De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations

Screening of Potential Inhibitors Targeting the Main Protease Structure of SARS-CoV-2 via Molecular Docking, and Approach with Molecular Dynamics, RMSD, RMSF, H-Bond, SASA and MMGBSA

Severe Acute Respiratory Syndrome caused by a coronavirus is a recent viral infection. There is no scientific evidence or clinical trials to indicate that possible therapies have demonstrated results in suspected or confirmed patients. This work aims to perform a virtual screening of 1430 ligands through molecular docking and to evaluate the possible inhibitory capacity of these drugs about the Mpro protease of Covid-19. The selected drugs were registered with the FDA and available in the virtual drug library, widely used by the population. The simulation was performed using the MolAiCalD algorithm, with a Lamarckian genetic model (GA) combined with energy estimation based on rigid and flexible conformation grids. In addition, molecular dynamics studies were also performed to verify the stability of the receptor-ligand complexes formed through analyses of RMSD, RMSF, H-Bond, SASA, and MMGBSA. Compared to the binding energy of the synthetic redocking coupling (-6.8 kcal/mol/RMSD of 1.34 Å), which was considerably higher, it was then decided to analyze the parameters of only three ligands: ergotamine (-9.9 kcal/mol/RMSD of 2.0 Å), dihydroergotamine (-9.8 kcal/mol/RMSD of 1.46 Å) and olysio (-9.5 kcal/mol/RMSD of 1.5 Å). It can be stated that ergotamine showed the best interactions with the Mpro protease of Covid-19 in the in silico study, showing itself as a promising candidate for treating Covid-19.

6-Bromo quinazoline derivatives as cytotoxic agents: design, synthesis, molecular docking and MD simulation

Based on unselectively, several side effects and drug resistance of available anticancer agents, the development and research for novel anticancer agents is necessary. In this study, a new series of quinazoline-4(3H)-one derivatives having a thiol group at position 2 of the quinazoline ring (8a-8 h) were designed and synthesized as potential anticancer agents. The Chemical structures of all compounds were characterized by 1H-NMR, 13C-NMR, and Mass spectroscopy. The antiproliferative activity of all derivatives were determined against two cancer cell lines (MCF-7 and SW480) and one normal cell lines (MRC-5) by the MTT method. Cisplatin, Erlotinib and Doxorubicin were used as positive controls. The results of in vitro screening showed that 8a with an aliphatic linker to SH group was the most potent compound with IC50 values of 15.85 ± 3.32 and 17.85 ± 0.92 µM against MCF-7 and SW480 cell lines, respectively. 8a indicated significantly better potency compared to Erlotinib in the MCF-7 cell line. The cytotoxic results obtained from testing compound 8a on the normal cell line, revealing an IC50 value of 84.20 ± 1.72 µM, provide compelling evidence of its selectivity in distinguishing between tumorigenic and non-tumorigenic cell lines. Structure-activity relationship indicated that the variation in the anticancer activities of quinazoline-4(3H)-one derivatives was affected by different substitutions on the SH position. Molecular docking and MD simulation were carried out for consideration of the binding affinity of compounds against EGFR and EGFR-mutated. The binding energy of compounds 8a and 8c were calculated at -6.7 and - 5.3 kcal.mol- 1, respectively. Compounds 8a and 8c were found to establish hydrogen bonds and some other important interactions with key residue. The DFT analysis was also performed at the B3LYP/6-31 + G(d, p) level for compounds 8a, 8c and Erlotinib. Compound 8a was thermodynamically more stable than 8c. Also, the calculated theoretical and experimental data for the IR spectrum were in agreement. The obtained results delineated that the 8a can be considered an appropriate pharmacophore to develop as an anti-proliferative agent.

Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for de novo drug design and computational chemistry approaches

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of coronavirus disease (COVID-19) since its emergence in December 2019. As of January 2024, there has been over 774 million reported cases and 7 million deaths worldwide. While vaccination efforts have been successful in reducing the severity of the disease and decreasing the transmission rate, the development of effective therapeutics against SARS-CoV-2 remains a critical need. The main protease (Mpro) of SARS-CoV-2 is an essential enzyme required for viral replication and has been identified as a promising target for drug development. In this study, we report the identification of novel Mpro inhibitors, using a combination of deep reinforcement learning for de novo drug design with 3D pharmacophore/shape-based alignment and privileged fragment match count scoring components followed by hit expansions and molecular docking approaches. Our experimentally validated results show that 3 novel series exhibit potent inhibitory activity against SARS-CoV-2 Mpro, with IC50 values ranging from 1.3 μM to 2.3 μM and a high degree of selectivity. These findings represent promising starting points for the development of new antiviral therapies against COVID-19.

Phylogeny and evolution of SARS-CoV-2 during Delta and Omicron variant waves in India

SARS-CoV-2 evolution has continued to generate variants, responsible for new pandemic waves locally and globally. Varying disease presentation and severity has been ascribed to inherent variant characteristics and vaccine immunity. This study analyzed genomic data from 305 whole genome sequences from SARS-CoV-2 patients before and through the third wave in India. Delta variant was reported in patients without comorbidity (97%), while Omicron BA.2 was reported in patients with comorbidity (77%). Tissue adaptation studies brought forth higher propensity of Omicron variants to bronchial tissue than lung, contrary to observation in Delta variants from Delhi. Study of codon usage pattern distinguished the prevalent variants, clustering them separately, Omicron BA.2 isolated in February grouped away from December strains, and all BA.2 after December acquired a new mutation S959P in ORF1b (44.3% of BA.2 in the study) indicating ongoing evolution. Loss of critical spike mutations in Omicron BA.2 and gain of immune evasion mutations including G142D, reported in Delta but absent in BA.1, and S371F instead of S371L in BA.1 could explain very brief period of BA.1 in December 2021, followed by complete replacement by BA.2. Higher propensity of Omicron variants to bronchial tissue, probably ensured increased transmission while Omicron BA.2 became the prevalent variant possibly due to evolutionary trade-off. Virus evolution continues to shape the epidemic and its culmination.Communicated by Ramaswamy H. Sarma.

Enhancing biocatalyst performance through immobilization of lipase (Eversa® Transform 2.0) on hybrid amine-epoxy core-shell magnetic nanoparticles

Magnetic nanoparticles were functionalized with polyethylenimine (PEI) and activated with epoxy. This support was used to immobilize Lipase (Eversa® Transform 2.0) (EVS), optimization using the Taguchi method. XRF, SEM, TEM, XRD, FTIR, TGA, and VSM performed the characterizations. The optimal conditions were immobilization yield (I.Y.) of 95.04 ± 0.79 %, time of 15 h, ionic load of 95 mM, protein load of 5 mg/g, and temperature of 25 °C. The maximum loading capacity was 25 mg/g, and its stability in 60 days of storage showed a negligible loss of only 9.53 % of its activity. The biocatalyst demonstrated better stability at varying temperatures than free EVS, maintaining 28 % of its activity at 70 °C. It was feasible to esterify free fatty acids (FFA) from babassu oil with the best reaction of 97.91 % and ten cycles having an efficiency above 50 %. The esterification of produced biolubricant was confirmed by NMR, and it displayed kinematic viscosity and density of 6.052 mm2/s and 0.832 g/cm3, respectively, at 40 °C. The in-silico study showed a binding affinity of -5.8 kcal/mol between EVS and oleic acid, suggesting a stable substrate-lipase combination suitable for esterification.

Discovery of lupus nephritis targeted inhibitors based on De novo molecular design: comprehensive application of vinardo scoring, ADMET analysis, and molecular dynamics simulation

Lupus Nephritis (LN) is an autoimmune disease affecting the kidneys, and conventional drug studies have limitations due to its imprecise and complex pathogenesis. Therefore, the aim of this study was to design a novel Lupus Nephritis-targeted drug with good clinical due potential, high potency and selectivity by computer-assisted approach.NIK belongs to the serine/threonine protein kinase, which is gaining attention as a drug target for Lupus Nephritis. we used bioinformatics, homology modelling and sequence comparison analysis, small molecule ab initio design, ADMET analysis, molecular docking, molecular dynamics simulation, and MM/PBSA analysis to design and explore the selectivity and efficiency of a novel Lupus Nephritis-targeting drug, ClImYnib, and a classical NIK inhibitor, NIK SMI1. We used bioinformatics techniques to determine the correlation between lupus nephritis and the NF-κB signaling pathway. De novo drugs design was used to create a NIK-targeted inhibitor, ClImYnib, with lower toxicity, after which we used molecular dynamics to simulate NIK SMI1 against ClImYnib, and the simulation results showed that ClImYnib had better selectivity and efficiency. Our research delves into the molecular mechanism of protein ligands, and we have designed and validated an excellent NIK inhibitor using multiple computational simulation methods. More importantly, it provides an idea of target designing small molecules.Communicated by Ramaswamy H. Sarma.

