Valorization of coffee bean processing waste for bioethanol production: comparison and evaluation of mass transfer effects in fermentations using free and encapsulated cells of Saccharomyces cerevisiae

Coffee husk, an agricultural waste abundant in carbohydrates and nutrients, is typically discarded through landfills, mixed with animal fodder, or incinerated. However, in alignment with sustainable development principles, researchers worldwide are exploring innovative methods to harness the value of coffee husk, transforming it into profitable products. One such avenue is the biotechnological approach to bioethanol production from agricultural wastes, offering an eco-friendly alternative to mitigate the adverse effects of fossil fuels. This study delves into the feasibility of utilizing coffee husk as a substrate for bioethanol production, employing and comparing various hydrolysis methods. The enzymatic hydrolysis method outshone thermochemical and thermal approaches, yielding 1.84 and 3.07 times more reducing sugars in the hydrolysate, respectively. In examining bioethanol production, a comparison between free and encapsulated cells in enzyme hydrolysate revealed that free-cell fermentation faced challenges due to cell viability issues. Under specific fermentation conditions, bioethanol yield (0.59 and 0.83 g of bioethanol/g of reducing sugar) and productivity (0.1 and 0.12 g/L h) were achieved for free and encapsulated cells, respectively. However, it was noted that bioethanol production by encapsulated cells was more significantly influenced by internal mass transfer effects, as indicated by the Thiele modulus and effectiveness factor. In conclusion, our findings underscore the potential of coffee husk as a valuable substrate for bioethanol production, showcasing its viability in contributing to sustainable and eco-friendly practices.

A computational study on structural and functional consequences of nsSNPs in human dopa decarboxylase

The Dopa Decarboxylase (DDC) gene plays an important role in the synthesis of biogenic amines such as dopamine, serotonin, and histamine. Non-synonymous single nucleotide polymorphisms (nsSNPs) in the DDC gene have been linked with various neurodegenerative disorders. In this study, a comprehensive in silico analysis of nsSNPs in the DDC gene was conducted to assess their potential functional consequences and associations with disease outcomes. Using publicly available databases, a complete list of nsSNPs in the DDC gene was obtained. 29 computational tools and algorithms were used to characterise the effects of these nsSNPs on protein structure, function, and stability. In addition, the population-based association studies were performed to investigate possible associations between specific nsSNPs and arthritis. Our research identified four novel DDC gene nsSNPs that have a major impact on the structure and function of proteins. Through molecular dynamics simulations (MDS), we observed changes in the stability of the DDC protein induced by specific nsSNPs. Furthermore, population-based association studies have revealed potential associations between certain DDC nsSNPs and various neurological disorders, including Parkinson's disease and dementia. The in silico approach used in this study offers insightful information about the functional effects of nsSNPs in the DDC gene. These discoveries provide insight into the cellular processes that underlie cognitive disorders. Furthermore, the detection of disease-associated nsSNPs in the DDC gene may facilitate the development of tailored and targeted therapy approaches.Communicated by Ramaswamy H. Sarma.

Comprehensive analysis of non-synonymous missense SNPs of human galactose mutarotase (GALM) gene: an integrated computational approach

Missense Non-synonymous single nucleotide polymorphisms (nsSNPs) of Galactose Mutarotase (GALM) are associated with the Novel type of Galactosemia (Galactosemia type 4) together with symptoms such as high blood galactose levels and eye cataracts. The objective of the present study was to identify deleterious nsSNPs of GALM recorded on the dbSNP database through comprehensive insilico analysis. Among the 319 missense nsSNPs reported, various insilco tools predicted R78S, R82G, A163E, P210S, Y281C, E307G and F339C as the most deleterious mutations. Structural analysis, PTM analysis and molecular dynamics simulations (MDS) were carried out to understand the effect of these mutations on the structural and physicochemical properties of the GALM protein. The residues R82G and E307G were found to be part of the binding site that resulted in decreased surface accessibility. Replacing the charged wild-type residue with a neutral mutant type affected its substrate binding. All 7 mutations were found to increase the rigidity of the protein structure, which is unfavorable during ligand binding. The mutation F339E made the protein structure more rigid than all the other mutations. Y281 is a phosphorylated site, and therefore, less significant structural changes were observed when compared to other mutations; however, it may have significant differences in the usual functioning of the protein. In summary, the structural and functional analysis of missense SNPs of GALM is important to reduce the number of potential mutations to be evaluated in vitro to understand the association with some genetic diseases.Communicated by Ramaswamy H. Sarma.

