Analysis and prediction pathways of natural products and their cytotoxicity against HeLa cell line protein using docking, molecular dynamics and ADMET

Thiadiazine-thiones as inhibitors of leishmania pteridine reductase (PTR1) target: investigations and in silico approach

Leishmaniasis is a widespread parasitic disease and is one of the major public health concerns in developing countries. Many drugs have been identified for leishmania as targets, but the potential toxicity and long-term treatment remain the most significant problems in terms of further development. The present study employed physicochemical investigations, structure-based virtual screening, ADMET analysis, molecular dynamics simulation, and MM-PBSA, to identify potential compounds for Leishmania. We evaluated 30,926 natural products from the NPASS database, and four potentials passed the pharmacokinetic ADMET studies and were verified using the molecular docking approach. Molecular docking results showed good binding interaction of the compounds with the active site of leishmania pteridine reductase enzyme PTR1, with compound TTC1 showing FRED and Autodock binding energies of -10.33 and -10.94, respectively, which were comparable with the antileishmania drugs of Allopurinol, Miltefosine and the original ligand, methotrexate. TTC1 compound was found to be favorable for hydrophobic interaction with PTR1. In addition, the physicochemical properties of the compounds were studied using the SwissADME web server. All compounds followed Lipinski's rule of five and can be considered as good oral candidates. The analysis of the 100 ns molecular dynamics simulation results based on the best-docked TTC1 with PTR1 receptor demonstrates stable interactions, and the complex undergoes low conformational fluctuations. The average of the calculated binding free energy of the TTC1-1e7w complex is (-68.67 kJ/mol), and the result demonstrated that the TTC1 promoted stability to the Leishmania-PTR1 complex. The potential compounds can be further explored for their antileishmanial activity.Communicated by Ramaswamy H. Sarma.

Chemistry, biosynthesis, and theranostics of antioxidant flavonoids and polyphenolics of genus Rhododendron: an overview

The genus Rhododendron is an ancient and most widely distributed genus of the family Ericaceae consisting of evergreen plant species that have been utilized as traditional medicine since a very long time for the treatment of various ailments including pain, asthma, inflammation, cold, and acute bronchitis. The chemistry of polyphenolics isolated from a number of species of the genus Rhododendron has been investigated. During the currently designed study, an in-depth study on the phytochemistry, natural distribution, biosynthesis, and pharmacological properties including their potential capability as free radical scavengers has been conducted. This work provides structural characteristics of phenolic compounds isolated from the species of Rhododendron with remarkable antioxidant potential. In addition, biosynthesis and theranostic study have also been encompassed with the aims to furnish a wide platform of valuable information for designing of new drug entities. The detailed information including names, structural features, origins, classification, biosynthetic pathways, theranostics, and pharmacological effects of about 171 phenolics and flavonoids isolated from the 36 plant species of the genus Rhododendron with the antioxidant potential has been covered in this manuscript. This study demonstrated that species of Rhododendron genus have excellent antioxidant activities and great potential as a source for natural health products. This comprehensive review might serve as a foundation for more investigation into the Rhododendron genus.

PSSM-Sumo: deep learning based intelligent model for prediction of sumoylation sites using discriminative features

Post-translational modifications (PTMs) are fundamental to essential biological processes, exerting significant influence over gene expression, protein localization, stability, and genome replication. Sumoylation, a PTM involving the covalent addition of a chemical group to a specific protein sequence, profoundly impacts the functional diversity of proteins. Notably, identifying sumoylation sites has garnered significant attention due to their crucial roles in proteomic functions and their implications in various diseases, including Parkinson's and Alzheimer's. Despite the proposal of several computational models for identifying sumoylation sites, their effectiveness could be improved by the limitations associated with conventional learning methodologies. In this study, we introduce pseudo-position-specific scoring matrix (PsePSSM), a robust computational model designed for accurately predicting sumoylation sites using an optimized deep learning algorithm and efficient feature extraction techniques. Moreover, to streamline computational processes and eliminate irrelevant and noisy features, sequential forward selection using a support vector machine (SFS-SVM) is implemented to identify optimal features. The multi-layer Deep Neural Network (DNN) is a robust classifier, facilitating precise sumoylation site prediction. We meticulously assess the performance of PSSM-Sumo through a tenfold cross-validation approach, employing various statistical metrics such as the Matthews Correlation Coefficient (MCC), accuracy, sensitivity, specificity, and the Area under the ROC Curve (AUC). Comparative analyses reveal that PSSM-Sumo achieves an exceptional average prediction accuracy of 98.71%, surpassing existing models. The robustness and accuracy of the proposed model position it as a promising tool for advancing drug discovery and the diagnosis of diverse diseases linked to sumoylation sites.