GLA Mutations Suppress Autophagy and Stimulate Lysosome Generation in Fabry Disease

Fabry disease (FD) is an X-linked recessive inheritance lysosomal storage disorder caused by pathogenic mutations in the GLA gene leading to a deficiency of the enzyme alpha-galactosidase A (α-Gal A). Multiple organ systems are implicated in FD, most notably the kidney, heart, and central nervous system. In our previous study, we identified four GLA mutations from four independent Fabry disease families with kidney disease or neuropathic pain: c.119C>A (p.P40H), c.280T>C (C94R), c.680G>C (p.R227P) and c.801+1G>A (p.L268fsX3). To reveal the molecular mechanism underlying the predisposition to Fabry disease caused by GLA mutations, we analyzed the effects of these four GLA mutations on the protein structure of α-galactosidase A using bioinformatics methods. The results showed that these mutations have a significant impact on the internal dynamics and structures of GLA, and all these altered amino acids are close to the enzyme activity center and lead to significantly reduced enzyme activity. Furthermore, these mutations led to the accumulation of autophagosomes and impairment of autophagy in the cells, which may in turn negatively regulate autophagy by slightly increasing the phosphorylation of mTOR. Moreover, the overexpression of these GLA mutants promoted the expression of lysosome-associated membrane protein 2 (LAMP2), resulting in an increased number of lysosomes. Our study reveals the pathogenesis of these four GLA mutations in FD and provides a scientific foundation for accurate diagnosis and precise medical intervention for FD.

Synthesis, structural, characterization, antibacterial and antibiotic modifying activity, ADMET study, molecular docking and dynamics of chalcone (E)-1-(4-aminophenyl)-3-(4-nitrophenyl)prop-2-en-1-one in strains of Staphylococcus aureus carrying NorA and MepA efflux pumps

Chalcones have an open chain flavonoid structure that can be obtained from natural sources or by synthesis and are widely distributed in fruits, vegetables, and tea. They have a simple and easy to handle structure due to the α-β-unsaturated bridge responsible for most biological activities. The facility to synthesize chalcones combined with its efficient in combating serious bacterial infections make these compounds important agents in the fight against microorganisms. In this work, the chalcone (E)-1-(4-aminophenyl)-3-(4-nitrophenyl)prop-2-en-1-one (HDZPNB) was characterized by spectroscopy and electronic methods. In addition, microbiological tests were performed to investigate the modulator potential and efflux pump inhibition on S. aureus multi-resistant strains. The modulating effect of HDZPNB chalcone in association with the antibiotic norfloxacin, on the resistance of the S. aureus 1199 strain, resulted in increase the MIC. In addition, when HDZPNB was associated with ethidium bromide (EB), it caused an increase in the MIC value, thus not inhibiting the efflux pump. For the strain of S. aureus 1199B, carrying the NorA pump, the HDZPNB associated with norfloxacin showed no modulatory, and when the chalcone was used in association with EB, it had no inhibitory effect on the efflux pump. For the tested strain of S. aureus K2068, which carries the MepA pump, it can be observed that the chalcone together the antibiotic resulted in an increase the MIC. On the other hand, when chalcone was used in association with EB, it caused a decrease in bromide MIC, equal to the reduction caused by standard inhibitors. Thus, these results indicate that the HDZPNB could also act as an inhibitor of the S. aureus gene overexpressing pump MepA. The molecular docking reveals that chalcone has a good binding energies -7.9 for HDZPNB/MepA complexes, molecular dynamics simulations showed that Chalcone/MetA complexes showed good stability of the structure in an aqueous solution, and ADMET study showed that the chalcone has a good oral bioavailability, high passive permeability, low risk of efflux, low clearance rate and low toxic risk by ingestion. The microbiological tests show that the chalcone can be used as a possible inhibitor of the Mep A efflux pump.Communicated by Ramaswamy H. Sarma.

Anxiolytic effects of N-(4,5-dihydro-5-oxo-1,2-dithiolo-[4,3,b]-pyrrole-6-yl)-N-methylformamide, a pyrroloformamide isolated from a marine Streptomyces sp., in adult zebrafish by the 5-HT system

General anxiety disorders are among the most prevalent mental health problems worldwide. The emergence and development of anxiety disorders can be due to genetic (30-50%) or non-genetic (50-70%) factors. Despite medical progress, available pharmacotherapies are sometimes ineffective or can cause undesirable side effects. Thus, it becomes necessary to discover new safe and effective drugs against anxiety. This study evaluated the anxiolytic effect in adult zebrafish (Danio rerio) of a natural pyrroloformamide (PFD), N-(4,5-dihydro-5-oxo-1,2-dithiolo-[4,3,b]-pyrrole-6-yl)-N-methylformamide, isolated from a Streptomyces sp. bacterium strain recovered from the ascidian Eudistoma vannamei. The complete structure of PFD was determined by a detailed NMR analysis, including 1H-13C and 1H-15N-HBMC data. In addition, conformational and DFT computational studies also were performed. A group of fishes (n = 6) was treated orally with PFD (0.1, 0.5 and 1.0 mg/mL; 20 μL) and subjected to locomotor activity and light/dark tests, as well as, acute toxicity 96 h. The involvement of the GABAergic and serotonergic (5-HT) systems was investigated using flumazenil (a silent modulator of GABA receptor) and 5-HT1, 5-HT2A/2C and 5-HTR3A/3B receptors antagonists, known as pizotifen, granisetron and cyproheptadine, respectively. PFD was nontoxic, reduced locomotor activity and promoted the anxiolytic effect in zebrafish. Flumazenil did not inhibit the anxiolytic effect of the PFD via the GABAergic system. This effect was reduced by a pretreatment with pizotifen and granisetron, and was not reversed after treatment with cyproheptadine. Molecular docking and dynamics studies confirmed the interaction of PFD with the 5-HT receptor.Communicated by Ramaswamy H. Sarma.

Investigation of a set of flavonoid compounds as Helicobacter pylori urease inhibitors: insights from in silico studies

Helicobacter pylori (H. pylori) is a spiral, microaerophilic gram-negative bacterium, which is associated with the destruction of the lining of the stomach, leads to chronic inflammation of the stomach, which can cause stomach and duodenal ulcers. The problems caused by the treatment with antibiotics have caused researchers to use new approaches to treat infections caused by H. pylori, among them specific treatments with flavonoids. Urease enzyme, as one of the most important pathogenic and antigenic factors of this bacterium, is a suitable target for this purpose. In this study, the inhibitory effect of flavonoid compounds compared to acetohydroxamic acid on H. pylori urease enzyme was evaluated using molecular modeling methods. First, the interaction of flavonoids with urease enzyme compared with acetohydroxamic acid was investigated by molecular docking method to produce efficient docking poses. Then the physicochemical properties and toxicity of the best flavonoid compounds were analyzed using the swissadme server. Also, molecular dynamics calculations were performed to precisely understand the interactions between ligands and protein. The results of this study show that all the investigated flavonoid compounds are capable of inhibiting H. pylori urease. Among these compounds, six compounds chrysin, galangin, kaempferol, luteolin, morin and quercetin showed a greater tendency to bind to urease, compared to the acetohydroxamic acid inhibitor. These compounds are desirable in terms of physicochemical properties. This study also revealed that the flavonoids with their hydroxyl groups (-OH) play an important role during bond formation with amino acids Ala278, Ala169, His314, Asp362 and Asn168. Therefore, flavonoid compounds, due to their suitable location in the active site of the urease, create a more effective inhibition than the chemical drug acetohydroxamic acid and can be suitable candidates for the treatment of Helicobacter pylori under in vitro and in vivo investigations.Communicated by Ramaswamy H. Sarma.

