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

AI-based 3D-QSAR model of FDA-approved repurposed drugs for inhibiting sclerostin

BACKGROUND

Wnt activation promotes bone formation and prevents bone loss. The Wnt pathway antagonist sclerostin and additional anti-sclerostin antibodies were discovered as a result of the development of the monoclonal antibody romosozumab. These monoclonal antibodies greatly increase the risk of cardiac arrest. Three-dimensional quantitative structure-activity relationships (3D-QSAR) predicts biological activities of ligands based on their three-dimensional features by employing powerful chemometric investigations such as artificial neural networks (ANNs) and partial least squares (PLS).

OBJECTIVE

In this study, ligand-receptor interactions were investigated using 3D-QSAR Comparative molecular field analysis (CoMFA). Estimates of steric and electrostatic characteristics in CoMFA are made using Lennard-Jones and Coulomb potentials.

METHODS

To identify the conditions necessary for the activity of these molecules, fifty Food and Drug Administration (FDA)-approved medications were chosen for 3D-QSAR investigations and done by CoMFA. For QSAR analysis, there are numerous tools available. This study employed Open 3D-QSAR for analysis due to its simplicity of use and capacity to produce trustworthy results. Four tools were used for the analysis on this platform: Py-MolEdit, Py-ConfSearch, and Py-CoMFA.

RESULTS

Maps that were generated were used to determine the screen's r2 (Coefficient of Multiple Determinations) value and q2 (correlation coefficient). These numbers must be fewer than 1, suggesting a good, trustworthy model. Cross-validated (q2) 0.532 and conventional (r2) correlation values of 0.969 made the CoMFA model statistically significant. The model showed that hydroxamic acid inhibitors are significantly more sensitive to the steric field than the electrostatic field (70%) (30%). This hypothesis states that steric (43.1%), electrostatic (26.4%), and hydrophobic (20.3%) qualities were important in the design of sclerostin inhibitors.

CONCLUSION

With 3D-QSAR and CoMFA, statistically meaningful models were constructed to predict ligand inhibitory effects. The test set demonstrated the model's robustness. This research may aid in the development of more effective sclerostin inhibitors that are synthesised using FDA-approved medications.

A comprehensive review on the antidiabetic attributes of thiazolidine-4-ones: Synthetic strategies and structure-activity relationships

The thiazolidine-4-one scaffold has recently emerged as a potential pharmacophore having clinical significance for medicinal chemists. This heterocyclic ring has been reported to possess a plethora of biological activities, including antidiabetic activity that has inspired researchers to integrate this core with different pharmacophoric fragments to design novel and effective antidiabetic leads. The antidiabetic activity has been observed due to the ability of the thiazolidine-4-one nucleus to interact with different biological targets, including peroxisome proliferator-activated receptor γ, protein tyrosine phosphatase 1B, aldose reductase, α-glucosidase, and α-amylase. The present review discusses the mode of action of thiazolidine-4-ones through these antidiabetic drug targets. This review attempts to summarize and analyze the recent developments with regard to the antidiabetic potential of thiazolidine-4-ones covering different synthetic strategies, structure-activity relationships, and docking studies reported in the literature. The significance of various structural modifications at C-2, N-3, and C-5 of the thiazolidine-4-one ring has also been discussed in this manuscript. This comprehensive compilation will provide an inevitable scope for the design and development of potential antidiabetic drug candidates having a thiazolidine-4-one core.

A comparison between 2D and 3D descriptors in QSAR modeling based on bio-active conformations

QSAR models are widely and successfully used in many research areas. The success of such models highly depends on molecular descriptors typically classified as 1D, 2D, 3D, or 4D. While 3D information is likely important, e. g., for modeling ligand-protein binding, previous comparisons between the performances of 2D and 3D descriptors were inconclusive. Yet in such comparisons the modeled ligands were not necessarily represented by their bioactive conformations. With this in mind, we mined the PDB for sets of protein-ligand complexes sharing the same protein for which uniform activity data were reported. The results, totaling 461 structures spread across six series were compiled into a carefully curated, first of its kind dataset in which each ligand is represented by its bioactive conformation. Next, each set was characterized by 2D, 3D and 2D + 3D descriptors and modeled using three machine learning algorithms, namely, k-Nearest Neighbors, Random Forest and Lasso Regression. Models' performances were evaluated on external test sets derived from the parent datasets either randomly or in a rational manner. We found that many more significant models were obtained when combining 2D and 3D descriptors. We attribute these improvements to the ability of 2D and 3D descriptors to code for different, yet complementary molecular properties.

