In Silico Study of Pubchem Compounds for Solanum torvum as Antiviral Agent against SARS-CoV-2

Background: The recent epidemic outbreak of a novel coronavirus called SARS-CoV-2 has caused suffering among many people in the form of respiratory tract infection. Currently, there are no targeted drugs, and effective treatment options remain limited. Objective: In order to rapidly discover new compounds for clinical purposes, in silico drug design and virtual drug screening have been initiated to identify new drug leads that target the main protease of the COVID-19 virus. Mpro is a key CoV enzyme, which plays a pivotal role in mediating viral replication and transcription, making it an attractive drug target for this virus. Methods: The present study was done to investigate the PubChem compounds of an ayurvedic herb Solanum torvum as an effective antiviral agent against COVID-19. The PubChem compounds like Torvoside H, Torvoside A, Torvoside E, Torvoside F, Torvonin A, 2,3,4-trimethyltriacontane, Torvanol A Q27134802, 5-hexatriacontanone, Jurubine, Tritriacontan-3-one, Torvanol A, Chlorogenone Spirostane-3,6-dione of Solanum torvum were downloaded from NCBI PubChem database acting as ligands for protein ligand docking. The 3D structure of the viral MPro (PDB ID: 6yb7) was retrieved from the RCSB PDB database. The active sites and binding sites were analyzed, and Docking molecular simulations were realized among a total of 12 ligands against COVID-19. Results: The PubChem compounds from the fruits of Solanum torvum showed good docking score and protein-ligand interaction, indicating that the PubChem compounds can cure the COVID-19 disease and act as an effective antiviral agent. Conclusion: Most of the PubChem compounds in the fruits of Solanum torvum showed better paramagnetic parameters.

Molecular Docking and Interaction Studies of Identified Abscisic Acid Receptors in Oryza sativa: An In-Silico Perspective on Comprehending Stress Tolerance Mechanisms

Background: Abiotic stresses affect plants in several ways and as such, phytohormones such as abscisic acid (ABA) play an important role in conferring tolerance towards these stresses. Hence, to comprehend the role of ABA and its interaction with receptors of the plants, a thorough investigation is essential. Aim: The current study aimed to identify the ABA receptors in Oryza sativa, to find the receptor that binds best with ABA and to examine the mutations present to help predict better binding of the receptors with ABA Methods: Protein sequences of twelve PYL (Pyrabactin resistance 1) and seven PP2C (type 2C protein phosphatase) receptors were retrieved from Rice Annotation Project database and their 3D structures were predicted using RaptorX. Protein-ligand molecular docking studies between PYL and ABA was performed using AutoDock 1.5.6, followed by 100ns molecular dynamic simulation studies using Desmond to determine the acceptable conformational changes after docking via root mean square deviation RMSD plot analysis. Protein-protein docking was then carried out in three sets: PYL-PP2Cs, PYL-ABA-PP2C and PYL(mut)-ABA-PP2C to scrutinize changes in structural conformations and binding energies between complexes. The amino acids of interest were mapped at its respective genomic coordinates using SNP-seek database to ascertain if there were any naturally occurring single nucleotide polymorphisms (SNPs) responsible for triggering rice PYLs mutations Results: Initial protein-ligand docking studies revealed good binding between the complexes, wherein PYL6-ABA complex showed the best energy of -8.15 kcal/mol. The 100ns simulation studies revealed changes in the RMSD values after docking, indicating acceptable conformational changes. Furthermore, mutagenesis study performed at specific PYL-ABA interacting residues followed by downstream PYL(mut)-ABA-PP2C protein-protein docking results after induction of mutations demonstrated a binding energy of -8.17 kcal/mol for PP2C79-PYL11-ABA complex. No naturally occurring SNPs that were responsible for triggering rice PYL mutations were identified when specific amino acid coordinates were mapped at respective genomic coordinates. Conclusion: Thus, the present study provides valuable insights on the interactions of ABA receptors in rice and induced mutations in PYL11 that can enhance the downstream interaction with PP2C

Engineering a Pseudo-26-kDa Schistosoma Glutathione Transferase from bovis/haematobium for Structure, Kinetics, and Ligandin Studies