Investigation of the mutated antimicrobial peptides to inhibit ACE2, TMPRSS2 and GRP78 receptors of SARS-CoV-2 and angiotensin II type 1 receptor (AT1R) as well as controlling COVID-19 disease

SARS-CoV-2 is a global problem nowadays. Based on studies, some human receptors are involved in binding to SARS-CoV-2. Thus, the inhibition of these receptors can be effective in the treatment of Covid-19. Because of the proven benefits of antimicrobial peptides (AMPs) and the side effects of chemical drugs, they can be known as an alternative to recent medicines. RCSB PDB to obtain PDB id, StraPep and PhytAMP to acquire Bio-AMPs information and 3-D structure, and AlgPred, Toxinpred, TargetAntiAngio, IL-4pred, IL-6pred, ACPred and Hemopred databases were used to find the best score peptide features. HADDOCK 2.2 was used for molecular docking analysis, and UCSF Chimera software version 1.15, SWISS-MODEL and BIOVIA Discovery Studio Visualizer4.5 were used for mutation and structure modeling. Furthermore, MD simulation results were achieved from GROMACS 4.6.5. Based on the obtained results, the Moricin peptide was found to have the best affinity for ACE2. Moreover, Bacteriocin leucocin-A had the highest affinity for GRP78, Cathelicidin-6 had the best affinity for AT1R, and Bacteriocin PlnK had the best binding affinity for TMPRSS2. Additionally, Bacteriocin glycocin F, Bacteriocin lactococcin-G subunit beta and Cathelicidin-6 peptides were the most common compounds among the four receptors. However, these peptides also have some side effects. Consequently, the mutation eliminated the side effects, and MD simulation results indicated that the mutation proved the result of the docking analysis. The effect of AMPs on ACE2, GRP78, TMPRSS2 and AT1R receptors can be a novel treatment for Covid-19.Communicated by Ramaswamy H. Sarma.

Innovative applications of artificial intelligence in zoonotic disease management

Zoonotic diseases, transmitted between humans and animals, pose a substantial threat to global public health. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the fight against diseases. This comprehensive review discusses the innovative applications of AI in the management of zoonotic diseases, including disease prediction, early diagnosis, drug development, and future prospects. AI-driven predictive models leverage extensive datasets to predict disease outbreaks and transmission patterns, thereby facilitating proactive public health responses. Early diagnosis benefits from AI-powered diagnostic tools that expedite pathogen identification and containment. Furthermore, AI technologies have accelerated drug discovery by identifying potential drug targets and optimizing candidate drugs. This review addresses these advancements, while also examining the promising future of AI in zoonotic disease control. We emphasize the pivotal role of AI in revolutionizing our approach to managing zoonotic diseases and highlight its potential to safeguard the health of both humans and animals on a global scale.

The search for new efficient inhibitors of SARS-COV-2 through the De novo drug design developed by artificial intelligence

The pandemic caused by Sars-CoV-2 is a viral infection that has generated one of the most significant health problems worldwide. Previous studies report the main protease (Mpro) as a potential target for this virus, as it is considered a crucial enzyme in mediating replication and viral transcription. This work presented the construction of new bioactive compounds for possible inhibition. The De novo molecular design of drugs method in the incremental construction of a ligant model within a receptor model was used, producing new structures with the help of artificial intelligence. The research algorithm and the scoring function responsible for predicting orientation and affinity in the molecular target at the time of coupling showed, as a result of the simulation, the compound with the highest bioaffinity value, Hit 998, with the energy of -17.62 kcal/mol, and synthetic viability close to 50%. While hit 1103 presented better synthetic viability (80%), its affinity energy of -10.28 kcal/mol. Both were compared with the reference linker N3, with a binding affinity of -7.5 kcal/mol. ADMET tests demonstrated that simulated compounds have a low risk of metabolic activation and do not exert effective distribution in the CNS, suggesting a pharmacokinetic mechanism based on local action, even with high topological polarity, which resulted in low oral bioavailability. In conclusion, MMGBSA, H-bonds, RMSD, SASA, and RMSF values were also obtained through molecular dynamics to verify the stability of the receptor-ligant complex within the active protein site to seek new therapeutic propositions in the fight against the pandemic.Communicated by Ramaswamy H. Sarma.

Comprehensive binding analysis of glycated myosin with furan derivatives via glucose by means of multi-spectroscopy techniques and molecular docking simulation

Myosin is an ideal binding receptor for aroma compounds and its functional properties are easily affected by glucose. The study comprehensively clarified the effects of glucose glycation-induced structural modifications of myosin on its binding ability with furan derivatives, including 2-methylfuran, 2-furfural, and 2-furfurylthiol. The results demonstrated that the binding levels of furan derivatives were obviously affected by the glycation levels of myosin due to the changes of myosin structure and surface. The increased glycation levels caused the unfolding of myosin structure and accelerated the aggregation, as were exhibited by the data of zeta potential, particle size, microstructure, and secondary structure. The glycated myosin with wrinkled surfaces favored the significant increase of hydrophobic interactions (31.59-69.50 μg), the more exposure of amino acid residues (3459-6048), the formation of free sulfhydryl groups (16.37-20.58 mmol/104g) and hydrogen bonds. These key (non)covalent linkages accounted for the generation of glycated myosin-odorants complex, including 2-furfurylthiol (29.17-47.87 %), thus enhancing the resultant binding ability as evidenced by the free furan derivatives concentrations, fluorescence quenching and molecular docking simulation analysis. The glycated myosin for 8 h bound highest concentrations of furan derivatives. The results will provide comprehensive data on the retention of aroma compounds in meat products.

Anti-Viral Activity of Bioactive Molecules of Silymarin against COVID-19 via In Silico Studies

The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection drove the global coronavirus disease 2019 (COVID-19) pandemic, causing a huge loss of human life and a negative impact on economic development. It is an urgent necessity to explore potential drugs against viruses, such as SARS-CoV-2. Silymarin, a mixture of herb-derived polyphenolic flavonoids extracted from the milk thistle, possesses potent antioxidative, anti-apoptotic, and anti-inflammatory properties. Accumulating research studies have demonstrated the killing activity of silymarin against viruses, such as dengue virus, chikungunya virus, and hepatitis C virus. However, the anti-COVID-19 mechanisms of silymarin remain unclear. In this study, multiple disciplinary approaches and methodologies were applied to evaluate the potential mechanisms of silymarin as an anti-viral agent against SARS-CoV-2 infection. In silico approaches such as molecular docking, network pharmacology, and bioinformatic methods were incorporated to assess the ligand-protein binding properties and analyze the protein-protein interaction network. The DAVID database was used to analyze gene functions, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment. TCMSP and GeneCards were used to identify drug target genes and COVID-19-related genes. Our results revealed that silymarin compounds, such as silybin A/B and silymonin, displayed triplicate functions against SARS-CoV-2 infection, including directly binding with human angiotensin-converting enzyme 2 (ACE2) to inhibit SARS-CoV-2 entry into the host cells, directly binding with viral proteins RdRp and helicase to inhibit viral replication and proliferation, and regulating host immune response to indirectly inhibit viral infection. Specifically, the targets of silymarin molecules in immune regulation were screened out, such as proinflammatory cytokines TNF and IL-6 and cell growth factors VEGFA and EGF. In addition, the molecular mechanism of drug-target protein interaction was investigated, including the binding pockets of drug molecules in human ACE2 and viral proteins, the formation of hydrogen bonds, hydrophobic interactions, and other drug-protein ligand interactions. Finally, the drug-likeness results of candidate molecules passed the criteria for drug screening. Overall, this study demonstrates the molecular mechanism of silymarin molecules against SARS-CoV-2 infection.

Synthesis and in silico study of chenodeoxycholic acid and its analogues as an alternative inhibitor of spike glycoprotein of SARS-CoV-2

COVID-19, caused by SARS-CoV-2, is a viral infection that has generated one of the most significant health problems in the world. Spike glycoprotein is a crucial enzyme in viral replication and transcription mediation. There are reports in the literature on using bile acid in the fight against this virus through in vitro tests. This work presents the synthesis of nine chenodeoxycholic acid derivatives (1-9), which were prepared by oxidation, acetylation, formylation, and esterification reactions, and the analogs 6-9 have not yet been reported in the literature and the possibility of conducting an in silico study of bile acid derivatives as a therapeutic alternative to combat the virus using glycoprotein as a macromolecular target. As a result, five compounds (1, 6-9) possessed favorable competitive interactions with the lowest energies compared to the native ligand (BLA), and the highlighted compound 9 got the best scores. At the same time, analog 1 presented the best ADME filter result. Molecular dynamics also simulated these compounds to verify their stability within the active protein site to seek new therapeutic propositions to fight against the pandemic. Physical and spectroscopic data have fully characterized all the compounds.Communicated by Ramaswamy H. Sarma.

