3D-QSAR CoMFA/CoMSIA models based on theoretical active conformers of HOE/BAY-793 analogs derived from HIV-1 protease inhibitor complexes

Exploring the Efficacy of Noncovalent SARS-CoV-2 Main Protease Inhibitors: A Computational Simulation Analysis Study

The SARS-CoV-2 main protease, as a key target for antiviral therapeutics, is instrumental in maintaining virus stability, facilitating translation, and enabling the virus to evade innate immunity. Our research focused on designing non-covalent inhibitors to counteract the action of this protease. Utilizing a 3D-QSAR model and contour map, we successfully engineered eight novel non-covalent inhibitors. Further evaluation and comparison of these novel compounds through methodologies including molecular docking, ADMET analysis, frontier molecular orbital studies, molecular dynamics simulations, and binding free energy revealed that the inhibitors N02 and N03 demonstrated superior research performance (N02 ΔGbind=-206.648 kJ/mol, N03 ΔGbind=-185.602 kJ/mol). These findings offer insightful guidance for the further refinement of molecular structures and the development of more efficacious inhibitors. Consequently, future investigations can draw upon these findings to unearth more potent inhibitors, thereby amplifying their impact in the treatment and prevention of associated diseases.

3D-QSAR Studies, Molecular Docking, Molecular Dynamic Simulation, and ADMET Proprieties of Novel Pteridinone Derivatives as PLK1 Inhibitors for the Treatment of Prostate Cancer

Overexpression of polo-like kinase 1 (PLK1) has been found in many different types of cancers. With its essential role in cell proliferation, PLK1 has been determined to be a broad-spectrum anti-cancer target. In this study, 3D-QSAR, molecular docking, and molecular dynamics (MD) simulations were applied on a series of novel pteridinone derivatives as PLK1 inhibitors to discover anti-cancer drug candidates. In this work, three models—CoMFA (Q² = 0.67, R² = 0.992), CoMSIA/SHE (Q² = 0.69, R² = 0.974), and CoMSIA/SEAH (Q² = 0.66, R² = 0.975)—of pteridinone derivatives were established. The three models that were established gave Rpred2 = 0.683, Rpred 2= 0.758, and Rpred 2= 0.767, respectively. Thus, the predictive abilities of the three proposed models were successfully evaluated. The relations between the different champs and activities were well-demonstrated by the contour chart of the CoMFA and CoMSIA/SEAH models. The results of molecular docking indicated that residues R136, R57, Y133, L69, L82, and Y139 were the active sites of the PLK1 protein (PDB code: 2RKU), in which the more active ligands can inhibit the enzyme of PLK1. The results of the molecular dynamic MD simulation diagram were obtained to reinforce the previous molecular docking results, which showed that both inhibitors remained stable in the active sites of the PLK1 protein (PDB code: 2RKU) for 50 ns. Finally, a check of the ADME-Tox properties of the two most active molecules showed that molecular N° 28 could represent a good drug candidate for the therapy of prostate cancer diseases.

Exploring EPR Parameters of 187Re Complexes for Designing New MRI Probes: From the Gas Phase to Solution and a Model Protein Environment

Breast cancer is one of the major types of cancer around the world, and early diagnosis is essential for successful treatment. New contrast agents (CAs), with reduced toxicology, are needed to improve diagnosis. One of the most promising Magnetic Resonance Imaging (MRI) CA is based on rhenium conjugated with a benzothiazole derivate (ReABT). In this sense, DFT has been used to evaluate the best methodology for calculating the hyperfine coupling constant (Aiso) of ReABT. Then, a thermodynamic analysis was performed to confirm the stability of the complex. Furthermore, a docking study of ReABT at the enzyme PI3K active site and Aiso calculations of ReABT in the enzyme environment were carried out. The best methodology for the Aiso calculation of ReABT was using the M06L functional, SARC-ZORA-TZVP (for Re) and TZVP (for all other atoms) basis set, relativistic Hamiltonian, and the CPCM solvation model with water as the solvent which confirm that the relativistic effects are important for calculating the Aiso values. In addition, thermodynamic analysis indicates that ReABT presents a higher stability and a lower toxicity than Gd-based CAs. The docking studies point out that ReABT interacts with amino acids residues of alanine, aspartate, and lysine from the PI3K active site. Considering the enzyme environment, Aiso values decrease significantly. These findings indicate that the CA candidate ReABT could be a good candidate for a new contrast agent.