Estrogen Receptor Alpha Binders for Hormone-Dependent Forms of Breast Cancer: e-QSAR and Molecular Docking Supported by X-ray Resolved Structures

Cancer, a life-disturbing and lethal disease with a high global impact, causes significant economic, social, and health challenges. Breast cancer refers to the abnormal growth of cells originating from breast tissues. Hormone-dependent forms of breast cancer, such as those influenced by estrogen, prompt the exploration of estrogen receptors as targets for potential therapeutic interventions. In this study, we conducted e-QSAR molecular docking and molecular dynamics analyses on a diverse set of inhibitors targeting estrogen receptor alpha (ER-α). The e-QSAR model is based on a genetic algorithm combined with multilinear regression analysis. The newly developed model possesses a balance between predictive accuracy and mechanistic insights adhering to the OECD guidelines. The e-QSAR model pointed out that sp2-hybridized carbon and nitrogen atoms are important atoms governing binding profiles. In addition, a specific combination of H-bond donors and acceptors with carbon, nitrogen, and ring sulfur atoms also plays a crucial role. The results are supported by molecular docking, MD simulations, and X-ray-resolved structures. The novel results could be useful for future drug development for ER-α.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

Mechanistic QSAR modeling derived virtual screening, drug repurposing, ADMET and in-vitro evaluation to identify anticancer lead as lysine-specific demethylase 5a inhibitor

A lysine-specific demethylase is an enzyme that selectively eliminates methyl groups from lysine residues. KDM5A, also known as JARID1A or RBP2, belongs to the KDM5 Jumonji histone demethylase subfamily. To identify novel molecules that interact with the LSD5A receptor, we created a quantitative structure-activity relationship (QSAR) model. A group of 435 compounds was used in a study of the quantitative relationship between structure and activity to guess the IC50 values for blocking LASD5A. We used a genetic algorithm-multilinear regression-based quantitative structure-activity connection model to forecast the bioactivity (PIC50) of 1615 food and drug administration pharmaceuticals from the zinc database with the goal of repurposing clinically used medications. We used molecular docking, molecular dynamic simulation modelling, and molecular mechanics generalised surface area analysis to investigate the molecule's binding mechanism. A genetic algorithm and multi-linear regression method were used to make six variable-based quantitative structure-activity relationship models that worked well (R2 = 0.8521, Q2LOO = 0.8438, and Q2LMO = 0.8414). ZINC000000538621 was found to be a new hit against LSD5A after a quantitative structure-activity relationship-based virtual screening of 1615 zinc food and drug administration compounds. The docking analysis revealed that the hit molecule 11 in the KDM5A binding pocket adopted a conformation similar to the pdb-6bh1 ligand (docking score: -8.61 kcal/mol). The results from molecular docking and the quantitative structure-activity relationship were complementary and consistent. The most active lead molecule 11, which has shown encouraging results, has good absorption, distribution, metabolism, and excretion (ADME) properties, and its toxicity has been shown to be minimal. In addition, the MTT assay of ZINC000000538621 with MCF-7 cell lines backs up the in silico studies. We used molecular mechanics generalise borne surface area analysis and a 200-ns molecular dynamics simulation to find structural motifs for KDM5A enzyme interactions. Thus, our strategy will likely expand food and drug administration molecule repurposing research to find better anticancer drugs and therapies.Communicated by Ramaswamy H. Sarma.