Antimalarial drug discovery against malaria parasites through haplopine modification: An advanced computational approach

The widespread emergence of antimalarial drug resistance has created a major threat to public health. Malaria is a life-threatening infectious disease caused by Plasmodium spp., which includes Apicoplast DNA polymerase and Plasmodium falciparum cysteine protease falcipain-2. These components play a critical role in their life cycle and metabolic pathway, and are involved in the breakdown of erythrocyte hemoglobin in the host, making them promising targets for anti-malarial drug design. Our current study has been designed to explore the potential inhibitors from haplopine derivatives against these two targets using an in silico approach. A total of nine haplopine derivatives were used to perform molecular docking, and the results revealed that Ligands 03 and 05 showed strong binding affinity compared to the control compound atovaquone. Furthermore, these ligand-protein complexes underwent molecular dynamics simulations, and the results demonstrated that the complexes maintained strong stability in terms of RMSD (root mean square deviation), RMSF (root mean square fluctuation), and Rg (radius of gyration) over a 100 ns simulation period. Additionally, PCA (principal component analysis) analysis and the dynamic cross-correlation matrix showed positive outcomes for the protein-ligand complexes. Moreover, the compounds exhibited no violations of the Lipinski rule, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) predictions yielded positive results without indicating any toxicity. Finally, density functional theory (DFT) and molecular electrostatic potential calculations were conducted, revealing that the mentioned derivatives exhibited better stability and outstanding performance. Overall, this computational approach suggests that these haplopine derivatives could serve as a potential source for developing new, effective antimalarial drugs to combat malaria. However, further in vitro or in vivo studies might be conducted to determine their actual effectiveness.

Virtual screening and molecular dynamics simulation study of abyssomicins as potential inhibitors of COVID-19 virus main protease and spike protein

The lack of any effective cure for the infectious COVID-19 disease has created a sense of urgency and motivated the search for effective antiviral drugs. Abyssomicins are actinomyces-derived spirotetronates polyketides antibiotics known for their promising antibacterial, antitumor, and antiviral activities. In this study, computational approaches were used to investigate the binding mechanism and the inhibitory ability of 38 abyssomicins against the main protease (Mpro) and the spike protein receptor-binding domain (RBD) of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The results identified abyssomicins C, J, W, atrop-O-benzyl abyssomicin C, and atrop-O-benzyl desmethyl abyssomicin C as the most potential inhibitors of Mpro and RBD with binding energy ranges between -8.1 and -9.9 kcal mol-1; and between -6.9 and -8.2 kcal mol-1, respectively. Further analyses of physicochemical properties and drug-likeness suggested that all selected active abyssomicins, with the exception of abyssomicin J, obeyed Lipinski's rule of five. The stability of protein-ligand complexes was confirmed by performing molecular dynamics simulation for 100 ns and evaluating parameters such as such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), total number of contacts, and secondary structure. Prime/MM-GBSA (Molecular Mechanics-General Born Surface Area) and principal component analysis (PCA) analyses also confirmed the stable nature of protein-ligand complexes. Overall, the results showed that the studied abyssomicins have significant interactions with the selected protein targets; therefore, they were deemed viable candidates for further in vitro and in vivo evaluation.Communicated by Ramaswamy H. Sarma.

Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management

In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.

De novo design of bioactive phenol and chromone derivatives for inhibitors of Spike glycoprotein of SARS-CoV-2 in silico

This work presents the synthesis of 12 phenol and chromone derivatives, prepared by the analogs, and the possibility of conducting an in silico study of its derivatives as a therapeutic alternative to combat the SARS-CoV-2, pathogen responsible for COVID-19 pandemic, using its S-glycoprotein as a macromolecular target. After the initial screening for the ranking of the products, it was chosen which structure presented the best energy bond with the target. As a result, derivative 4 was submitted to a molecular growth study using artificial intelligence, where 8436 initial structures were obtained that passed through the interaction filters and similarity to the active glycoprotein pocket through the MolAICal computational package. Thus, 557 Hits with active configuration were generated, which is very promising compared to the BLA reference link for inhibiting the biological target. Molecular dynamics also simulated these compounds to verify their stability within the active protein site to seek new therapeutic propositions to fight against the pandemic. The Hit 48 and 250 are the most active compounds against SARS-CoV-2. In summary, the results show that the Hit 250 would be more active than the natural compound, which could be further developed for further testing against SARS-CoV-2. The study employs the de novo approach to design new drugs, combining artificial intelligence and molecular dynamics simulations to create efficient molecular structures. This research aims to contribute to the development of effective therapeutic strategies against the pandemic.

Exploring the natural products chemical space through a molecular search to discover potential inhibitors that target the hypoxia-inducible factor (HIF) prolyl hydroxylase domain (PHD)

Introduction: Hypoxia-inducible factor (HIF) prolyl hydroxylase domain (PHD) enzymes are major therapeutic targets of anemia and ischemic/hypoxia diseases. To overcome safety issues, liver failure, and problems associated with on-/off-targets, natural products due to their novel and unique structures offer promising alternatives as drug targets. Methods: In the current study, the Marine Natural Products, North African, South African, East African, and North-East African chemical space was explored for HIF-PHD inhibitors discovery through molecular search, and the final hits were validated using molecular simulation and free energy calculation approaches. Results: Our results revealed that CMNPD13808 with a docking score of -8.690 kcal/mol, CID15081178 with a docking score of -8.027 kcal/mol, CID71496944 with a docking score of -8.48 kcal/mol and CID11821407 with a docking score of -7.78 kcal/mol possess stronger activity than the control N-[(4-hydroxy-8-iodoisoquinolin-3-yl)carbonyl]glycine, 4HG (-6.87 kcal/mol). Interaction analysis revealed that the target compounds interact with Gln239, Tyr310, Tyr329, Arg383 and Trp389 residues, and chelate the active site iron in a bidentate manner in PHD2. Molecular simulation revealed that these target hits robustly block the PHD2 active site by demonstrating stable dynamics. Furthermore, the half-life of the Arg383 hydrogen bond with the target ligands, which is an important factor for PHD2 inhibition, remained almost constant in all the complexes during the simulation. Finally, the total binding free energy of each complex was calculated as CMNPD13808-PHD2 -72.91 kcal/mol, CID15081178-PHD2 -65.55 kcal/mol, CID71496944-PHD2 -68.47 kcal/mol, and CID11821407-PHD2 -62.06 kcal/mol, respectively. Conclusion: The results show the compounds possess good activity in contrast to the control drug (4HG) and need further in vitro and in vivo validation for possible usage as potential drugs against HIF-PHD2-associated diseases.

Deciphering the intrinsic dynamics of unphosphorylated IRAK4 kinase bound to type I and type II inhibitors

Interleukin-1 receptor-associated kinase 4 (IRAK4) is a vital protein involved in Toll-like and interleukin-1 receptor signal transduction. Several studies have reported regarding the crystal structure, dynamic properties, and interactions with inhibitors of the phosphorylated form of IRAK4. However, no dynamic properties of inhibitor-bound unphosphorylated IRAK4 have been previously studied. Herein, we report the intrinsic dynamics of unphosphorylated IRAK4 (uIRAK4) bound to type I and type II inhibitors. The corresponding apo and inhibitor-bound forms of uIRAK4 were subjected to three independent simulations of 500 ns (total 1.5 μs) each, and their trajectories were analyzed. The results indicated that all three systems were relatively stable, except for the type II inhibitor-bound form of uIRAK4, which exhibited less compact folding and higher solvent surface area. The intra-hydrogen bonds corroborated the structural deformation of the type-II inhibitor-bound complex, which could be attributed to the long molecular structure of the type-II inhibitor. Moreover, the type II inhibitor bound to uIRAK4 showed higher binding free energy with uIRAK4 than the type I inhibitor. The free energy landscape analysis showed a reorientation of Phe330 side chain from the DFG motif at different metastable states for all the systems. The intra-residual distance between residues Lys213, Glu233, Tyr262, and Phe330 suggests a functional interplay when the inhibitors are bound to uIRAK4, thereby hinting at their crucial role in the inhibition mechanism. Ultimately, the intrinsic dynamics study observed between type I/II inhibitor-bound forms of uIRAK4 may assist in better understanding the enzyme and designing therapeutic compounds.

A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation

The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.

