Computational drug design of novel COVID-19 inhibitor.

Background: In 2003, the first case of severe acute respiratory syndrome coronavirus (SARS-CoV) was recorded. Coronaviruses (CoVs) have caused a major outbreak of human fatal pneumonia. Currently, there is no specific drug or treatment for diseases caused by SARS CoV 2. Computational approach that adopts dynamic models is widely accepted as indispensable tool in drug design but yet to be exploited in covid-19 in Zaria, Nigeria. In this study, steps were taken to advance on the successful achievements in the field of covid-19 drug, with the aid of in silico drug design technique, to create novel inhibitor drug candidates with better activity. In this study, one thousand human immunodeficiency virus (HIV1) antiviral chemical compounds from www.bindingBD.org were docked on the SARS CoV 2 main protease protein data bank identification number 6XBH (PDB ID: 6XBH) and the molecular docking score were ranked in order to identify the compounds with the highest inhibitory effects, and easy selection for future studies. Results: The docking studies showed some interesting results. Inhibitors with Index numbers 331, 741, and 819 had the highest binding affinity. Similarly, inhibitors with Index number 441, 847, and 46 had the lowest hydrogen bond energy. Inhibitor with index number 331 was reported with the lowest value (- 48.38kCal/mol). Five new compounds were designed from the selected six (6) compounds with the best binding score giving a total of thirty (30) novel compounds. The low binding energy of inhibitor with index no. 847b is unique, as most of the interaction energies are of H-bond type with amino acids (Thr26, Gly143, Ser144, Cys145, Glu166, Gln189, Hie164, Met49, Thr26, Thr25, Thr190, Asn142, Met165) resulting in an overall negative value (-16.31 kCal/mol) making it the best of all the newly designed inhibitors. Conclusions: The novel inhibitor is 2-(2-(5-amino-2-((((3-aminobenzyl)oxy)carbonyl)amino)-5-oxopentanamido)-4-(2-(tert-butyl)-4-oxo-4-(pentan-3-ylamino) butanamido)-3-hydroxybutyl) benzoic acid. The improvement it has over the parent inhibitor is from the primary amine group attached to meta position of first benzene ring and the carboxyl group attached to the ortho position of the second benzene ring. The molecular dynamics studies also show that the novel inhibitor remains stable after the study. This result makes it a better drug candidate against SARS CoV 2 main protease when compared with the co-crystallized inhibitor or any of the 1000 docked inhibitors. Supplementary Information: The online version contains supplementary material available at 10.1186/s42269-022-00892-z.

Computer-aided modeling of triazole analogues, docking studies of the compounds on DNA gyrase enzyme and design of new hypothetical compounds with efficient activities.

The increasing problem of multi-drug resistant-tuberculosis has focused attention on developing new drugs that are not only active against drug-resistant tuberculosis, but also shorten the lengthy therapy. Therefore, this work employs the application of modeling technique to predict the inhibition activities of some prominent compounds which been reported to be efficient against Mycobacterium tuberculosis. To accomplish the purpose of this work, multiple regression and genetic function approximation were adopted to create the model. The established model was swayed with topological descriptors; MATS7s, SpMin4_Bhv, TDB3v and RDF70v. More also, interactions between the compounds and the target protein 'DNA gyrase' were evaluated via molecular docking approach utilizing the PyRx and discovery studio simulation software. Based on the docking analysis, compound 20 has the most noticeable binding affinity of -16.5 kcal/mol. Therefore, compound 20 served as a reference structural template and insight to design fourteen novel hypothetical agents with more prominent anti-tubercular activities. More also, compound 20j was observed with the highest activity among the designed compounds with a prominent binding affinity of -24.3 kcal/mol. Therefore, this research recommends in-vivo, in-vitro screening and pharmacokinetic properties to be carried out in order to determine the toxicity of the designed compounds.Communicated by Ramaswamy H. Sarma.

Homology modeling and molecular docking simulation of some novel imidazo[1,2-a]pyridine-3-carboxamide (IPA) series as inhibitors of Mycobacterium tuberculosis.

BACKGROUND: Tuberculosis (TB) remains a serious global health challenge that is caused by Mycobacterium tuberculosis and has killed numerous people. This necessitated the urgent need for the hunt and development of more potent drugs against the fast-emerging extensively drug-resistant (XDR) and multiple-drug-resistant (MDR) M. tuberculosis strains. Mycobacterium tuberculosis cytochrome b subunit of the cytochrome bc1 complex (QcrB) was recognized as a potential drug target in M. tuberculosis (25618/H37Rv) for imidazo[1,2-a]pyridine-3-carboxamides whose crystal strucuture is not yet reported in the Protein Data Bank (PDB). The concept of homology modeling as a powerful and useful computational method can be applied, since the M. tuberculosis QcrB protein sequence data are available. RESULTS: The homology model of QcrB protein in M. tuberculosis was built from the X-ray structure of QcrB in M. smegmatis as a template using the Swiss-Model online workspace. The modeled protein was assessed, validated, and prepared for the molecular docking simulation of 35 ligands of N-(2-phenoxy)ethyl imidazo[1,2-a] pyridine-3-carboxamide (IPA) to analyze their theoretical binding affinities and modes. The docking results showed that the binding affinity values ranged from - 6.5 to - 10.1 kcal/mol which confirms their resilience potency when compared with 6.0kcal/mol of isoniazid standard drug. However, ligands 2, 7, 22, 26, and 35 scored higher binding affinity values of - 9.60, - 9.80, - 10.10, - 10.00, and - 10.00 kcal/mol, and are respectively considered as the best ligands among others with better binding modes in the active site of the modeled QcrB protein. CONCLUSION: The information derived in this research revealed some potential hits and paved a route for structure-based drug discovery of new hypothetical imidazo pyridine amide analogs as anti-tubercular drug candidates.