Green Chemistry and In silico Techniques for Synthesis of Novel Pyranopyrazole and Pyrazolo-pyrano-pyrimidine Derivatives as Promising Antifungal Agents

BACKGROUND

Every year Invasive Fungal Infections (IFI) are globally affecting millions of people. Candida albicans and Aspergillus niger have been reported as the most infectious and mortality-inducing fungal strains among all pathogenic fungi.

AIMS & OBJECTIVES

To tackle this problem in the current study Pyranopyrazoles and Pyrazolopyrano- pyrimidine derivatives were developed using molecular hybridization, green chemistry and one-pot multicomponent reaction.

MATERIALS AND METHODS

In the present work, New Chemical entities (NCE's) were developed on the basis of Structure activity relationship. All designed NCE's were screened for ADMET studies using the QikProp module of Schrodinger software. NCE's with zero violations were further docked on the crystal structure of 14α demethylase, cytochrome P450 and thymidine synthase (PDB ID: 5V5Z, 7SHI, 1BID). Selected molecules were synthesized using green chemistry techniques and evaluated for in vitro antifungal activity against Candida albicans and Aspergillus niger.

RESULTS AND DISCUSSION

Designed NCE's (B1-12 and C1-11) showed favorable results in ADMET studies. In the docking study six compounds from series-B and five molecules from series- C showed good dock score and binding interaction when compared with the standard drugs. Compounds B-3 and C-4 showed the highest zone of inhibition activity against Candida albicans, where as B-1 and C-3 had shown highest zone of inhibition activity against Aspergillus niger.

CONCLUSION

Bicyclic ring (series B) showed better activity as compare to fused tricyclic ring (series C).

Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features

Sumoylation is a post-translation modification (PTM) mechanism that involves many critical biological processes, such as gene expression, localizing and stabilizing proteins, and replicating the genome. Moreover, sumoylation sites are associated with different diseases, including Parkinson's and Alzheimer's. Due to its vital role in the biological process, identifying sumoylation sites in proteins is significant for monitoring protein functions and discovering multiple diseases. Therefore, in the literature, several computational models utilizing conventional ML methods have been introduced to classify sumoylation sites. However, these models cannot accurately classify the sumoylation sites due to intrinsic limitations associated with the conventional learning methods. This paper proposes a robust computational model (called Deep-Sumo) for predicting sumoylation sites based on a deep-learning algorithm with efficient feature representation methods. The proposed model employs a half-sphere exposure method to represent protein sequences in a feature vector. Principal Component Analysis is applied to extract discriminative features by eliminating noisy and redundant features. The discriminant features are given to a multilayer Deep Neural Network (DNN) model to predict sumoylation sites accurately. The performance of the proposed model is extensively evaluated using a 10-fold cross-validation test by considering various statistical-based performance measurement metrics. Initially, the proposed DNN is compared with the traditional learning algorithm, and subsequently, the performance of the Deep-Sumo is compared with the existing models. The validation results show that the proposed model reports an average accuracy of 96.47%, with improvement compared with the existing models. It is anticipated that the proposed model can be used as an effective tool for drug discovery and the diagnosis of multiple diseases.

Computational screening of natural products to identify potential inhibitors for human neuropilin-1 (NRP1) receptor to abrogate the binding of SARS-CoV-2 and host cell

