure is limited by a vast majority of chemicals with little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative structure–activity relationship (QSAR) models based on chemical structure information, can predict hazard in the absence of experimental data. Risk assessment requires identification of a quantitative point-of-departure (POD) value, the point on the dose-response curve that marks the beginning of a low-dose extrapolation. This study presents two sets of QSAR models to predict POD values (PODQSAR) for repeat dose toxicity. For training and validation, a publicly available in vivo toxicity dataset for 3592 chemicals was compiled using the U.S. Environmental Protection Agency’s Toxicity Value database (ToxValDB). The first set of QSAR models predict point-estimates of POD values (PODQSAR) using structural and physicochemical descriptors for repeat dose study types and species combinations. A random forest QSAR model using study type and species as descriptors showed the best performance, with an external test set root mean square error (RMSE) of 0.71 log10-mg/kg/day and coefficient of determination (R2) of 0.53. The second set of QSAR models predict the 95% confidence intervals for PODQSAR using a constructed POD distribution with a mean equal to the median POD value and a standard deviation of 0.5 log10-mg/kg/day, based on previously published typical study-to-study variability that may lead to uncertainty in model predictions. Bootstrap resampling of the pre-generated POD distribution was used to derive point-estimates and 95% confidence intervals for each POD prediction. Enrichment analysis to evaluate the accuracy of PODQSAR showed that 80% of the 5% most potent chemicals were found in the top 20% of the most potent chemical predictions, suggesting that the repeat dose POD QSAR models presented here may help inform screening level human health risk assessments in the absence of other data.

Molecular docking and receptor-based QASR studies on pyrimidine derivatives as potential phosphodiesterase 10A inhibitors

In the present work, molecular docking methodology in combination with quantitative structure–activity relationship (QSAR) was employed to predict the inhibition activity of 87 structurally diverse pyrimidine-based derivatives as phosphodiestrae10A (PDE10A) inhibitors due to their potential in the treatment of schizophrenia. In this method, compounds in their preferred enzyme-docked conformations were utilized to derive interaction-based quantitative descriptors in order to explain reported PDE10A inhibitory activities. Multiple linear regression (MLR), artificial neural network (ANN), and least square support vector regression (LS-SVR) were exploited to developing the structure-based quantitative structure–activity relationship models. Among these models, LS-SVR model showed more satisfactory statistical parameters with regard to both internal (Rtrain = 0.951, Q2 = 0.804, RMSEtrain = 0.494) and external validation (Rtest = 0.941, RMSEtest = 0.549) test results. Information from the most relevant descriptors suggests that incorporating steric effect, electronegativity, and the number of substituted aromatic carbon correlate the activity with structural features of the studied compounds. Molecular docking analysis of the most potent inhibitor explored that hydrogen bond formation and hydrophobicity participated in the binding interaction of PDE10A complex active pocket which these findings are in line with those obtained from QSAR model. The reliability assessment of compounds predictions was checked by model applicability domain (AD) analysis.

Exploration of structural and physicochemical properties of small molecules to inhibit NMDA functionality

The N-methyl-D-aspartate (NMDA) is the family of glutamate receptor, which is involved in controlling synaptic plasticity and memory function; but overactivation of this receptor results to excess intracellular calcium formation, triggers neuronal injury and also involves in several pathologies. Both ligand- and structure-based quantitative structure-activity relationship (QSAR), pharmacophore, docking and simulation studies have been performed on a set of structurally diverse inhibitors to explore prime molecular structural features involve for specific binding to NMDA, and vis-a-vis inhibiting enzyme activity. 3D QSAR studies, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models showed the importance of steric, electrostatic and hydrophobic features; while hydrogen bond acceptor and hydrophobic features are depicted as important pharmacophore features of the molecule. Molecular docking and simulation studies corroborated the consequence of the features obtained from ligand-based Bayesian model (AUROCcv = 0.878); 3D QSAR CoMFA (R2 = 0.895, se = 0.513, Q2 = 0.602, R2pred = 0.673); CoMSIA (R2 = 0.877, se = 0.555, Q2 = 0.615, R2pred = 0.727); hologram QSAR (Q2 = 0.812, R2 = 0.941, R2pred = 0.772), and pharmacophore models (Q2 = 0.926, R2 = 0.927, R2pred = 0.621). Presence of aromatic ring, hetero and halogen atoms along with alkyl group of molecular scaffold shows their importance for binding affinity to NMDA receptor. Stability of the complex is adjudged by both docking and simulation studies.