Insights into the value of statistical models, solvent, and relativistic effects for investigating Re complexes of 2-(4'-aminophenyl)benzothiazole: a potential spectroscopic probe

Cancer affects a major part of the worldwide population, and, to minimize deaths, the diagnosis in the early stages of the disease is fundamental. Thus, to improve diagnosis and treatment new potential spectroscopic probes are crucial. Benzothiazole derivates present antitumor properties and are highly selective and interact strongly with the enzyme phosphoinositide 3-kinase (PI3K), which was associated with cell proliferation and breast cancer cells. In this paper, the rhenium shielding tensors (¹⁸⁷Re(σ)) and hydrogen and carbon chemical shifts (¹H(δ) and ¹³C(δ)) of the Re(CO)₃(NNO) complex conjugated with 2-(4'-aminophenyl)benzothiazole (ReABT) were evaluated. A statistical HCA model was used to analyze the best DFT protocol to compute σ and δ values and to evaluate the relativistic effects, both in the basis set and Hamiltonian as well as the functionals M06L or PBE0. The best protocol was applied to obtain ¹⁸⁷Re(σ) of the ReABT complex in different environments (gas phase, solution, and in the active site of the PI3K enzyme). The results point out that ¹⁸⁷Re(σ) values of the ReABT complex change significantly when the complex is docked in the PI3K enzyme.

Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

The recent application of 3D-QSAR and docking studies to novel HIV-protease inhibitor drug discovery

INTRODUCTION

Despite the availability of FDA approved inhibitors of HIV protease, numerous efforts are still ongoing to achieve 'near-perfect' drugs devoid of characteristic adverse side effects, toxicities, and mutational resistance. While experimental methods have been plagued with huge consumption of time and resources, there has been an incessant shift towards the use of computational simulations in HIV protease inhibitor drug discovery.

AREAS COVERED

Herein, the authors review the numerous applications of 3D-QSAR modeling methods over recent years relative to the design of new HIV protease inhibitors from a series of experimentally derived compounds. Also, the augmentative contributions of molecular docking are discussed.

EXPERT OPINION

Efforts to optimize 3D QSAR and molecular docking for HIV-1 drug discovery are ongoing, which could further incorporate inhibitor motions at the active site using molecular dynamics parameters. Also, highly predictive machine learning algorithms such as random forest, K-means, decision trees, linear regression, hierarchical clustering, and Bayesian classifiers could be employed.

Protease Inhibitors for the Treatment of HIV/AIDS: Recent Advances and Future Challenges

Acquired Immunodeficiency Syndrome (AIDS) is a chronic disease characterized by multiple life-threatening illnesses caused by a retro-virus, Human Immunodeficiency Virus (HIV). HIV infection slowly destroys the immune system and increases the risk of various other infections and diseases. Although, there is no immediate cure for HIV infection/AIDS, several drugs targeting various cruxes of HIV infection are used to slow down the progress of the disease and to boost the immune system. One of the key therapeutic strategies is Highly Active Antiretroviral Therapy (HAART) or ' AIDS cocktail' in a general sense, which is a customized combination of anti-retroviral drugs designed to combat the HIV infection. Since HAART's inception in 1995, this treatment was found to be effective in improving the life expectancy of HIV patients over two decades. Among various classes of HAART treatment regimen, Protease Inhibitors (PIs) are known to be widely used as a major component and found to be effective in treating HIV infection/AIDS. For the past several years, a variety of protease inhibitors have been reported. This review outlines the drug design strategies of PIs, chemical and pharmacological characteristics of some mechanism-based inhibitors, summarizes the recent developments in small molecule based drug discovery with HIV protease as a drug target. Further discussed are the pharmacology, PI drug resistance on HIV PR, adverse effects of HIV PIs and challenges/impediments in the successful application of HIV PIs as an important class of drugs in HAART regimen for the effective treatment of AIDS.

Value of p-Value

The goal of this manuscript is to discuss important aspects of external validation of classification and category Quantitative Structure - Activity/Property/Toxicity Relationship QS/A/P/T/R models that to the best of author's knowledge are not addressed in publications. Statistical significance (in terms of p-value) and accuracy of prediction (in terms of Correct Classification Rate (CCR)) of external validation set compounds are among most important characteristics of the models. We assert that in most cases the models built for classification or category response variable should be statistically significant and predictive for each class or category. We show that three thresholds of the number of compounds in each class or category of the external validation sets should be satisfied. 1) The p-value criterion can never be satisfied, if the number of compounds is below the first threshold. 2) If the number of compounds is between the first and the second thresholds, p-value criterion should be used. 3) If it is higher than the third threshold, classification or category accuracy criterion should be used. 4) If the number of compounds is between second and third thresholds, either one or the other criterion should be used depending on the value of p-value. 5) When the number of compounds in the class approaches infinity, the maximum relative error of prediction approaches the relative expected error. The results are of interest in other areas of multidimensional data analysis.