Glutathione transferases (GSTs) are the main detoxification enzymes in schistosomes. These parasitic enzymes tend to be upregulated during drug treatment, with Schistosoma haematobium being one of the species that mainly affect humans. There is a lack of complete sequence information on the closely related bovis and haematobium 26-kDa GST isoforms in any database. Consequently, we engineered a pseudo-26-kDa S. bovis/haematobium GST (Sbh26GST) to understand structure–function relations and ligandin activity towards selected potential ligands. Sbh26GST was overexpressed in Escherichia coli as an MBP-fusion protein, purified to homogeneity and catalyzed 1-chloro-2,4-dinitrobenzene-glutathione (CDNB-GSH) conjugation activity, with a specific activity of 13 μmol/min/mg. This activity decreased by ~95% in the presence of bromosulfophthalein (BSP), which showed an IC50 of 27 µM. Additionally, enzyme kinetics revealed that BSP acts as a non-competitive inhibitor relative to GSH. Spectroscopic studies affirmed that Sbh26GST adopts the canonical GST structure, which is predominantly α-helical. Further extrinsic 8-anilino-1-naphthalenesulfonate (ANS) spectroscopy illustrated that BSP, praziquantel (PZQ), and artemisinin (ART) might preferentially bind at the dimer interface or in proximity to the hydrophobic substrate-binding site of the enzyme. The Sbh26GST-BSP interaction is both enthalpically and entropically driven, with a stoichiometry of one BSP molecule per Sbh26GST dimer. Enzyme stability appeared enhanced in the presence of BSP and GSH. Induced fit ligand docking affirmed the spectroscopic, thermodynamic, and molecular modelling results. In conclusion, BSP is a potent inhibitor of Sbh26GST and could potentially be rationalized as a treatment for schistosomiasis.

Galaxy workflows for fragment-based virtual screening: a case study on the SARS-CoV-2 main protease

We present several workflows for protein-ligand docking and free energy calculation for use in the workflow management system Galaxy. The workflows are composed of several widely used open-source tools, including rDock and GROMACS, and can be executed on public infrastructure using either Galaxy's graphical interface or the command line. We demonstrate the utility of the workflows by running a high-throughput virtual screening of around 40000 compounds against the SARS-CoV-2 main protease, a system which has been the subject of intense study in the last year.

Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemicalprotein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods. Compared with AlphaFold2-based protein-ligand docking, Portal Learning significantly improved the performance by 79% in PR-AUC and 27% in ROC-AUC, respectively. The superior performance of Portal Learning allowed us to target previously “undruggable” proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific inquiry.

Capturing Acyltransferase(s) Transforming Final Step in the Biosynthesis of a Major Iridoid Glycoside, (Picroside-II) in a Himalayan Medicinal Herb, Picrorhiza kurroa

BackgroundPicrorhiza kurroa has been reported as an age-old ayurvedic hepatoprotection to treat hepatic disorders due to the presence of iridoids such as picroside-II (P-II), picroside-I, and kutkoside. The acylation of catalpol and vanilloyl coenzyme A by acyltransferases (ATs) is critical step in P-II biosynthesis. Since accumulation of P-II occurs only in roots, rhizomes and stolons, uprooting of this critically endangered herb has been the only source of this compound. Recently, we reported that P-II acylation likely happen in roots, while stolons serve as the vital P-II storage compartment. Therefore, developing an alternate engineered platform for P-II biosynthesis require identification of P-II specific AT/s.Methods and results In that direction, egg-NOG function annotated 815 ATs from de novo RNA sequencing of tissue culture based ‘shoots-only’ system and nursery grown shoots, roots, and stolons varying in P-II content, were cross-compared in silico to arrive at ATs sequences unique and/or common to stolons and roots. Verification for organ and accession-wise upregulation in gene expression of these ATs by qPCR has shortlisted six putative ‘P-II-forming’ ATs. Further, six-frame translation, ab initio protein structure modelling and protein-ligand molecular docking of these ATs signified one MBOAT domain containing AT with preferential binding to the vanillic acid CoA thiol ester as well as with P-II., implying that this could be potential AT decorating final structure of P-II. ConclusionOrgan-wise comparative transcriptome mining coupled with reverse transcription real time qPCR and protein-ligand docking led to the identification of an acyltransferases, contributing to the final structure of P-II.

