Pharmacophore- based virtual screening, 3D- QSAR, molecular docking approach for identification of potential dipeptidyl peptidase IV inhibitors

In silico anti-viral assessment of phytoconstituents in a traditional (Siddha Medicine) polyherbal formulation - Targeting Mpro and pan-coronavirus post-fusion Spike protein

Background and aim

Novel nature of the viral pathogen SARS-CoV-2 and the absence of standard drugs for treatment, have been a major challenge to combat this deadly infection. Natural products offer safe and effective remedy, for which traditional ethnic medicine can provide leads. An indigenous poly-herbal formulation, Kabasura Kudineer from Siddha system of medicine was evaluated here using a combination of computational approaches, to identify potential inhibitors against two anti-SARS-CoV-2 targets - post-fusion Spike protein (structural protein) and main protease (Mpro, non-structural protein).

Experimental procedure

We docked 32 phytochemicals from the poly-herbal formulation against viral post-fusion Spike glycoprotein and Mpro followed by molecular dynamics using Schrodinger software. Drug-likeness analysis was performed using machine learning (ML) approach and pkCSM.

Results

The binding affinity of the phytochemicals in Kabasura Kudineer revealed the following top-five bioactives: Quercetin > Luteolin > Chrysoeriol > 5-Hydroxy-7,8-Dimethoxyflavone > Scutellarein against Mpro target, and Gallic acid > Piperlonguminine > Chrysoeriol > Elemol > Piperine against post-fusion Spike protein target. Quercetin and Gallic acid exhibited binding stability in complexation with their respective viral-targets and favourable free energy change as revealed by the molecular dynamics simulations and MM-PBSA analysis. In silico predicted pharmacokinetic profiling of these ligands revealed appropriate drug-likeness properties.

Conclusion

These outcomes provide: (a) potential mechanism for the anti-viral efficacy of the indigenous Siddha formulation, targeting Mpro and post-fusion Spike protein (b) top bioactive lead-molecules that may be developed as natural product-based anti-viral pharmacotherapy and their pleiotropic protective effects may be leveraged to manage co-morbidities associated with COVID-19.

Discovery of newer pyrazole derivatives with potential anti-tubercular activity via 3D-QSAR based pharmacophore modelling, virtual screening, molecular docking and molecular dynamics simulation studies

Tuberculosis is one of the leading causes of death of at least one million people annually. The deadliest infectious disease has caused more than 120 million deaths in humans since 1882. The cell wall structure of Mycobacterium tuberculosis is important for survival in the host environment. InhA is the foremost target for the development of novel anti-tubercular agents. Therefore, we report pharmacophore-based virtual screening (ZINC and ASINEX databases) and molecular docking study (PDB Code: 4TZK) to identify and design potent inhibitors targeting to InhA. A five-point pharmacophore model AADHR_1 (with R2 = 0.97 and Q2 = 0.77) was developed by using 47 compounds with its reported MIC values. Further, to identify and design potent hit molecules based on lead identification and modification, generated hypothesis employed for virtual screening using ZINC and ASINEX databases. Predicted pyrazole derivatives further gauged for drug likeliness and docked against enoyl acyl carrier protein reductase to categorize the essential amino acid interactions to the active site of the enzyme. Structure elucidation of these synthesized compounds was carried out using IR, MS, 1H-NMR and 13C-NMR spectroscopy. Amongst all the synthesized compounds, some of the compounds 5a, 5c, 5d and 5e were found to be potent with their MIC ranging from 2.23 to 4.61 µM. Based on preliminary anti-tubercular activity synthesized potent molecules were further assessed for MDR-TB, XDR-TB and cytotoxic study.