Structure-based QSAR, molecule design and bioassays of protease-activated receptor 1 inhibitors

Quantitative structure–activity relationship (QSAR) studies were performed on a series of protease-activated receptor 1 (PAR1) inhibitors to identify the key structural features responsible for their biological activity. Induced-fit docking (IFD) was used to explore the active mechanisms of all PAR1 inhibitors at the active pocket of PAR1, and the best plausible conformation was determined by IFD for further QSAR studies. Based on the best plausible conformation, structure-based descriptors and ligand descriptors incorporating the ligand–receptor interaction were calculated. The random forest method was used to select important descriptors and build the 2D-QSAR model. The results of the 2D-QSAR model gave a squared correlation coefficient (R2) of 0.937, a prediction squared correlation coefficient (R2pred) of 0.845 and a mean square error (MSE) of 0.056. Furthermore, a 3D-QSAR model was developed via topomer comparative molecular field analysis (Topomer CoMFA), resulting in an R2 of 0.938, a cross-validated Q2 of 0.503 and a R2pred of 0.758. Based on the developed QSAR model, Topomer search was used for virtual screening of the R2 fragment in lead-like inhibitors from the National Cancer Institute (NCI) database, which contains 260,000 molecules. Eighty-two compounds were designed with different R2 fragments, and four of these compounds were selected for further biological testing. All four compounds showed inhibitory potency against PAR1.

Genome-wide Screening of Human Papillomavirus-Specific CTL Epitopes Presented by HLA-A Alleles in Cervical Cancer

Human papillomavirus (HPV) is the most common viral infection of the reproductive tract, which can increase the risk of cervical cancer. Development of HPV-specific vaccines that can be recognized and presented by human leukocyte antigen (HLA) system is considered as an important approach to prevention and treatment of cervical cancer. In this study, a combination of bioinformatics analysis and peptide binding assay was described to identify potential cytotoxic T lymphocyte epitopes from the HPV genome. In the procedure, a structure-based quantitative structure–activity relationship (SB-QSAR) was used to analyze the intermolecular interactions between HLA proteins and peptide epitopes, and the obtained nonbonded descriptors were then correlated linearly and nonlinearly with HLA–peptide affinities by using partial least squares regression and support vector machine, respectively. Several SB-QSAR models were established and evaluated rigorously, from which the best predictor was applied to virtual screening against thousands of nonapeptide segments generated from the HPV-16 genome. With this protocol a number of peptide ligands were inferred to have high affinity for a variety of frequently observed HLA-A alleles, including 0101, 0201, 0202, 0203, 0206 and 0301, from which several promising candidates were selected and tested in vitro to determine their binding strength toward HLA proteins. Consequently, several peptides were identified as potent HLA binders with half-maximal binding BL50 at micromolar level. The molecular mechanism underlying the high-affinity binding of identified peptide ligands to HLA proteins was also investigated in detail.

Targeting EGFR mutants with non-cognate kinase inhibitors in non-small cell lung cancer

Considering that the human protein kinase family members share high conservation in both primary sequence and advanced structure, and a large number of small-molecule inhibitors have already been developed to target these different members, we herein are interested in whether certain existing inhibitors that were originally designed for other (cognate) kinases can also bind efficiently to (non-cognate) epidermal growth factor receptor (EGFR) mutants and simultaneously lie low affinity to wild-type EGFR. To explore this notion, a structure-based quantitative structure–activity relationship model was derived from 77 crystal structure-solved, affinity-known kinase–inhibitor complexes. We employed this model to profile a systematic interaction map of 2 cognate and 11 non-cognate kinase inhibitors with wild-type EGFR and its several important NSCLC-related mutants. As might be expected, the cognate inhibitors exhibited generally high affinity to both wild-type and mutant EGFR, while most non-cognate inhibitors have low binding potency for EGFR. However, few combinations of non-cognate inhibitors and EGFR mutants, such as TAE684 and T790M, SKI606 and L858R, and R406 and T790M, show a favorable interaction as compared to the same inhibitors with wild-type EGFR. A further kinase assay was performed to determine the inhibitory activities of compound TAE684, originally developed as a ALK kinase inhibitor, against wild-type and T790M mutant EGFR; it is revealed that the TAE684 inhibited the mutant with ~tenfold higher potency than the wild-type enzyme. This work would help to establish a new strategy for the new use of an old drug.