Multi-state Modeling of G-protein Coupled Receptors at Experimental Accuracy

The family of G-protein coupled receptors (GPCRs) is one of the largest protein families in the human genome. GPCRs transduct chemical signals from extracellular to intracellular regions via a conformational switch between active and inactive states upon ligand binding. While experimental structures of GPCRs remain limited, high-accuracy computational predictions are now possible with AlphaFold2. However, AlphaFold2 only predicts one state and is biased towards the inactive conformation. Here, a multi-state prediction protocol is introduced that extends AlphaFold2 to predict either active or inactive states at very high accuracy using state-annotated templated GPCR databases. The predicted models accurately capture the main structural changes upon activation of the GPCR at the atomic level. The models were also highly successful in predicting ligand binding poses via protein-ligand docking. We expect that high accuracy GPCR models in both activation states will promote understanding in GPCR activation mechanisms and drug discovery for GPCRs. At the time, the new protocol paves the way towards capturing the dynamics of proteins at high-accuracy via machine-learning methods.

Tangeretin and its Derivatives: An Integrative Bioinformatic Study of Obesity and Related Immunodeficiency

Obesity is a complex and major public health concern known to exacerbate many diseases. There are increasing evidences stating the obese people due to adiposity are getting more susceptible to immune deficiency disorders. Tangeretin is a key member of flavonoids reported to have many favourable biological activities. In search of novel leads in ameliorating obesity and related immunodeficiency, the present study is aimed at the in silico evaluation of tangeretin derivatives to assess their biological role. Initially tangeretin derivatives are designed by molecular manipulation approach.Drug likeness and bioactivity score prediction was done using Molinspiration web tool. Swiss ADME prediction and toxicological predictions were performed. In silico Molecular Docking studies were performed by employing a flexible ligand docking approach using Schrodinger on the protein targets namely leptin, Fat mass and obesity associated protein (FTO), Pancreatic lipase, Peroxisome proliferated receptor (PPARɣ) and NADH oxidase. Further the electronic parameters were computed for the best fitted ligands by DFT analysis. The evaluation of results was made based on Glide (Schrodinger) dock score. Out of 18 screened compounds, some of them showed the best docking scores with the targets when compared with the standard (Lovastatin). Particularly the two ligands (L-13 and L-8) showed the best binding score with all five targets. Moreover, DFT analysis carried out for the tangeretin and best fitted ligands (L13 and L8) substantiated the other in silico studies. These findings probably provide excellent lead candidates for the development of therapeutic drugs in combating obesity and related immune deficiency.

Identification of Peptides as Novel Inhibitors to Target IFN-γ, IL-3, and TNF-α in Systemic Lupus Erythematosus

Autoimmune disorder is a chronic immune imbalance which is developed through a series of pathways. The defect in B cells, T cells, and lack of self-tolerance has been greatly associated with the onset of many types of autoimmune complications including rheumatoid arthritis, systemic lupus erythematosus (SLE), multiple sclerosis, and chronic inflammatory demyelinating polyneuropathy. The SLE is an autoimmune disease with a common type of lupus that causes tissue and organ damage due to the wide spread of inflammation. In the current study, twenty anti-inflammatory peptides derived from plant and animal sources were docked as ligands or peptides counter to proinflammatory cytokines. Interferon gamma (IFN-γ), interleukin 3 (IL-3), and tumor necrosis factor alpha (TNF-α) were targeted in this study as these are involved in the pathogenesis of SLE in many clinical studies. Two docking approaches (i.e., protein-ligand docking and peptide-protein docking) were employed in this study using Molecular Operating Environment (MOE) software and HADDOCK web server, respectively. Amongst docked twenty peptides, the peptide DEDTQAMMPFR with S -score of -11.3018 and HADDOCK score of − 10.3 ± 2.5 kcal/mol showed the best binding interactions and energy validation with active amino acids of IFN-γ protein in both docking approaches. Depending upon these results, this peptide could be used as a potential drug candidate to target IFN-γ, IL-3, and TNF-α proteins to control inflammatory events. Other peptides (i.e., QEPQESQQ and FRDEHKK) also revealed good binding affinity with IFN-γ with S -scores of -10.98 and -10.55, respectively. Similarly, the peptides KHDRGDEF, FRDEHKK, and QEPQESQQ showed best binding interactions with IL-3 with S -scores of -8.81, -8.64, and -8.17, respectively.