Assessment of long-range transport potential of polychlorinated Naphthalenes based on three-dimensional QSAR models

Experimentally determined octanol-air partition coefficients (K _OA) for 43 polychlorinated naphthalene (PCN) congeners and experimentally determined subcooled liquid vapor pressures (P _L) for 17 PCN congeners were used with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to generate three-dimensional quantitative structure-activity relationship (3D-QSAR) models. The data were used to predict K _OA values for the other 32 congeners and P _L values for the other 58 congeners. The CoMFA and CoMSIA model contour maps showed that the electrostatic fields of the PCN molecules are the most important factors affecting the K _OA and P _L values. The long-range transport potentials of several PCN homologs were assessed using the following grading system: high mobility (MoCNs), relatively high mobility (DiCNs to TeCNs), relatively low mobility (PeCNs to HeCNs) and low mobility (HeCNs and OCN). The PCN-2 molecule was modified using the contour maps of the two models, and the results showed that introducing an electronegative R₁ substituent increased the K _OA value but introducing an electropositive R₆ substituent decreased the P _L value. PCN-2 was in the high mobility class, but introducing these substituents moved the long-range transport potentials of the modified molecules to the relatively high mobility class.

The History and Development of Quantitative Structure-Activity Relationships (QSARs)

It is widely accepted that modern QSAR began in the early 1960s. However, as long ago as 1816 scientists were making predictions about physical and chemical properties. The first investigations into the correlation of biological activities with physicochemical properties such as molecular weight and aqueous solubility began in 1841, almost 60 years before the important work of Overton and Meyer linking aquatic toxicity to lipid-water partitioning. Throughout the 20th century QSAR progressed, though there were many lean years. In 1962 came the seminal work of Corwin Hansch and co-workers, which stimulated a huge interest in the prediction of biological activities. Initially that interest lay largely within medicinal chemistry and drug design, but in the 1970s and 1980s, with increasing ecotoxicological concerns, QSAR modelling of environmental toxicities began to grow, especially once regulatory authorities became involved. Since then QSAR has continued to expand, with over 1400 publications annually from 2011 onwards.

Multidimensional (3D/4D-QSAR) probability-guided pharmacophore mapping: investigation of activity profile for a series of drug absorption promoters

A hybrid approach that combines 3D and 4D-QSAR methods based on grid and neural paradigms with automated IVE-PLS procedure was examined to identify the pharmacophore pattern for cholic acid derivatives as potential drug absorption promoters.

Insight into the Structural Determinants of Imidazole Scaffold-Based Derivatives as TNF-α Release Inhibitors by in Silico Explorations

Presently, 151 widely-diverse pyridinylimidazole-based compounds that show inhibitory activities at the TNF-α release were investigated. By using the distance comparison technique (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) methods, the pharmacophore models and the three-dimensional quantitative structure-activity relationships (3D-QSAR) of the compounds were explored. The proposed pharmacophore model, including two hydrophobic sites, two aromatic centers, two H-bond donor atoms, two H-bond acceptor atoms, and two H-bond donor sites characterizes the necessary structural features of TNF-α release inhibitors. Both the resultant CoMFA and CoMSIA models exhibited satisfactory predictability (with Q² (cross-validated correlation coefficient) = 0.557, R²_ncv (non-cross-validated correlation coefficient) = 0.740, R²_pre (predicted correlation coefficient) = 0.749 and Q² = 0.598, R²_ncv = 0.767, R²_pre = 0.860, respectively). Good consistency was observed between the 3D-QSAR models and the pharmacophore model that the hydrophobic interaction and hydrogen bonds play crucial roles in the mechanism of actions. The corresponding contour maps generated by these models provide more diverse information about the key intermolecular interactions of inhibitors with the surrounding environment. All these models have extended the understanding of imidazole-based compounds in the structure-activity relationship, and are useful for rational design and screening of novel 2-thioimidazole-based TNF-α release inhibitors.

QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors

In this study, application of a new hybrid docking-quantitative structure activity relationship (QSAR) methodology to model and predict the HIV-1 protease inhibitory activities of a series of newly synthesized chemicals is reported. This hybrid docking-QSAR approach can provide valuable information about the most important chemical and structural features of the ligands that affect their inhibitory activities. Docking studies were used to find the actual conformations of chemicals in active site of HIV-1 protease. Then the molecular descriptors were calculated from these conformations. Multiple linear regression (MLR) and least square support vector machine (LS-SVM) were used as QSAR models, respectively. The obtained results reveal that statistical parameters of the LS-SVM model are better than the MLR model, which indicate that there are some non-linear relations between selected molecular descriptors and anti-HIV activities of interested chemicals. The correlation coefficient (R), root mean square error (RMSE) and average absolute error (AAE) for LS-SVM are: R=0.988, RMSE=0.207 and AAE=0.145 for the training set, and R=0.965, RMSE=0.403 and AAE=0.338 for the test set. Leave one out cross validation test was used for assessment of the predictive power and validity of models which led to cross-validation correlation coefficient QUOTE of 0.864 and 0.850 and standardized predicted relative error sum of squares (SPRESS) of 0.553 and 0.581 for LS-SVM and MLR models, respectively.

Insight into the structural features of pyrazolopyrimidine- and pyrazolopyridine-based B-Raf(V600E) kinase inhibitors by computational explorations

Presently, both ligand-based and receptor-based 3D-QSAR modelings were performed on 107 pyrazolopyrimidine- and pyrazolopyridine-based inhibitors of B-Raf(V600E) kinase. The optimal model is successful to predict the inhibitors' activity with Q(2) of 0.504, R(2) ncv of 0.960, and R(2) pred of 0.872. Besides, the 3D contour maps explain well the structural requirements of the interaction between the ligand and the receptor. Furthermore, molecular docking and MD were also carried out to study the binding mode. Our findings are the following: (i) Bulky substituents at position 3, 10 and ring D improve the inhibitory activity, but impair the activity at position 5, 11, and 19. (ii) Electropositive groups at position 10, 13 and 20 and electronegative groups at position 2 increase the biological activity. (iii) Hydrophobic substituents at ring C are beneficial to improve the biological activity, while hydrophilic substituents at position 11 and ring D are good for the activity. (4) This scaffold of inhibitors may bind to the B-Raf kinase with an 'L' conformation and belong to type III binding mode, which is fixed by hydrophobic interaction and hydrogen bonds with residues from hinge region and DFG motif. These results may be a guidance to develop new B-Raf(V600E) kinase inhibitors.

A combined 3D-QSAR and docking studies for the In-silico prediction of HIV-protease inhibitors

BACKGROUND

Tremendous research from last twenty years has been pursued to cure human life against HIV virus. A large number of HIV protease inhibitors are in clinical trials but still it is an interesting target for researchers due to the viral ability to get mutated. Mutated viral strains led the drug ineffective but still used to increase the life span of HIV patients.

RESULTS

In the present work, 3D-QSAR and docking studies were performed on a series of Danuravir derivatives, the most potent HIV- protease inhibitor known so far. Combined study of 3D-QSAR was applied for Danuravir derivatives using ligand-based and receptor-based protocols and generated models were compared. The results were in good agreement with the experimental results. Additionally, docking analysis of most active 32 and least active 46 compounds into wild type and mutated protein structures further verified our results. The 3D-QSAR and docking results revealed that compound 32 bind efficiently to the wild and mutated protein whereas, sufficient interactions were lost in compound 46.

CONCLUSION

The combination of two computational techniques would helped to make a clear decision that compound 32 with well inhibitory activity bind more efficiently within the binding pocket even in case of mutant virus whereas compound 46 lost its interactions on mutation and marked as least active compound of the series. This is all due to the presence or absence of substituents on core structure, evaluated by 3D-QSAR studies. This set of information could be used to design highly potent drug candidates for both wild and mutated form of viruses.

Revealing interaction mode between HIV-1 protease and mannitol analog inhibitor

HIV protease is a key enzyme to play a key role in the HIV-1 replication cycle and control the maturation from HIV viruses to an infectious virion. HIV-1 protease has become an important target for anti-HIV-1 drug development. Here, we used molecular dynamics simulation to study the binding mode between mannitol derivatives and HIV-1 protease. The results suggest that the most active compound (M35) has more stable hydrogen bonds and stable native contacts than the less active one (M17). These mannitol derivatives might have similar interaction mode with HIV-1 protease. Then, 3D-QSAR was used to construct quantitative structure-activity models. The cross-validated q(2) values are found as 0.728 and 0.611 for CoMFA and CoMSIA, respectively. And the non-cross-validated r(2) values are 0.973 and 0.950. Nine test set compounds validate the model. The results show that this model possesses better prediction ability than the previous work. This model can be used to design new chemical entities and make quantitative prediction of the bioactivities for HIV-1 protease inhibitors before resorting to in vitro and in vivo experiment.