Recently, a new variant B.1.1.529 or Omicron variant and its sub-variants (BA2.75, BA.5) of SARS-CoV-2 (Severe acute respiratory virus 2) have been reported with a larger number of mutations in the spike protein and particularly in the RBD (receptor-binding domain). The omicron (B.1.1.529) variant has aggravated the pandemic situation further and needs more analysis for therapeutic development. Keeping in view the urgency of the required data, the current study used molecular modeling and simulation-based methods to target the NRP1 (Neuropilin 1) protein to halt the entry into the host cell. Employing a molecular screening approach to screen the North-East African natural compounds database (NEANCDB) revealed Subereamine B with a docking score of -8.44 kcal/mol, Zinolol with the docking score of -8.05 while Subereamine A with a docking score of -7.88 kcal/mol as the best hits against NRP1. Molecular simulation-based further validation revealed stable dynamics, good structural packing, and dynamic residues flexibility index. Moreover, hydrogen bonding fraction analysis demonstrated the interactions remained sustained during the simulation. Furthermore, the total binding free energy for Subereamine B was -44.24 ±0.91 kcal/mol, for Zinolol -34.32 ±0.40 kcal/mol while for Subereamine A the TBE was calculated to be -41.78 ± 0.36 kcal/mol respectively. This shows that the two arginine-based alkaloids, i.e. Subereamine B and Subereamine A could inhibit the NRP1 more strongly than Zinolol. In conclusion, this study provides a basis for the development of novel drugs against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Design, synthesis, spectroscopic characterization, in-vitro antibacterial evaluation and in-silico analysis of polycaprolactone containing chitosan-quercetin microspheres

Aim of present study was to synthesize a novel chitosan-quercetin (CTS-QT) complex by making a carbodiimide linkage using maleic anhydride as cross-linker and to investigate its enhanced antibacterial and antioxidant activities as compare to pure CTS and QT. Equimolar concentration of QT and maleic anhydride were used to react with 100 mg CTS to form CTS-QT complex. For this purpose, three bacterial strains namely E. Coli, S. Aureus and P. Aeruginosa were used for in-vitro antibacterial analysis (ZOI, MIC, MBC, checker board and time kill assay). Later molecular docking studies were performed on protein structure of E. Coli to assess binding affinity of pure QT and CTS-QT complex. MD simulations with accelerated settings were used to explore the protein-ligand complex's binding interactions and stability. Antioxidant profile was determined by performing DPPH• radical scavenging assay, total antioxidant capacity (TAC) and total reducing power (TRP) assays. Delivery mechanism to CTS-QT complex was improved by synthesizing polycaprolactone containing microspheres (CTS-QT-PCL-Levo-Ms) using Levofloxacin as model drug to enhance their antibacterial profile. Resulted microspheres were evaluated by particle size, charge, surface morphology, in-vitro drug release and hemolytic profile and are all were found within limits. Antibacterial assay revealed that CTS-QT-PCL-Levo-Ms showed more than two folds increased bactericidal activity against E. Coli and P. Aeruginosa, while 1.5 folds against S. Aureus. Green colored formation of phosphate molybdate complexes with highest 85 ± 1.32% TAC confirmed its antioxidant properties. Furthermore, molecular docking and dynamics studies revealed that CTS-QT was embedded nicely within the active pocket of UPPS with binding energy greater than QT with RSMD value of below 1.5. Conclusively, use of maleic acid, in-vitro and in-silico antimicrobial studies confirm the emergence of CTS-QT complex containing microspheres as novel treatment strategy for all types of bacterial infections.Communicated by Ramaswamy H. Sarma.

Pharmacophore-based virtual screening, molecular docking, and molecular dynamics studies for the discovery of novel FLT3 inhibitors

FLT3 is considered a potential target of acute myeloid leukemia therapy. In this study, we applied a computer-aided methodology unifying molecular docking and pharmacophore screening to identify potent inhibitors against FLT3. To investigate the pharmacophore area and binding mechanism of FLT3, the reported co-crystallized Gilteritinib ligand was docked into the active site using Glide XP. Based on the docking results, we identified structure-based pharmacophore characteristics resistant to potent FLT3 inhibitors. The best hypothesis was corroborated using test and decoy sets, and the verified hypo was utilized to screen the chemical database. The hits from the pharmacophore-based screening were then screened again using a structure-based method that included molecular docking at various precisions; the selected molecules were further examined and refined using drug-like filters and ADMET analysis. Finally, two hits were picked out for molecular dynamic simulation. The results showed two hits were expected to have potent inhibitory activity and excellent ADMET characteristics, and they might be used as new leads in the development of FLT3 inhibitors.Communicated by Ramaswamy H. Sarma.