Exploring the Potential of Black Soldier Fly Larval Proteins as Bioactive Peptide Sources through in Silico Gastrointestinal Proteolysis: A Cheminformatic Investigation

Despite their potential as a protein source for human consumption, the health benefits of black soldier fly larvae (BSFL) proteins following human gastrointestinal (GI) digestion are poorly understood. This computational study explored the potential of BSFL proteins to release health-promoting peptides after human GI digestion. Twenty-six proteins were virtually proteolyzed with GI proteases. The resultant peptides were screened for high GI absorption and non-toxicity. Shortlisted peptides were searched against the BIOPEP-UWM and Scopus databases to identify their bioactivities. The potential of the peptides as inhibitors of myeloperoxidase (MPO), NADPH oxidase (NOX), and xanthine oxidase (XO), as well as a disruptor of Keap1–Nrf2 protein–protein interaction, were predicted using molecular docking and dynamics simulation. Our results revealed that about 95% of the 5218 fragments generated from the proteolysis of BSFL proteins came from muscle proteins. Dipeptides comprised the largest group (about 25%) of fragments arising from each muscular protein. Screening of 1994 di- and tripeptides using SwissADME and STopTox tools revealed 65 unique sequences with high GI absorption and non-toxicity. A search of the databases identified 16 antioxidant peptides, 14 anti-angiotensin-converting enzyme peptides, and 17 anti-dipeptidyl peptidase IV peptides among these sequences. Results from molecular docking and dynamic simulation suggest that the dipeptide DF has the potential to inhibit Keap1–Nrf2 interaction and interact with MPO within a short time frame, whereas the dipeptide TF shows promise as an XO inhibitor. BSFL peptides were likely weak NOX inhibitors. Our in silico results suggest that upon GI digestion, BSFL proteins may yield high-GI-absorbed and non-toxic peptides with potential health benefits. This study is the first to investigate the bioactivity of peptides liberated from BSFL proteins following human GI digestion. Our findings provide a basis for further investigations into the potential use of BSFL proteins as a functional food ingredient with significant health benefits.

Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (Mpro) using molecular docking and deep learning methods

Highly transmissive and rapidly evolving Coronavirus disease-2019 (COVID-19), a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a global pandemic, which is one of the most researched viruses in the academia. Effective drugs to treat people with COVID-19 have yet to be developed to reduce mortality and transmission. Studies on the SARS-CoV-2 virus identified that its main protease (M^pro) might be a potential therapeutic target for drug development, as this enzyme plays a key role in viral replication. In search of potential inhibitors of M^pro, we developed a phytochemical library consisting of 2431 phytochemicals from 104 Korean medicinal plants that exhibited medicinal and antioxidant properties. The library was screened by molecular docking, followed by revalidation by re-screening with a deep learning method. Recurrent Neural Networks (RNN) computing system was used to develop an inhibitory predictive model using SARS coronavirus M^pro dataset. It was deployed to screen the top 12 compounds based on their docked binding affinity that ranged from -8.0 to -8.9 kcal/mol. The top two lead compounds, Catechin gallate and Quercetin 3-O-malonylglucoside, were selected depending on inhibitory potency against M^pro. Interactions with the target protein active sites, including His41, Met49, Cys145, Met165, and Thr190 were also examined. Molecular dynamics simulation was performed to analyze root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RG), solvent accessible surface area (SASA), and number of hydrogen bonds. Results confirmed the inflexible nature of the docked complexes. Absorption, distribution, metabolism, excretion, and toxicity (ADMET), as well as bioactivity prediction confirmed the pharmaceutical activities of the lead compound. Findings of this research might help scientists to optimize compatible drugs for the treatment of COVID-19 patients.

Deciphering the mechanism of resistance by novel double mutations in pncA in Mycobacterium tuberculosis using protein structural graphs (PSG) and structural bioinformatic approaches

The evolution of MDR and XDR-TB is a growing concern and public health safety threat around the world. Gene mutations are the prime cause of drug resistance in tuberculosis, however the reports of double mutations further aggravated the situation. Despite the large-scale genomic sequencing and identification of novel mutations, structure investigation of the protein is still required to structurally and functionally characterize these novel mutations to design novel drugs for improved clinical outcome. Hence, we used structural bioinformatics approaches i.e. molecular modeling, residues communication and molecular simulation to understand the impact of novel double S59Y-L85P, D86G-V180F and S104G-V130 M mutation on the structure, function of pncA encoded Pyrazinamidase (PZase) and resistance of Pyrazinamide (PZA). Our results revealed that these mutations alter the binding paradigm and destabilize the protein to release the drug. Protein commination network (PCN) revealed variations in the hub residues and sub-networks which consequently alter the internal communication and signaling. The region 1-75 demonstrated higher flexibility in the mutant structures and minimal by the wild type which destabilize of the internally arranged beta-sheets which consequently reduce the binding of PZA and potentially Fe ion in the mutants. Hydrogen bonding analysis further validated the findings. The total binding free energy (ΔG) for each complex i.e. wild type -7.46 kcal/mol, S59Y-L85P -5.21 kcal/mol, S104G-V130 M -5.33 kcal/mol while for the D86G-V180F mutant the TBE was calculated to be -6.26 kcal/mol. This further confirms that these mutations reduce the binding energy of PZA for PZase and causes resistance in the effective therapy for TB. The trajectories motion was also observed to be affected by these mutations. In conclusion, these mutations use destabilizing approach to reduce the binding of PZA and causes resistance. These features can be used to design novel structure-based drugs against Tuberculosis.

Virtual screening, molecular simulations and bioassays: Discovering novel microsomal prostaglandin E Synthase-1 (mPGES-1) inhibitors

Microsomal prostaglandin E synthase-1 (mPGES-1) is an inducible prostaglandin E synthase expressed following exposure to pro-inflammatory stimuli. The mPGES-1 enzyme represents a new target for the therapeutic treatment of acute and chronic inflammatory disorders and cancer. In the present study, compounds from the ZINC15 database with an indole scaffold were docked at the mPGES-1 binding site using Glide (high-throughput virtual screening [HTVS], standard precision [SP] and extra precision [XP]), and the stabilities of the complexes were determined by molecular simulation studies. Following HTVS, the top 10% compounds were retained and further screened by SP. Again, the top 10% of these compounds were retained. Finally, the Glide XP scores of the compounds were determined, 20% were analyzed, and the Prime MM-GBSA total free binding energies of the compounds were calculated. The molecular simulations (100 ns) of the reference ligand, LVJ, and the two best-scoring compounds were performed with the Desmond program to analyze the dynamics of the target protein-ligand complexes. In human lung cells treated with the hit compounds, cell viability by colorimetric method and PGE₂ levels by immunoassay method were determined. These in vitro experiments demonstrated that the two indole-containing hit compounds are potential novel inhibitors of mPGES-1 and are, therefore, potential therapeutic agents for cancer/inflammation therapies. Moreover, the compounds are promising lead mPGES-1 inhibitors for novel molecule design.

Advances in the discovery of new chemotypes through ultra-large library docking

INTRODUCTION

The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (M^pro).

AREAS COVERED

This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel M^pro inhibitors targeting the SARS-CoV-2 virus.

EXPERT OPINION

With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.

Structural-based design of HD-TAC7 PROteolysis TArgeting chimeras (PROTACs) candidate transformations to abrogate SARS-CoV-2 infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for about 672 million infections and 6.85 million deaths worldwide. Upon SARS-CoV-2 infection, Histone deacetylases (HDACs) hyperactivate the pro-inflammatory response resulting in stimulation of Acetyl-Coenzyme A and cholesterol for viral entry. HDAC3 inhibition results in the anti-inflammatory activity and reduction of pro-inflammatory cytokines that may restrict COVID-19 progression. Here, we have designed 44 conformational ensembles of previously known HD-TAC7 by enumerating torsions of dihedral angles tested for their binding preferences against HDAC3. Through scrutinizing their placements at active site and binding affinities, three hits were isolated. Cereblon (CRBN) is a well-known E3 ligase that facilitates Proteolysis Targeting Chimeras (PROTACs) targeting. Three entities, including HDAC3-binding moiety (4-acetamido-N-(2-amino-4 fluorophenyl) benzamide), a 6-carbon linker, and CRBN binding ligand (pomalidomide) were assembled to design 4 PROTACs followed by energy minimization and docking against HDAC3 and CRBN, respectively. Subsequent molecular dynamics (MD) and free energy analyses corroborated similar binding trends and favorable energy values. Among all cases, Met88, GLu106, Pro352, Trp380 and Trp388 residues of CRBN, and Pro23, Arg28, Lys194, Phe199, Leu266, Thr299 and Ile346 residues of HDAC3 were engaged in PROTAC binding. Thus, conformational dynamics of both HDAC3 and CRBN moieties are essential for the placement of PROTAC, resulting in target degradation. Overall, the proposed bifunctional small molecules may effectively target HDAC3, stimulating innate immune response to restrict COVID-19 hyperinflammation. This study supports the basis for designing new PROTACs by limiting the conformational search space that may prove more efficient for targeting the protein of interest.Communicated by Ramaswamy H. Sarma.