Discovery of a novel cyclin-dependent kinase 8 inhibitor with an oxindole core for anti-inflammatory treatment

Chronic inflammation is an underlying cause in a number of diseases. Cyclin-dependent kinase 8 (CDK8) has been implicated as an inflammatory mediator, indicating its potential as an anti-inflammatory target. Herein, we performed structure-based virtual screening (SBVS) to identify novel CDK8 inhibitors. The pharmacological interactions for CDK8 were identified and incorporated into a SBVS protocol. Selected compounds were tested in enzymatic assays, and one compound was confirmed to be a CDK8 inhibitor with a 50% inhibitory concentration (IC₅₀) value of 1684.4 nM. Comparing structural analogs identified a compound, F059-1017, with greater potency (IC₅₀ 558.1 nM). When tested in cell lines, the compounds displayed low cytotoxicity. Cellular assays revealed that the identified CDK8 inhibitors can reduce phosphorylation and expression of signaling mediators associated with inflammation. In addition, results of kinase profiling showed that compound F059-1017 is selective towards CDK8. These findings suggest that the new inhibitors have great potential as lead compounds for developing novel anti-inflammatory therapeutics.

Anti-chronic myeloid leukemia activity and quantitative structure-activity relationship of novel thiazole aminobenzamide derivatives

The anti-chronic myeloid leukemia activity of thiazole aminobenzamide derivatives in vitro was tested by a methanethiosulfonate (MTS)-based viability assay method, and the result showed that some compounds exhibited good inhibitory activities against human chronic myeloid leukemia cell line K562, imatinib-resistant strain K562/R and T135I mutant cell line BaF₃-ABL-BCR-T315I. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods were used to analyze the relationship between the structure of thiazole aminobenzamide derivatives and the inhibition of K562/R cell activity. In CoMFA, Q² was 0.899 and R² was 0.963; in CoMSIA, Q² and R² were 0.840 and 0.903, respectively. These data indicated that the selected test set showed suitable external predictive ability. Combined with the contour map results, we further analyzed the three-dimensional quantitative structure (3D-QSAR) model. The results demonstrated that in the backbone of the thiazole aminobenzamide derivative, the substitution of a small group at R¹ position, or the introduction of a hydrophilic group at R² position, or the introduction of a large-volume amino acid at R³ position may be beneficial to improve the anti-CML activity of the compound.

Synthesis, biological evaluation, and molecular docking study of some new rohitukine analogs as protein tyrosine phosphatase 1B inhibitors

Rohitukine (RH) was extracted from the stem bark of Dysoxylum binectariferum Hook. It was derivatized to different arylsulphanmides by treating with the corresponding aryl sulphonyl chlorides. These derivatives were tested in-vitro on protein tyrosine phosphatase 1B (PTP1B) inhibition. Among these the active compounds K2, K3, K5, and K8 significantly inhibited the PTP1B by 51.3%, 65.6%, 71.9%, and 55.9% respectively at 10 µg/ml, the results were also supported by in-silico docking experiments. The most potent compound K5 was analyzed for antidiabetic and antidyslipidemic activity in vivo. It showed a marked reduction in blood glucose level (random and fasting) and serum insulin level in db/db mice. It improved glucose intolerance as ascertained by the oral glucose tolerance test (OGTT). These NCEs (New Chemical Entities) also lowered cholesterol and triglyceride profiles while improved high-density lipoprotein cholesterol in db/db mice. The K5 was further evaluated for antiadipogenic activity on MDI (Methylisobutylxanthine, dexamethasone, and insulin)-induced adipogenesis. where it significantly inhibited MDI-induced adipogenesis in 3 T3-L1 preadipocytes, at 10 µM and 20 µM concentration. These results were compared with the parent compound RH which inhibited 35% and 45% lipid accumulation while the RH analog K5 inhibited the lipid accumulation by 41% and 51% at 10 and 20 µM concentration, respectively. These results well corroborated with in-silico studies.

A Library of Thiazolidin-4-one Derivatives as Protein Tyrosine Phosphatase 1B (PTP1B) Inhibitors: An Attempt To Discover Novel Antidiabetic Agents

Protein tyrosine phosphatase 1B (PTP1B) is an important target for the treatment of diabetes. A series of thiazolidin-4-one derivatives 8-22 was designed, synthesized and investigated as PTP1B inhibitors. The new molecules inhibited PTP1B with IC₅₀ values in the micromolar range. 5-(Furan-2-ylmethylene)-2-(4-nitrophenylimino)thiazolidin-4-one (17) exhibited potency with a competitive type of enzyme inhibition. structure-activity relationship studies revealed various structural facets important for the potency of these analogues. The findings revealed a requirement for a nitro group-including hydrophobic heteroaryl ring for PTP1B inhibition. Molecular docking studies afforded good correlation with experimental results. H-bonding and π-π interactions were responsible for optimal binding and effective stabilization of virtual protein-ligand complexes. Furthermore, in-silico pharmacokinetic properties of test compounds predicted their drug-like characteristics for potential oral use as antidiabetic agents.Additionally, a binding site model demonstrating crucial pharmacophoric characteristics influencing potency and binding affinity of inhibitors has been proposed, which can be employed in the design of future potential PTP1B inhibitors.