Insights into the structural requirements of triazole derivatives as promising DPP IV inhibitors: computational investigations

Diabetes is one of the leading causes of death globally as per World Health Organization 2019. To cope up with side effects of current diabetes therapy, researchers have found several novel targets for the treatment of diabetes. Currently, dipeptidyl peptidase IV (DPP IV) has emerged as a target in modulating the diabetes physiology. In the present work, various 3D-Quantitative structure activity relationship (QSAR) techniques namely comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis, topomer CoMFA and molecular hologram QSAR are used to explore the structural requirements of triazole derivatives as DPP IV inhibitors. Different models generated by 3D QSAR studies had acceptable statistical values for further prediction of molecules. From the contour maps of QSAR results, important structural features are deduced. Substitutions on N₁ and N₂ of triazole ring with H-bond donor group enhances the biological activity. Aliphatic side chain, less bulky group, H-bond donor group and -COOH group on N₃ of triazole ring are vital for the DPP IV inhibition. Moreover, electron withdrawing side chain on the triazole ring improves the biological activity. Further, novel triazole derivatives were designed and docking results of these compounds proved the efficiency of the developed 3D QSAR model. In future, results of this study may provide promising DPP IV inhibitors for the treatment of diabetes. Communicated by Ramaswamy H. Sarma.

Molecular Design and Mechanism Analysis of Phthalic Acid Ester Substitutes: Improved Biodegradability in Processes of Sewage Treatment and Soil Remediation

Phthalic acid esters (PAEs) have the characteristics of environmental persistence. Therefore, improving the biodegradability of PAEs is the key to reducing the extent of ecological harm realized. Firstly, the scoring function values of PAEs docking with various degrading enzymes in sewage treatment were calculated. Based on this, a 3D-quantitative structure-activity relationship (3D-QSAR) model for PAE biodegradability was built, and 38 PAE substitutes were created. By predicting the endocrine-disrupting toxicity and functions of PAE substitutes, two types of PAE substitutes that are easily degraded by microorganisms, have low toxicity, and remain functional were successfully screened. Meanwhile, the differences in the mechanism of molecular degradation difference before and after PAE modification were analyzed based on the distribution characteristics of amino acid residues in the molecular docking complex. Finally, the photodegradability and microbial degradability of the PAE substitutes in the soil environment was evaluated. From the 3D-QSAR model design perspective, the modification mechanism of PAE substitutes suitable for sewage treatment and soil environment degradation was analyzed. We aim to improve the biodegradability of PAEs at the source and provide theoretical support for alleviating the environmental hazards of using PAEs.

A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance

Molecular design and evaluation for drug development and chemical safety assessment have been advanced by quantitative structure–activity relationship (QSAR) using artificial intelligence techniques, such as deep learning (DL). Previously, we have reported the high performance of prediction models molecular initiation events (MIEs) on the adverse toxicological outcome using a DL-based QSAR method, called DeepSnap-DL. This method can extract feature values from images generated on a three-dimensional (3D)-chemical structure as a novel QSAR analytical system. However, there is room for improvement of this system’s time-consumption. Therefore, in this study, we constructed an improved DeepSnap-DL system by combining the processes of generating an image from a 3D-chemical structure, DL using the image as input data, and statistical calculation of prediction-performance. Consequently, we obtained that the three prediction models of agonists or antagonists of MIEs achieved high prediction-performance by optimizing the parameters of DeepSnap, such as the angle used in the depiction of the image of a 3D-chemical structure, data-split, and hyperparameters in DL. The improved DeepSnap-DL system will be a powerful tool for computer-aided molecular design as a novel QSAR system.

Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design

Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development. This need for large chemical libraries led to the birth of high-throughput synthesis methods and combinatorial chemistry. Virtual combinatorial chemistry is based on the same principle as real chemistry—many different compounds can be generated from a few building blocks at once. The difference lies in its speed, as millions of compounds can be produced in a few seconds. On the other hand, many virtual screening methods, such as QSAR (Quantitative Sturcture-Activity Relationship), pharmacophore models, and molecular docking, have been developed to study these libraries. These models allow for the selection of molecules to be synthesized and tested with a high probability of success. The virtual combinatorial chemistry–virtual screening tandem has become a fundamental tool in the process of searching for and developing a drug, as it allows the process to be accelerated with extraordinary economic savings.