Investigation on quantitative structure activity relationships and pharmacophore modeling of a series of mGluR2 antagonists

MGluR2 is G protein-coupled receptor that is targeted for diseases like anxiety, depression, Parkinson’s disease and schizophrenia. Herein, we report the three-dimensional quantitative structure–activity relationship (3D-QSAR) studies of a series of 1,3-dihydrobenzo[ b][1,4]diazepin-2-one derivatives as mGluR2 antagonists. Two series of models using two different activities of the antagonists against rat mGluR2, which has been shown to be very similar to the human mGluR2, (activity I: inhibition of [³H]-LY354740; activity II: mGluR2 (1S,3R)-ACPD inhibition of forskolin stimulated cAMP.) were derived from datasets composed of 137 and 69 molecules respectively. For activity I study, the best predictive model obtained from CoMFA analysis yielded a Q² of 0.513, R²_ncv of 0.868, R²_pred = 0.876, while the CoMSIA model yielded a Q² of 0.450, R²_ncv = 0.899, R²_pred = 0.735. For activity II study, CoMFA model yielded statistics of Q² = 0.5, R²_ncv = 0.715, R²_pred = 0.723. These results prove the high predictability of the models. Furthermore, a combined analysis between the CoMFA, CoMSIA contour maps shows that: (1) Bulky substituents in R₇, R₃ and position A benefit activity I of the antagonists, but decrease it when projected in R₈ and position B; (2) Hydrophilic groups at position A and B increase both antagonistic activity I and II; (3) Electrostatic field plays an essential rule in the variance of activity II. In search for more potent mGluR2 antagonists, two pharmacophore models were developed separately for the two activities. The first model reveals six pharmacophoric features, namely an aromatic center, two hydrophobic centers, an H-donor atom, an H-acceptor atom and an H-donor site. The second model shares all features of the first one and has an additional acceptor site, a positive N and an aromatic center. These models can be used as guidance for the development of new mGluR2 antagonists of high activity and selectivity. This work is the first report on 3D-QSAR modeling of these mGluR2 antagonists. All the conclusions may lead to a better understanding of the mechanism of antagonism and be helpful in the design of new potent mGluR2 antagonists.

Molecular docking, 3D-QSAR studies, and in silico ADME prediction of p-aminosalicylic acid derivatives as neuraminidase inhibitors

Neuraminidase (NA) is a major glycoprotein of influenza virus which is essential for viral infection. It offers a potential target for antiviral drug design and discovery. To develop novel potent neuraminidase inhibitors (NAI), Surflex-Dock was employed to dock 40 hydrophobic p-aminosalicylic acid derivatives into the active site of NA. The 3D-quantitative structure-activity relationship studies involving comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were carried out on 40 molecules. Both CoMFA (q(2) = 0.628, r(2) = 0.697) and CoMSIA (q(2) = 0.746, r(2) = 0.816) gave reasonable results. A preliminary pharmacokinetic profile of these NAI was also performed on the basis of Volsurf predictions. The results obtained from this study will be useful in the design of novel potent NAI.

Looking at the proteases from a simple perspective

Proteases have received enormous interest from the research and medical communities because of their significant roles in several human diseases. Some examples include the involvement of thrombin in thrombosis, HIV-1 protease in Acquired Immune Deficiency Syndrome, cruzain in Trypanosoma cruzi infection, and membrane-type 1 matrix metalloproteinase in tumor invasion and metastasis. Many efforts has been undertaken to design effective inhibitors featuring potent inhibitory activity, specificity, and metabolic stability to those proteases involved in such pathologies. Protease inhibitors usually target the active site, but some of them act by other inhibitory mechanisms. The understanding of the structure-function relationships of proteases and inhibitors has an impact on new inhibitor drugs designing. In this paper, the structures of four proteases (thrombin, HIV-protease, cruzain, and a matrix metalloproteinase) are briefly reviewed, and used as examples of the importance of proteases for the development of new treatment strategies, leading to a longer and healthier life.