Molecular dynamics simulation study and molecular docking descriptors in structure-based QSAR on acetylcholinesterase (AChE) inhibitors

In this study we present an approach for predicting the inhibitory activity of acetylcholinesterase (AChE) inhibitors by combining molecular dynamics (MD) simulation and docking studies in a structure-based quantitative structure–activity relationship (QSAR) model. The MD simulation was performed on AChE to obtain enzyme conformation in a water environment. The resulting conformation of the enzyme was used for docking with the most potent inhibitor (26a). Docking analysis revealed that hydrophobic interactions play important roles in the AChE-inhibitor complex. Then, all inhibitors that could bind simultaneously at the catalytic site and at the peripheral anionic site of AChE were docked into the enzyme and their interactions with AChE were used as new interpretable descriptors in a structure-based QSAR model. The least squares support vector regression was constructed using the four most relevant docking descriptors and one molecular structure descriptor. The Q 2 value of the model was found to be 0.790. Furthermore, to study the enzyme conformation stability, a second MD simulation was performed on AChE-inhibitor 26a complex. In MD simulation, the topological parameters of the inhibitor were derived from the PRODRG server, and partial atomic charges were modified using the B3LYP/6-31G level of theory. The radius of gyration for the complex showed that AChE conformation did not change in the presence of the inhibitors.

Docking, molecular dynamics simulation studies, and structure-based QSAR model on cytochrome P450 2A6 inhibitors

In this study, we used molecular docking and molecular dynamics (MD) simulation studies to analyze the interactions of a series of naphthalene and non-naphthalene derivatives (n = 38) as potent inhibitors of cytochrome P450 2A6 (CYP2A6) and to construct a structure-based quantitative structure activity relationship (QSAR) model. A 6000 ps MD simulation in a cubic water box were employed to build 3D structure of the CYP2A6 in a water environment. After reaching the equilibrium, the most potent inhibitor (2-bromonaphthalene) was docked into the CYP2A6 to realize the binding site of the enzyme. The docking analysis showed that π–π interaction of the inhibitors with four phenylalanine residues at positions of 107, 111, 118, and 480 plays an important role in the activities of the inhibitors. Then, an additional 6000 ps MD simulation was performed on the complex of the CYP2A6–2-bromonaphthalene to explore the effect of the inhibitor on the stability of the protein–inhibitor complex. Radius of gyration values for CYP2A6 and CYP2A6–2-bromonaphthalene complex were 2.23 ± 0.01 and 2.21 ± 0.01 nm, respectively, which revealed that the structure of the CYP2A6 in the presence of 2-bromonaphthalene has not changed. Finally, all the inhibitors docked into the enzyme and the docked configuration of the inhibitors with the lowest free energy was used to calculate the most feasible descriptors. The selected descriptors were related to the inhibitory activities using multiple linear regression (MLR) and least squares support vector regression (LS-SVR) models. The Q2 values for MLR and LS-SVR were 0.695 and 0.728, respectively.

A Structure‐based QSAR and Docking Study on Imidazo[1,5‐a][1,2,4]‐triazolo[1,5‐d][1,4,]benzodiazepines as Selective GABAAα5 Inverse Agonists

As three-dimensional (3D) structure of the GABA(A) α5 was not determined, the crystal structure of 2Vl0E at 3.3 Å resolution which is a ligand-gated K(+) channel was used as a template in homology modeling, and the result was used in molecular dynamic simulation for obtaining its conformation in a water sphere. The resulted conformation of the receptor was used for docking with the most potent of imidazo[1,5-a][1,2,4]-triazolo[1,5-d][1,4,] benzodiazepines drugs to find out binding sites and consequently the types of the interaction between the drugs and receptor. The results showed that π-π interaction of the drugs with three phenylalanine and tyrosine residues plays an important role in determining the potency of the inhibitors. The obtained information relating to the binding sites of the receptor was utilized for docking all the drugs into the receptor and find out optimized conformation for each drug, used in structure-based quantitative structure-activity relationship (QSAR) model for calculation of descriptors. Then, selected descriptors were related to the binding affinity and selectivity of the drugs using multiple linear regression and least squares-support vector regression. Finally, the results of target- and ligand-based QSAR models were compared, resulted the superiority of the structure-based QSAR to the ligand-based model.

Structure-Based Quantitative Structure–Activity Relationship Modeling of Estrogen Receptor β-Ligands

A variety of chemotypes have been studied as estrogen receptor (ER) β-selective ligands for potential drugs against various indications, including neurodegenerative diseases. Their structure--activity relationship data and the x-ray structures of the ERβ ligand-binding domain bound with different ligands have become available. Thus, it is vitally important for future development of ERβ-selective ligands that robust quantitative structure-activity relationship (QSAR) models be built. We employed a newly developed structure--based QSAR method (structure-based pharmacophore keys QSAR) that utilizes both the structure--activity relationship data and the 3D structural information of ERβ, as well as a robust QSAR workflow to analyze 37 ligands. Four sets of QSAR models were obtained, among which approximately 30 models afforded high (>0.60) training-r(2) and test set-R(2) statistics. We have obtained an ensemble of predictive models of ERβ ligands that will be useful in the future discovery of novel ERβ-selective molecules.