Molecular docking, 3D-QSAR, and molecular dynamics simulations of thieno[3,2-b]pyrrole derivatives against anticancer targets of KDM1A/LSD1

Lysine-specific demethylase 1 (LSD1) is a histone-modifying enzyme, which has been proposed as a promising target for anticancer drug development. Extensive research on LSD1 inhibitors has been performed since its discovery. In order to get more information for lead identification and optimization, we carried out a molecular modeling study on a set of 43 thieno[3,2-b]pyrrole competitive inhibitors of LSD1 using three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations. Based on the co-crystallized conformer-based alignment (CCBA) method, 3D-QSAR model of thieno[3,2-b]pyrrole derivatives as LSD1 inhibitors was established. The significant statistics (q² = 0.595, r² = 0.959, r²_pred = 0.846) of the 3D-QSAR indicated the good predictive power and statistical reliability of this model. Based on the corresponding contour maps six LSD1 inhibitors were designed and their activities were predicted by 3D-QSAR model. Meanwhile, molecular docking was performed to simulate the probable binding modes between ligands and LSD1 protein. The molecular interactions mainly contributions to the binding affinity for LSD1 inhibitions were further supplemented by 100 ns MD simulations and binding free energy calculation.

Design, synthesis, pharmacological evaluation and in silico ADMET prediction of novel substituted benzimidazole derivatives as angiotensin II-AT1 receptor antagonists based on predictive 3D QSAR models

In this study we designed novel substituted benzimidazole derivatives and predicted their absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, based on a predictive 3D QSAR study on 132 substituted benzimidazoles as AngII-AT1 receptor antagonists. The two best predicted compounds were synthesized and evaluated for AngII-AT1 receptor antagonism. Three different alignment tools for comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used. The best 3D QSAR models were obtained using the rigid body (Distill) alignment method. CoMFA and CoMSIA models were found to be statistically significant with leave-one-out correlation coefficients (q(2)) of 0.630 and 0.623, respectively, cross-validated coefficients (r(2)cv) of 0.651 and 0.630, respectively, and conventional coefficients of determination (r(2)) of 0.848 and 0.843, respectively. 3D QSAR models were validated using a test set of 24 compounds, giving satisfactory predicted results (r(2)pred) of 0.727 and 0.689 for the CoMFA and CoMSIA models, respectively. We have identified some key features in substituted benzimidazole derivatives, such as lipophilicity and H-bonding at the 2- and 5-positions of the benzimidazole nucleus, respectively, for AT1 receptor antagonistic activity. We designed 20 novel substituted benzimidazole derivatives and predicted their activity. In silico ADMET properties were also predicted for these designed molecules. Finally, the compounds with best predicted activity were synthesized and evaluated for in vitro angiotensin II-AT1 receptor antagonism.

CoMFA and CoMSIA studies on C-aryl glucoside SGLT2 inhibitors as potential anti-diabetic agents

SGLT2 has become a target of therapeutic interest in diabetes research. CoMFA and CoMSIA studies were performed on C-aryl glucoside SGLT2 inhibitors (180 analogues) as potential anti-diabetic agents. Three different alignment strategies were used for the compounds. The best CoMFA and CoMSIA models were obtained by means of Distill rigid body alignment of training and test sets, and found statistically significant with cross-validated coefficients (q²) of 0.602 and 0.618, respectively, and conventional coefficients (r²) of 0.905 and 0.902, respectively. Both models were validated by a test set of 36 compounds giving satisfactory predicted correlation coefficients (r² pred) of 0.622 and 0.584 for CoMFA and CoMSIA models, respectively. A comparison was made with earlier 3D QSAR study on SGLT2 inhibitors, which shows that our 3D QSAR models are better than earlier models to predict good inhibitory activity. CoMFA and CoMSIA models generated in this work can provide useful information to design new compounds and helped in prediction of activity prior to synthesis.