Identification of novel inhibitors against hantaviruses through 2D fingerprinting and molecular modeling approaches

With the immensely growing outbreaks of hantavirus with still no effective treatment available, there is an urgent need of exploring new computational approaches which will target potential virulent proteins that will eventually reduce its growth. In this study, an envelope glycoprotein, Gn, was targeted. The glycoproteins, which are the sole targets of neutralizing antibodies, drive virus entry via receptor-mediated endocytosis and endosomal membrane fusion. Inhibitors are herein proposed to negate its action mechanism. On the basis of the scaffolds of favipiravir, a FDA compound already used against hantavirus, a library was designed using a 2D fingerprinting approach. Upon molecular docking analysis, the top four docked compounds-(1) favipiravir (-4.5 kcal/mol), (2) N-hydroxy-3-oxo-3, 4-dihydropyrazine-2-carboxamide (-4.7 kcal/mol), (3) N, 5, 6-trimethyl-2-oxo-1H-pyrazine-3-carboxamide (-4.5 kcal/mol), and (4) 3-propyl-1H-pyrazin-2-one (-3.8)-were prioritized on the basis of the lowest binding energies score. Through molecular docking, the best-categorized compound was subjected to molecular dynamics simulation for a 100-ns time span. Molecular dynamics sheds light on each ligand behavior within the active site. Among the four complexes, only favipiravir and 6320122 compound were found to be stable inside the pocket. This is due to the presence of common rings, pyrazine and carboxamide ring, which make a significant interaction with active key residues Furthermore, the MMPB/GBSA binding free energy analysis calculated for all complexes supported the dynamics results by calculating the most stable values for favipiravir complex (-9.9933 and -8.6951 kcal/mol) and for 6320122 compound complex (-13.8675 and -9.3439 kcal/mol), which demonstrated that the selected compounds have a proper binding affinity with the targeted proteins. The hydrogen bond analysis similarly revealed a strong bonding interaction. The results yielded a strong interaction between the enzyme and the inhibitor throughout the simulation; thus, the inhibitor has the potential to become a lead compound and could be subjected to experimental evaluation to unveil their blockage ability.

GABAA and serotonergic receptors participation in anxiolytic effect of chalcones in adult zebrafish

The prevalence of anxiety is a significant public health problem, being the 24th leading cause of disability in individuals affected by this disorder. In this context, chalcones, a flavonoid subclass obtained from natural or synthetic sources, interact with central nervous system (CNS) receptors at the same binding site as benzodiazepines, the primary drugs used in the treatment of anxiety. Thus, our study investigates the anxiolytic effect of synthetic chalcones derived from the natural product 2-hydroxy-3,4,6-trimethoxyacetophenone isolated from Croton anisodontus Müll.Arg. in modulating anxiolytic activity via GABAergic and serotoninergic neurotransmission in an adult zebrafish model. Chalcones 1 and 2 were non-toxic to adult zebrafish and showed anxiolytic activity via GABAA receptors. Chalcone 2 also had its anxiolytic action reversed by the antagonist granisetron, indicating the participation of serotonergic receptors 5HTR3A/3B in the anxiolytic effect. In addition, molecular docking results showed that chalcones have a higher affinity for the GABAA receptor than DZP and binding in the same region of the DZP binding site, indicating a similar effect to the drug. Furthermore, the interaction of chalcones with GABAA and 5-HT3A receptors demonstrates the anxiolytic effect potential of these molecules.Communicated by Ramaswamy H. Sarma.

Pathfinder-Driven Chemical Space Exploration and Multiparameter Optimization in Tandem with Glide/IFD and QSAR-Based Active Learning Approach to Prioritize Design Ideas for FEP+ Calculations of SARS-CoV-2 PLpro Inhibitors

A global pandemic caused by the SARS-CoV-2 virus that started in 2020 and has wreaked havoc on humanity still ravages up until now. As a result, the negative impact of travel restrictions and lockdowns has underscored the importance of our preparedness for future pandemics. The main thrust of this work was based on addressing this need by traversing chemical space to design inhibitors that target the SARS-CoV-2 papain-like protease (PLpro). Pathfinder-based retrosynthesis analysis was used to generate analogs of GRL-0617 using commercially available building blocks by replacing the naphthalene moiety. A total of 10 models were built using active learning QSAR, which achieved good statistical results such as an R2 > 0.70, Q2 > 0.64, STD Dev < 0.30, and RMSE < 0.31, on average for all models. A total of 35 ideas were further prioritized for FEP+ calculations. The FEP+ results revealed that compound 45 was the most active compound in this series with a ΔG of −7.28 ± 0.96 kcal/mol. Compound 5 exhibited a ΔG of −6.78 ± 1.30 kcal/mol. The inactive compounds in this series were compound 91 and compound 23 with a ΔG of −5.74 ± 1.06 and −3.11 ± 1.45 kcal/mol. The combined strategy employed here is envisaged to be of great utility in multiparameter lead optimization efforts, to traverse chemical space, maintaining and/or improving the potency as well as the property space of synthetically aware design ideas.

Evaluation of decursin and its isomer decursinol angelate as potential inhibitors of human glutamate dehydrogenase activity through in silico and enzymatic assay screening

Glutaminolysis is a typical hallmark of malignant tumors across different cancers. Glutamate dehydrogenase (GDH, GLUD1) is one such enzyme involved in the conversion of glutamate to α-ketoglutarate. High levels of GDH are associated with numerous diseases and is also a prognostic marker for predicting metastasis in colorectal cancer. Therefore, inhibiting GDH can be a crucial therapeutic target. Here in this study, we performed molecular docking analysis of 8 different plants derived single compounds collected from pubChem database for screening and selected decursin (DN) and decursinol angelate (DA). We performed molecular dynamics simulation (MD), monitored the stability, interaction for protein and docked ligand at 50 ns, and evaluated the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) free energy calculation on the twoselected compounds along with a standard inhibitor epigallocatechin gallate (EGCG) as reference. The final results showed the formation of stable hydrogen bond interactions by DN and DA in the residues of R400 and Y386 at the ADP activation site of GDH, which was important for the selective inhibition of GDH activity. Additionally, the total binding energy of DN and DA were -115.5 kJ/mol and -106.2 kJ/mol, which was higher than the standard reference GDH inhibitor EGCG (-92.8 kJ/mol). Furthermore, biochemical analysis for GDH inhibition substantiated our computational results and established DN and DA as novel GDH inhibitor. The percentage of IC₅₀ inhibition for DN and DA were 1.035 μM and 1.432 μM. Conclusively, DN and DA can be a novel therapeutic drug for inhibition of glutamate dehydrogenase.

Molecular dynamics simulations highlight the altered binding landscape at the spike-ACE2 interface between the Delta and Omicron variants compared to the SARS-CoV-2 original strain

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.1.529 variant (Omicron), represents a significant deviation in genetic makeup and function compared to previous variants. Following the BA.1 sublineage, the BA.2 and BA.3 Omicron subvariants became dominant, and currently the BA.4 and BA.5, which are quite distinct variants, have emerged. Using molecular dynamics simulations, we investigated the binding characteristics of the Delta and Omicron (BA.1) variants in comparison to wild-type (WT) at the interface of the spike protein receptor binding domain (RBD) and human angiotensin converting enzyme-2 (ACE2) ectodomain. The primary aim was to compare our molecular modelling systems with previously published observations, to determine the robustness of our approach for rapid prediction of emerging future variants. Delta and Omicron were found to bind to ACE2 with similar affinities (−39.4 and −43.3 kcal/mol, respectively) and stronger than WT (−33.5 kcal/mol). In line with previously published observations, the energy contributions of the non-mutated residues at the interface were largely retained between WT and the variants, with F456, F486, and Y489 having the strongest energy contributions to ACE2 binding. Further, residues N440K, Q498R, and N501Y were predicted to be energetically favourable in Omicron. In contrast to Omicron, which had the E484A and K417N mutations, intermolecular bonds were detected for the residue pairs E484:K31 and K417:D30 in WT and Delta, in accordance with previously published findings. Overall, our simplified molecular modelling approach represents a step towards predictive model systems for rapidly analysing arising variants of concern.