Structure-based quantitative structure-activity relationship studies of checkpoint kinase 1 inhibitors

Structure-based quantitative structure-activity relationship (QSAR) studies on a series of checkpoint kinase 1 (Chk1) inhibitors were performed to find the key structural features responsible for their inhibitory activity. Molecular docking was employed to explore the binding mode of all inhibitors at the active site of Chk1 and determine the active conformation for the QSAR studies. Ligand and structure-based descriptors incorporating the ligand-receptor interaction were generated based on the docked complex. Genetic Algorithm-Multiple Linear Regression (GA-MLR) method was used to build 2D QSAR model. The 2D QSAR model gave a squared correlation coefficient R(2) of 0.887, cross-validated Q(2) of 0.837 and the prediction squared correlation coefficient R(2)(pred) of 0.849, respectively. Furthermore, three-dimensional quantitative structure-activity relationship (3D QSAR) model using comparative molecular field analysis (CoMFA) with R(2) of 0.983, Q(2) of 0.550 and R(2)(pred) of 0.720 was also developed. The obtained results are helpful for the design of novel Chk1 inhibitors with improved activities.

Why OppA protein can bind sequence-independent peptides? A combination of QM/MM, PB/SA, and structure-based QSAR analyses

Periplasmic oligopeptide-binding protein (OppA) is the initial receptor in the ATP-binding cassette (ABC) system of bacteria, which exhibits a broad specificity in binding oligopeptides without regard to sequence. Here, we present a computational study on the structural properties and energetic landscapes of OppA protein interacting with its cognate ligands on the basis of 28 structure/affinity-known OppA-tripeptide complexes. By employing a well-designed protocol that couples the hybrid quantum mechanical/molecular mechanical (QM/MM) scheme and the sophisticated Poisson-Boltzmann/surface area (PB/SA) solvent model together to analyze and decompose the energy components associated with the OppA-peptide binding, we demonstrate that the broad specificity of OppA-recognizing peptides is originated from a series of exquisite balances between the free energy contributions from, for example, the direct nonbonded interactions and indirect desolvation effects, the main chains and side chains, and the different residue positions of the tripeptide ligands. We also show that, in a framework of structure-based quantitative structure-activity relationship (SB-QSAR) methodology, the QM/MM-PB/SA-derived energy terms could be used as a good descriptor to characterize the interaction profile of OppA with peptides and correlate pretty well with the experimentally measured affinities of the binding.

A Novel Structure-Based Multimode QSAR Method Affords Predictive Models for Phosphodiesterase Inhibitors

Quantitative structure-activity relationship (QSAR) methods aim to build quantitatively predictive models for the discovery of new molecules. It has been widely used in medicinal chemistry for drug discovery. Many QSAR techniques have been developed since Hansch's seminal work, and more are still being developed. Motivated by Hopfinger's receptor-dependent QSAR (RD-QSAR) formalism and the Lukacova-Balaz scheme to treat multimode issues, we have initiated studies that focus on a structure-based multimode QSAR (SBMM QSAR) method, where the structure of the target protein is used in characterizing the ligand, and the multimode issue of ligand binding is systematically treated with a modified Lukacova-Balaz scheme. All ligand molecules are first docked to the target binding pocket to obtain a set of aligned ligand poses. A structure-based pharmacophore concept is adopted to characterize the binding pocket. Specifically, we represent the binding pocket as a geometric grid labeled by pharmacophoric features. Each pose of the ligand is also represented as a labeled grid, where each grid point is labeled according to the atom types of nearby ligand atoms. These labeled grids or three-dimensional (3D) maps (both the receptor map (R-map) and the ligand map (L-map)) are compared to each other to derive descriptors for each pose of the ligand, resulting in a multimode structure-activity relationship (SAR) table. Iterative partial least-squares (PLS) is employed to build the QSAR models. When we applied this method to analyze PDE-4 inhibitors, predictive models have been developed, obtaining models with excellent training correlation (r(2) = 0.65-0.66), as well as test correlation (R(2) = 0.64-0.65). A comparative analysis with 4 other QSAR techniques demonstrates that this new method affords better models, in terms of the prediction power for the test set.