Quantitative structure-activity relationship (QSAR) and design of novel ligands that demonstrate high potency and target selectivity as protein tyrosine phosphatase 1B (PTP 1B) inhibitors as an effective strategy used to model anti-diabetic agents.

Diabetes and obesity have increased dramatically in recent decades worldwide. Diabetes mainly emerged as a major health care burden disease in both the US and other industrialized countries, among which type II diabetes is the most common. Discovering new and effective treatments for diabetes is currently a high international health priority. In the present study a computational technique was used to model 97 compounds with PTP-1B inhibitory activity, in order to demonstrate the Quantitative structure-activity relationship (QSAR) of these compounds a genetic function approximation (GFA) algorithm was applied to pick the best descriptors and multiple linear regression (MLR) was used to establish a relationship between the PTP-1B inhibitory activity of these compounds and the best molecular descriptors. This QSAR study allowed investigating the influence of very simple and easy-to-compute descriptors in determining biological activities, which shed light on the key factors that aid in the design of novel potent molecules using computer-aided drug design tools.

Modulatory role of rutin on 2,5-hexanedione-induced chromosomal and DNA damage in rats: validation of computational predictions.

The aim of this study was to evaluate the potentials of rutin on 2,5-hexanedione-induced toxicities. Two successive phases were involved using in silico and in vivo approaches. The in silico was adopted for potential oral toxicity and docking. The in vivo was carried-out in two stages for two weeks; the ameliorative (stage 1, first week), preventive, and curative studies (stage 2, extended to second week). In stage 1, rats were divided into four groups of seven each (distilled water, 3% (v/v) 2,5-hexanedione, 10 mg/kg rutin, and co-administration). In stage 2, the experimental groups were given either rutin or 2,5-hexanedione and treated in reverse order. Lipid peroxidation, protein carbonyl, and DNA fragmentation in tissues and bone marrow cells micronucleus were determined. The predicted Median lethal dose (LD50) of >5000 mg/kg and toxicity class of five (5) indicates the safety of rutin when orally administered. 2,5-Hexanedione comfortably docked in to the active sites of SOD (-22.857Kcal/mol; KI = 0.9621 µM), GPx (-11.2032Kcal/mol; KI = 0.9813 µM), and CAT (-16.446Kcal/mol; KI = 0.9726 µM) with strong hydrogen bond and hydrophobic interactions. However, only strong hydrophobic interaction was observed in the case of DNA (-3.3296Kcal/mol; KI = 0.9944). In vivo findings revealed deleterious effects of 2,5-hexanedione through induction of oxidative and chromosomal/DNA damage characterized by higher level of malondialdehyde, micronuclei formations, and DNA fragmentation. These have invariably, validates the findings from in silico experiments. Furthermore, rutin was able to ameliorate, protect, and reverse these effects, and was relatively non-toxic corroborating toxicity predictions. Rutin exhibited counteractive effects on 2,5-hexanedione-induced oxidative, chromosomal, and DNA damage.

A Derived QSAR Model for Predicting Some Compounds as Potent Antagonist against Mycobacterium tuberculosis: A Theoretical Approach.

Development of more potent antituberculosis agents is as a result of emergence of multidrug resistant strains of M. tuberculosis. Novel compounds are usually synthesized by trial approach with a lot of errors, which is time consuming and expensive. QSAR is a theoretical approach, which has the potential to reduce the aforementioned problem in discovering new potent drugs against M. tuberculosis. This approach was employed to develop multivariate QSAR model to correlate the chemical structures of the 2,4-disubstituted quinoline analogues with their observed activities using a theoretical approach. In order to build the robust QSAR model, Genetic Function Approximation (GFA) was employed as a tool for selecting the best descriptors that could efficiently predict the activities of the inhibitory agents. The developed model was influenced by molecular descriptors: AATS5e, VR1_Dzs, SpMin7_Bhe, TDB9e, and RDF110s. The internal validation test for the derived model was found to have correlation coefficient (R2) of 0.9265, adjusted correlation coefficient (R2 adj) value of 0.9045, and leave-one-out cross-validation coefficient (Q_cv∧2) value of 0.8512, while the external validation test was found to have (R2 test) of 0.8034 and Y-randomization coefficient (cR_p∧2) of 0.6633. The proposed QSAR model provides a valuable approach for modification of the lead compound and design and synthesis of more potent antitubercular agents.