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.

Investigating the structure-activity relationship of marine polycyclic batzelladine alkaloids as promising inhibitors for SARS-CoV-2 main protease (Mpro)

Over a span of two years ago, since the emergence of the first case of the novel coronavirus (SARS-CoV-2) in China, the pandemic has crossed borders causing serious health emergencies, immense economic crisis and impacting the daily life worldwide. Despite the discovery of numerous forms of precautionary vaccines along with other recently approved orally available drugs, yet effective antiviral therapeutics are necessarily needed to hunt this virus and its variants. Historically, naturally occurring chemicals have always been considered the primary source of beneficial medications. Considering the SARS-CoV-2 main protease (M^pro) as the duplicate key element of the viral cycle and its main target, in this paper, an extensive virtual screening for a focused chemical library of 15 batzelladine marine alkaloids, was virtually examined against SARS-CoV-2 main protease (M^pro) using an integrated set of modern computational tools including molecular docking (MDock), molecule dynamic (MD) simulations and structure-activity relationships (SARs) as well. The molecular docking predictions had disclosed four promising compounds including batzelladines H–I (8–9) and batzelladines F-G (6–7), respectively according to their prominent ligand-protein energy scores and relevant binding affinities with the (M^pro) pocket residues. The best two chemical hits, batzelladines H–I (8–9) were further investigated thermodynamically though studying their MD simulations at 100 ns, where they showed excellent stability within the accommodated (M^pro) pocket. Moreover, SARs studies imply the crucial roles of the fused tricyclic guanidinic moieties, its degree of unsaturation, position of the N–OH functionality and the length of the side chain as a spacer linking between two active sites, which disclosed fundamental structural and pharmacophoric features for efficient protein-ligand interaction. Such interesting findings are greatly highlighting further in vitro/vivo examinations regarding those marine natural products (MNPs) and their synthetic equivalents as promising antivirals.

Exploration and validation of Taraxacum mongolicum anti-cancer effect

Taraxacum mongolicum gained a lot of concern and was applied in 93 formulas in China due to its fame as a traditional Chinese medicine. The earliest recorded application of Taraxacum mongolicum was traced back to the Han dynasty. Generations of doctors boosted the usage and enriched the pharmacological mechanism. Clinical application of the Taraxacum mongolicum is flourishing as it treats multiple diseases. This study aims to explore the anti-cancer effect, retrieve the active ingredients and screen the key targets of Taraxacum mongolicum in cancer therapy. We collected and evaluated 10 key active compounds to investigate the anti-cancer effect via 69 significant targets and a variety of biological processes and pathways. Gene Ontology (GO) enrichment analysis uncovered targets associated with protein phosphorylation, cell proliferation and apoptotic processes via regulation of kinases, ATP and enzyme binding activities. Half of the top 20 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were directly involved in cancer. Based on standard selection criteria, seven hub targets were obtained. These targets functioned through distinct patterns and pathways in realizing the anti-cancer effect. Molecular docking was conducted to validate the potential combination between compounds and hub targets to explore the pharmacological mechanism of key compounds in Taraxacum mongolicum against cancer. In summary, our findings indicate that the famous and widely used Chinese herb, Taraxacum mongolicum, shows good anti-cancer effect through its active compounds, targeted genes, and multiple involved biological processes. The results may provide a theoretical basis for subsequent experimental validation and drug development of Taraxacum mongolicum extract against cancer.

Investigation of the binding and dynamic features of A.30 variant revealed higher binding of RBD for hACE2 and escapes the neutralizing antibody: A molecular simulation approach

With the emergence of Delta and Omicron variants, many other important variants of SARS-CoV-2, which cause Coronavirus disease-2019, including A.30, are reported to increase the concern created by the global pandemic. The A.30 variant, reported in Tanzania and other countries, harbors spike gene mutations that help this strain to bind more robustly and to escape neutralizing antibodies. The present study uses molecular modelling and simulation-based approaches to investigate the key features of this strain that result in greater infectivity. The protein-protein docking results for the spike protein demonstrated that additional interactions, particularly two salt-bridges formed by the mutated residue Lys484, increase binding affinity, while the loss of key residues at the N terminal domain (NTD) result in a change to binding conformation with monoclonal antibodies, thus escaping their neutralizing effects. Moreover, we deeply studied the atomic features of these binding complexes through molecular simulation, which revealed differential dynamics when compared to wild type. Analysis of the binding free energy using MM/GBSA revealed that the total binding free energy (TBE) for the wild type receptor-binding domain (RBD) complex was -58.25 kcal/mol in contrast to the A.30 RBD complex, which reported -65.59 kcal/mol. The higher TBE for the A.30 RBD complex signifies a more robust interaction between A.30 variant RBD with ACE2 than the wild type, allowing the variant to bind and spread more promptly. The BFE for the wild type NTD complex was calculated to be -65.76 kcal/mol, while the A.30 NTD complex was estimated to be -49.35 kcal/mol. This shows the impact of the reported substitutions and deletions in the NTD of A.30 variant, which consequently reduce the binding of mAb, allowing it to evade the immune response of the host. The reported results will aid the development of cross-protective drugs against SARS-CoV-2 and its variants.

Benchmarking the ability of novel compounds to inhibit SARS-CoV-2 main protease using steered molecular dynamics simulations

BACKGROUND

The SARS-CoV-2 main protease (M^pro) is an attractive target in the COVID-19 drug development process. It catalyzes the polyprotein's translation from viral RNA and specifies a particular cleavage site. Due to the absence of identical cleavage specificity in human cell proteases, targeting M^pro with chemical compounds can obstruct the replication of the virus.

METHODS

To explore the potential binding mechanisms of 1,2,3-triazole scaffolds in comparison to co-crystallized inhibitors 11a and 11b towards M^pro, we herein utilized molecular dynamics and enhanced sampling simulation studies.

RESULTS AND CONCLUSION

All the 1,2,3-triazole scaffolds interacted with catalytic residues (Cys145 and His41) and binding pocket residues of M^pro involving Met165, Glu166, Ser144, Gln189, His163, and Met49. Furthermore, the adequate binding free energy and potential mean force of the topmost compound 3h was comparable to the experimental inhibitors 11a and 11b of M^pro. Overall, the current analysis could be beneficial in developing the SARS-CoV-2 M^pro potential inhibitors.

E2UbcH5B-derived peptide ligands target HECT E3-E2 binding site and block the Ub-dependent SARS-CoV-2 egression: A computational study

Homologous to E6AP carboxyl-terminus (HECT)-type E3 ligase performs ubiquitin (Ub)-proteasomal protein degradation via forming a complex with E2∼Ub. Enveloped viruses including SARS-CoV-2 escape from the infected cells by harnessing the E-class vacuolar protein-sorting (ESCRT) machinery and mimic the cellular system through PPAY motif-based linking to HECT Ub ligase activity. In the present study, we have characterized the binding pattern of E2^UbcH5B to HECT domains of NEDD4L, WWP1, WWP2, HECW1, and HECW2 through in silico analysis to isolate the E2^UbcH5B-specific peptide inhibitors that may target SARS-CoV-2 viral egression. Molecular dynamics analysis revealed more opening of E2^UbcH5B-binding pocket upon binding to HECT^NEDD4L, HECT^WWP1, HECT^WWP2, HECT^HECW1, and HECT^HECW2. We observed similar binding pattern for E2^UbcH5B and mentioned HECT domains as previously reported for HECT^NEDD4L where Trp762, Trp709, and Trp657 residues of HECT^NEDD4L, HECT^WWP1, and HECT^WWP2 are involved in making contacts with Ser94 residue of E2^UbcH5B. Similarly, corresponding to HECT^NEDD4L Tyr756 residue, HECT^WWP1, HECT^WWP2, HECT^HECW1, and HECT^HECW2-specific Phe703, Phe651, Phe1387, and Phe1353 residues execute interaction with E2^UbcH5B. Our analysis suggests that corresponding to Cys942 of HECT^NEDD4L, Cys890, Cys838, Cys1574, and Cys1540 residues of HECT^WWP1, HECT^WWP2, HECT^HECW1, and HECT^HECW2, respectively are involved in E2-to-E3 Ub transfer. Furthermore, MM-PBSA free energy calculations revealed favorable energy values for E2^UbcH5B-HECT complexes along with the individual residue contributions. Subsequently, two E2^UbcH5B-derived peptides (His55-Phe69 and Asn81-Ala96) were tested for their binding abilities against HECT domains of NEDD4L, WWP1, WWP2, HECW1, and HECW2. Their binding was validated through substitution of Phe62, Pro65, Ile84, and Cys85 residues into Ala, which revealed an impaired binding, suggesting that the proposed peptide ligands may selectively target E2-HECT binding and Ub-transfer. Collectively, we propose that peptide-driven blocking of E2-to-HECT Ub loading may limit SARS-CoV-2 egression and spread in the host cells.