Cyclohexane-1,3-dione Derivatives as Future Therapeutic Agents for NSCLC: QSAR Modeling, In Silico ADME-Tox Properties, and Structure-Based Drug Designing Approach

The abnormal expression of the c-Met tyrosine kinase has been linked to the proliferation of several human cancer cell lines, including non-small-cell lung cancer (NSCLC). In this context, the identification of new c-Met inhibitors based on heterocyclic small molecules could pave the way for the development of a new cancer therapeutic pathway. Using multiple linear regression (MLR)-quantitative structure-activity relationship (QSAR) and artificial neural network (ANN)-QSAR modeling techniques, we look at the quantitative relationship between the biological inhibitory activity of 40 small molecules derived from cyclohexane-1,3-dione and their topological, physicochemical, and electronic properties against NSCLC cells. In this regard, screening methods based on QSAR modeling with density-functional theory (DFT) computations, in silico pharmacokinetic/pharmacodynamic (ADME-Tox) modeling, and molecular docking with molecular electrostatic potential (MEP) and molecular mechanics-generalized Born surface area (MM-GBSA) computations were used. Using physicochemical (stretch-bend, hydrogen bond acceptor, Connolly molecular area, polar surface area, total connectivity) and electronic (total energy, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels) molecular descriptors, compound 6d is identified as the optimal scaffold for drug design based on in silico screening tests. The computer-aided modeling developed in this study allowed us to design, optimize, and screen a new class of 36 small molecules based on cyclohexane-1,3-dione as potential c-Met inhibitors against NSCLC cell growth. The in silico rational drug design approach used in this study led to the identification of nine lead compounds for NSCLC therapy via c-Met protein targeting. Finally, the findings are validated using a 100 ns series of molecular dynamics simulations in an aqueous environment on c-Met free and complexed with samples of the proposed lead compounds and Foretinib drug.

Rational identification of small molecules derived from 9,10-dihydrophenanthrene as potential inhibitors of 3CLpro enzyme for COVID-19 therapy: a computer-aided drug design approach

Small molecules such as 9,10-dihydrophenanthrene derivatives have remarkable activity toward inhibition of SARS-CoV-2 3CLpro and COVID-19 proliferation, which show a strong correlation between their structures and bioactivity. Therefore, these small compounds could be suitable for clinical pharmaceutical use against COVID-19. The objective of this study was to remodel the structures of 9,10-dihydrophenanthrene derivatives to achieve a powerful biological activity against 3CLpro and favorable pharmacokinetic properties for drug design and discovery. Therefore, by the use of bioinformatics techniques, we developed robust 3D-QSAR models that are capable of describing the structure-activity relationship for 46 molecules based on 9,10-dihydrophenanthrene derivatives using CoMFA/SE (R 2 = 0.97, Q 2 = 0.81, R 2 pred = 0.95, c R 2 p = 0.71) and CoMSIA/SEHDA (R 2 = 0.94, Q 2 = 0.76, R 2 pred = 0.91, c R 2 p = 0.65) techniques. Accordingly, 96 lead compounds were generated based on a template molecule that showed the highest observed activity in vitro (T40, pIC50 = 5.81) and predicted their activities and bioavailability in silico. The rational screening outputs of 3D-QSAR, Molecular docking, ADMET, and MM-GBSA led to the identification of 9 novel modeled molecules as potent noncovalent drugs against SARS-CoV-2-3CLpro. Finally, by molecular dynamics simulations, the stability and structural dynamics of 3CLpro free and complex (PDB code: 6LU7) were discussed in the presence of samples of 9,10-dihydrophenanthrene derivative in an aqueous environment. Overall, the retrosynthesis of the proposed drug compounds in this study and the evaluation of their bioactivity in vitro and in vivo may be interesting for designing and discovering a new drug effective against COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11224-022-02004-z.