Development of Improved Models for Phosphodiesterase-4 Inhibitors with a Multi-Conformational Structure-Based QSAR Method

Phosphodiesterase-4 (PDE-4) is an important drug target for several diseases, including COPD (chronic obstructive pulmonary disorder) and neurodegenerative diseases. In this paper, we describe the development of improved QSAR (quantitative structure-activity relationship) models using a novel multi-conformational structure-based pharmacophore key (MC-SBPPK) method. Similar to our previous work, this method calculates molecular descriptors based on the matching of a molecule's pharmacophore features with those of the target binding pocket. Therefore, these descriptors are PDE4-specific, and most relevant to the problem under study. Furthermore, this work expands our previous SBPPK QSAR method by explicitly including multiple conformations of the PDE-4 inhibitors in the regression analysis, and thus addresses the issue of molecular flexibility. The nonlinear regression problem resulted from including multiple conformations has been transformed into a linear equation and solved by an iterative partial least square (iPLS) procedure, according to the Lukacova-Balaz scheme. 35 PDE-4 inhibitors have been analyzed with this new method, and predictive models have been developed. Based on the prediction statistics for both the training set and the test set, these new models are more robust and predictive than those obtained by traditional ligand-based QSAR techniques as well as that obtained with the SBPPK method reported in our previous work. As a result, multiple predictive models have been added to the collection of QSAR models for PDE4 inhibitors. Collectively, these models will be useful for the discovery of new drug candidates targeting the PDE-4 enzyme.

Key residues controlling binding of diverse ligands to human cytochrome P450 2A enzymes.

Although the human lung cytochrome P450 2A13 (CYP2A13) and its liver counterpart cytochrome P450 2A6 (CYP2A6) are 94% identical in amino acid sequence, they metabolize a number of substrates with substantially different efficiencies. To determine differences in binding for a diverse set of cytochrome P450 2A ligands, we have measured the spectral binding affinities (K(D)) for nicotine, phenethyl isothiocyanate (PEITC), coumarin, 2'-methoxyacetophenone (MAP), and 8-methoxypsoralen. The differences in the K(D) values for CYP2A6 versus CYP2A13 ranged from 74-fold for 2'-methoxyacetophenone to 1.1-fold for coumarin, with CYP2A13 demonstrating the higher affinity. To identify active site amino acids responsible for the differences in binding of MAP, PEITC, and coumarin, 10 CYP2A13 mutant proteins were generated in which individual amino acids from the CYP2A6 active site were substituted into CYP2A13 at the corresponding position. Titrations revealed that substitutions at positions 208, 300, and 301 individually had the largest effects on ligand binding. The collective relevance of these amino acids to differential ligand selectivity was verified by evaluating binding to CYP2A6 mutant enzymes that incorporate several of the CYP2A13 amino acids at these positions. Inclusion of four CYP2A13 amino acids resulted in a CYP2A6 mutant protein (I208S/I300F/G301A/S369G) with binding affinities for MAP and PEITC much more similar to those observed for CYP2A13 than to those for CYP2A6 without altering coumarin binding. The structure-based quantitative structure-activity relationship analysis using COMBINE successfully modeled the observed mutant-ligand trends and emphasized steric roles for active site residues including four substituted amino acids and an adjacent conserved Leu(370).

A New Structure-Based QSAR Method Affords both Descriptive and Predictive Models for Phosphodiesterase-4 Inhibitors

We describe the application of a new QSAR (quantitative structure-activity relationship) formalism to the analysis and modeling of PDE-4 inhibitors. This new method takes advantage of the X-ray structural information of the PDE-4 enzyme to characterize the small molecule inhibitors. It calculates molecular descriptors based on the matching of their pharmacophore feature pairs with those (the reference) of the target binding pocket. Since the reference is derived from the X-ray crystal structures of the target under study, these descriptors are target-specific and easy to interpret. We have analyzed 35 indole derivative-based PDE-4 inhibitors where Partial Least Square (PLS) analysis has been employed to obtain the predictive models. Compared to traditional QSAR methods such as CoMFA and CoMSIA, our models are more robust and predictive measured by statistics for both the training and test sets of molecules. Our method can also identify critical pharmacophore features that are responsible for the inhibitory potency of the small molecules. Thus, this structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors. The success of this study has also laid a solid foundation for systematic QSAR modeling of the PDE family of enzymes, which will ultimately contribute to chemical genomics research and drug discovery targeting the PDE enzymes.

Structure-Based Quantitative Structure Activity Relationship Analysis of Omuralide Analogs in the 20S Proteasome: A Covalent Inhibitor COMBINE Study

Structure-Based Quantitative Structure Activity Relationship Analysis of Omuralide Analogs in the 20S Proteasome: A Covalent Inhibitor COMBINE Study