Profiling the structural determinants for the selectivity of representative factor-Xa and thrombin inhibitors using combined ligand-based and structure-based approaches

The current study deciphers the combined ligand- and structure-based computational insights to profile structural determinants for the selectivity of representative diverse classes of FXa-selective and thrombin-selective as well as dual FXa-thrombin high affinity inhibitors. The thrombin-exclusive insertion 60-loop (D-pocket) was observed to be one of the most notable recognition sites for the known thrombin-selective inhibitors. Based on the topological comparison of four common active-site pockets (S1-S4) of FXa and thrombin, the greater structural disparity was observed in the S4-pocket, which was more symmetrical (U-shaped) in FXa as compared to thrombin mainly due to the presence of L99 and I174 residues in latter in place of Y99 and F174 respectively in former protease. The S2 pocket forming partial roof at the entry of 12 Å deep S1-pocket, with two extended β-sheets running antiparallel to each other by undergoing U-turn (∼180̊), has two conserved glycine residues forming H-bonds with the bound ligand for governing ligand binding affinity. The docking, scoring, and binding pose comparison of the representative high-affinity and selective inhibitors into the active sites of FXa and thrombin revealed critical residues (S214, Y99, W60D) mediating selectivity through direct- and long-range electrostatic interactions. Interestingly, most of the thrombin-selective inhibitors attained S-shaped conformation in thrombin, while FXa-selective inhibitors attained L-shaped conformations in FXa. The role of residue at 99th position of FXa and thrombin toward governing protease selectivity was further substantiated using molecular dynamics simulations on the wild-type and mutated Y99L FXa bound to thrombin-selective inhibitor 2. Furthermore, predictive CoMFA (FXa q² = 0.814; thrombin q² = 0.667) and CoMSIA (FXa q² = 0.807; thrombin q² = 0.624) models were developed and validated (FXa r²(test) = 0.823; thrombin r(2)(test) = 0.816) to feature molecular determinants of ligand binding affinity using the docking-based conformational alignments (DBCA) of 141 (88(train)+53(test)) and 39 (27(train)+11(test)) nonamidine class of potent FXa (0.004 ≤ K(i) (nM) ≤ 4700) and thrombin (0.001 ≤ K(i) (nM) ≤ 940) inhibitors, respectively. Interestingly, the ligand-based insights well corroborated with the structure-based insights in terms of the role of steric, electrostatic, and hydrophobic parameters for governing the selectivity for the two proteases. The new computational insights presented in this study are expected to be valuable for understanding and designing potent and selective antithrombotic agents.

4D-QSAR analysis and pharmacophore modeling: electron conformational-genetic algorithm approach for penicillins

4D-QSAR studies were performed on a series of 87 penicillin analogues using the electron conformational-genetic algorithm (EC-GA) method. In this EC-based method, each conformation of the molecular system is described by a matrix (ECMC) with both electron structural parameters and interatomic distances as matrix elements. Multiple comparisons of these matrices within given tolerances for high active and low active penicillin compounds allow one to separate a smaller number of matrix elements (ECSA) which represent the pharmacophore groups. The effect of conformations was investigated building model 1 and 2 based on ensemble of conformers and single conformer, respectively. GA was used to select the most important descriptors and to predict the theoretical activity of the training (74 compounds) and test (13 compounds, commercial penicillins) sets. The model 1 for training and test sets obtained by optimum 12 parameters gave more satisfactory results (R(training)(2)=0.861, SE(training)=0.044, R(test)(2)=0.892, SE(test)=0.099, q(2)=0.702, q(ext1)(2)=0.777 and q(ext2)(2)=0.733) than model 2 (R(training)(2)=0.774, SE(training)=0.056, R(test)(2)=0.840, SE(test)=0.121, q(2)=0.514, q(ext1)(2)=0.641 and q(ext2)(2)=0.570). To estimate the individual influence of each of the molecular descriptors on biological activity, the E statistics technique was applied to the derived EC-GA model.

Rationalizing protein-ligand interactions for PTP1B inhibitors using computational methods

Protein tyrosine phosphatase 1B inhibitors were reported to have anti-diabetic properties and hence this enzyme has become interesting drug target in the recent time. Huge amount of data is available in public domain about the PTP1B inhibitors in the form of X-ray structures. This study is an attempt to transform this data into useful knowledge which can be directly used to design more effective protein tyrosine phosphatase inhibitors. In this study, we have built quantitative models for activity of co-crystallized protein tyrosine phosphatase inhibitors using two new approaches developed in our group, i.e. receptor-ligand interaction and Structure-based compound optimization, prioritization and evolution based on receptor-ligand interaction descriptors and residue-wise interaction energies as descriptors, respectively. These models have given insights into the receptor-ligand interactions essential for modulating the activity of PTP1B inhibitors. An external validation set of 22 molecules was used to test predictive power of these models on external set molecules.