Computational approach to decode the mechanism of curcuminoids against neuropathic pain

BACKGROUND

Curcumin (CUR), demethoxycurcumin (DMC) and bisdemethoxycurcumin (BDMC) are the main components of turmeric that commonly used to treat neuropathic pain (NP). However, the mechanism of the therapy is not sufficiently clarified. Herein, network pharmacology, molecular docking and molecular dynamics (MD) approaches were used to investigate the mechanism of curcuminoids for NP treatment.

METHODS

Active targets of curcuminoids were obtained from the Swiss Target database, and NP-related targets were retrieved from GeneCards, OMIM, Drugbank and TTD databases. A protein-protein interaction (PPI) network was built to screen the core targets. Furthermore, DAVID was used for GO and KEGG pathway enrichment analyses. Interactions between potential targets and curcuminoids were assessed by molecular docking and the MD simulations were run for 100ns to validate the docking results on the top six complexes.

RESULTS

CUR, DMC, and BDMC had 100, 99 and 100 targets respectively. After overlapping with NP there were 33, 33 and 31 targets respectively. PPI network analysis of TOP 10 core targets, TNF, GSK3β were common targets of curcuminoids. Molecular docking and MD results indicated that curcuminoids bind strongly with the core targets. The GO and KEGG showed that curcuminoids regulated nitrogen metabolism, the serotonergic synapse and ErbB signaling pathway to alleviate NP. Furthermore, specific targets in these three compounds were also analysed at the same time.

CONCLUSIONS

This study systematically explored and compared the anti-NP mechanism of curcuminoids, providing a novel perspective for their utilization.

Design of SARS-CoV-2 Main Protease Inhibitors Using Artificial Intelligence and Molecular Dynamic Simulations

Drug design is a time-consuming and cumbersome process due to the vast search space of drug-like molecules and the difficulty of investigating atomic and electronic interactions. The present paper proposes a computational drug design workflow that combines artificial intelligence (AI) methods, i.e., an evolutionary algorithm and artificial neural network model, and molecular dynamics (MD) simulations to design and evaluate potential drug candidates. For the purpose of illustration, the proposed workflow was applied to design drug candidates against the main protease of severe acute respiratory syndrome coronavirus 2. From the ∼140,000 molecules designed using AI methods, MD analysis identified two molecules as potential drug candidates.

Identification of Aloe-derived natural products as prospective lead scaffolds for SARS-CoV-2 main protease (Mpro) inhibitors

In the past two years, the COVID-19 pandemic has caused over 5 million deaths and 250 million infections worldwide. Despite successful vaccination efforts and emergency approval of small molecule therapies, a diverse range of antivirals is still needed to combat the inevitable resistance that will arise from new SARS-CoV-2 variants. The main protease of SARS-CoV-2 (M^pro) is an attractive drug target due to the clinical success of protease inhibitors against other viruses, such as HIV and HCV. However, in order to combat resistance, various chemical scaffolds need to be identified that have the potential to be developed into potent inhibitors. To this end, we screened a high-content protease inhibitor library against M^proin vitro, in order to identify structurally diverse compounds that could be further developed into antiviral leads. Our high-content screening efforts retrieved 27 hits each with > 50% inhibition in our M^pro FRET assay. Of these, four of the top inhibitor compounds were chosen for follow-up due to their potency and drugability (Lipinski's rules of five criteria): anacardic acid, aloesin, aloeresin D, and TCID. Further analysis via dose response curves revealed IC₅₀ values of 6.8 μM, 38.9 μM, 125.3 μM, and 138.0 μM for each compound, respectively. Molecular docking studies demonstrated that the four inhibitors bound at the catalytic active site of M^pro with varying binding energies (-7.5 to -5.6 kcal/mol). Furthermore, M^pro FRET assay kinetic studies demonstrated that M^pro catalysis is better represented by a sigmoidal Hill model than the standard Michaelis-Menten hyperbola, indicating substantial cooperativity of the active enzyme dimer. This result suggests that the dimerization interface could be an attractive target for allosteric inhibitors. In conclusion, we identified two closely-related natural product compounds from the Aloe plant (aloesin and aloeresin D) that may serve as novel scaffolds for M^pro inhibitor design and additionally confirmed the strongly cooperative kinetics of M^pro proteolysis. These results further advance our knowledge of structure-function relationships in M^pro and offer new molecular scaffolds for inhibitor design.

A multidrug efflux protein in Mycobacterium tuberculosis; tap as a potential drug target for drug repurposing

Tuberculosis (TB) is a serious communicative disease caused by Mycobacterium tuberculosis. Although there are vaccines and drugs available to treat the disease, they are not efficient, moreover, multidrug-resistant TB (MDR-TB) become a major hurdle in its therapy. These MDR strains utilize the multidrug efflux pump as a decisive weapon to fight against antitubercular drugs. Tap membrane protein was observed as a crucial multidrug efflux pump in M. tuberculosis and its critical implication in MDR-MTB development makes it an effective drug target. In the present study, we have utilized various in silico approaches to predict the applicability of FDA-approved ion channel inhibitors and blockers as therapeutic leads against Tuberculosis by targeting multidrug efflux protein; Tap in MTB. Tap protein structure is predicted by Phyre2 server followed by model refinement, validation, physio-chemical catheterization and target prediction. Further, the interaction between Tap protein and ligands were analysed by molecular docking and MD simulation run of 100 ns. Based on implication and compatibility, 18 FDA-approved ion channel inhibitors and blockers are selected as a ligand against the Tap protein and eventually observed five ligands; Glimepiride, Flecainide, Flupiritine, Nimodipine and Amlodipine as promising compounds which have exhibited the significant stable interaction with Tap protein and are proposed to modulate or interfere with its activity. These compounds illustrated the substantial docking score and total binding enthalpy more than -7 kcal/mol and -42 kcal/mol respectively which implies that the selected FDA-approved compounds can spontaneously interact with the Tap protein to modulate its function. This study proposed Tap protein as a prominent drug target in MTB and investigated compounds that show considerable interaction with the Tap protein as potential therapeutic molecules. These interactions may lead to modulating or inhibit the activity of drug efflux protein thereby making MTB susceptible to antitubercular drugs.

Discovery of novel Glutaminase allosteric inhibitors through drug repurposing and comparative MMGB/PBSA and molecular dynamics simulation

GLS1 enzymes (Glutaminase C (GAC) and kidney-type Glutaminase (KGA)) are gaining prominence as a target for tumor treatment including lung, breast, kidney, prostate, and colorectal. To date, several medicinal chemistry studies are being conducted to develop new and effective inhibitors against GLS1 enzymes. Telaglenastat, a drug that targets the allosteric site of GLS1, has undergone clinical trials for the first time for the therapy of solid tumors and hematological malignancies. A comprehensive computational investigation is performed to get insights into the inhibition mechanism of the Telaglenastat. Some novel inhibitors are also proposed against GLS1 enzymes using the drug repurposing approach using 2D-fingerprinting virtual screening method against 2.4 million compounds, application of pharmacokinetics, Molecular Docking, and Molecular Dynamic (MD) Simulations. A TIP3P water box of 10 Å was defined to solvate both enzymes to improve MD simulation reliability. The dynamics results were validated further by the MMGB/PBSA binding free energy method, RDF, and AFD analysis. Results of these computational analysis revealed a stable binding affinity of Telaglenastat, as well as an FDA approved drug Astemizole (IC₅₀ ∼ 0.9 nM) and a novel para position oriented methoxy group containing Chembridge compound (Chem-64284604) that provides an effective inhibitory action against GAC and KGA.