Non-digestible oligosaccharides-based prebiotics to ameliorate obesity: Overview of experimental evidence and future perspectives

The diverse populations reportedly suffer from obesity on a global scale, and inconclusive evidence has indicated that both environmental and genetic factors are associated with obesity development. Therefore, a need exists to examine potential therapeutic or prophylactic molecules for obesity treatment. Prebiotics with non-digestible oligosaccharides (NDOs) have the potential to treat obesity. A limited number of prebiotic NDOs have demonstrated their ability as a convincing therapeutic solution to encounter obesity through various mechanisms, viz., stimulating beneficial microorganisms, reducing the population of pathogenic microorganisms, and also improving lipid metabolism and glucose homeostasis. NDOs include pectic-oligosaccharides, fructo-oligosaccharides, xylo-oligosaccharides, isomalto-oligosaccharides, manno-oligosaccharides and other oligosaccharides which significantly influence the overall human health by different mechanisms. This review provides the treatment of obesity benefits by incorporating these prebiotic NDOs, according to established scientific research, which shows their good effects extend beyond the colon.

Immunoinformatic exploration of a multi-epitope-based peptide vaccine candidate targeting emerging variants of SARS-CoV-2

Many countries around the world are facing severe challenges due to the recently emerging variants of SARS-CoV-2. Over the last few months, scientists have been developing treatments, drugs, and vaccines to subdue the virus and prevent its transmission. In this context, a peptide-based vaccine construct containing pathogenic proteins of the virus known to elicit an immune response was constructed. An analysis of the spike protein-based epitopes allowed us to design an "epitope-based subunit vaccine" against coronavirus using the approaches of "reverse vaccinology" and "immunoinformatics." Computational experimentation and a systematic, comprehensive protocol were followed with an aim to develop and design a multi-epitope-based peptide (MEBP) vaccine candidate. Our study attempted to predict an MEBP vaccine by introducing mutations of SARS-CoV-2 (Delta, Lambda, Iota, Omicron, and Kappa) in Spike glycoprotein and predicting dual-purpose epitopes (B-cell and T-cell). This was followed by screening the selected epitopes based on antigenicity, allergenicity, and population coverage and constructing them into a vaccine by using linkers and adjuvants. The vaccine construct was analyzed for its physicochemical properties and secondary structure prediction, and a 3D structure was built, refined, and validated. Furthermore, the peptide-protein interaction of the vaccine construct with Toll-like receptor (TLR) molecules was performed. Immune profiling was performed to check the immune response. Codon optimization of the vaccine construct was performed to obtain the GC content before cloning it into the E. coli genome, facilitating its progression it into a vector. Finally, an in-silico simulation of the vaccine-protein complex was performed to comprehend its stability and conformational behavior.

Structure-based virtual screening of natural compounds against wild and mutant (R1155K, A1156T and D1168A) NS3-4A protease of Hepatitis C virus

NS3-4A, a serine protease, is a primary target for drug development against Hepatitis C Virus (HCV). However, the effectiveness of potent next-generation protease inhibitors is limited by the emergence of mutations and resulting drug resistance. To address this, in this study a structure-based drug design approach is employed to screen a large library of 7320 natural compounds against both wild-type and mutant variants of NS3-4A protease. Telaprevir, a widely used protease inhibitor, was recruited as the control drug. The top 10 compounds with favorable binding affinities underwent drug-likeness evaluation. Based on ADMET studies, complexes of NP_024762 and NP_006776 were selected for molecular dynamic simulations. Principal component analysis (PCA) was employed to explore the conformational space and protein dynamics of the protein-ligand complex using a Free Energy Landscape (FEL) approach. The cosine values obtained from FEL analysis ranged from 0 to 1, and eigenvectors with cosine values below 0.2 were chosen for further analysis. To forecast binding free energies and evaluate energy contributions per residue, the MM-PBSA method was employed. The results highlighted the crucial role of amino acids in the catalytic domain for the binding of the protease with phytochemicals. Stable associations between the top compounds and the target protease were confirmed by the formation of hydrogen bonds in the binding pocket involving residues: His1057, Gly1137, Ser1139, and Ala1157. These findings suggest the potential of these compounds for further validation through biological evaluation.Communicated by Ramaswamy H. Sarma.

Computational screening of pathogenic missense nsSNPs in heme oxygenase 1 (HMOX1) gene and their structural and functional consequences

Heme Oxygenase 1 (HMOX1) is a cytoprotective enzyme, exhibiting the highest activity in the spleen, catalyzing the heme ring breakdown into products of biological significance- biliverdin, CO, and Fe²⁺. In vascular cells, HMOX1 possesses strong anti-apoptotic, antioxidant, anti-proliferative, anti-inflammatory, and immunomodulatory actions. The majority of these activities are crucial for the prevention of atherogenesis. Single amino acid substitutions in proteins generated by missense non-synonymous single nucleotide polymorphism (nsSNPs) in the protein-encoding regions of genes are potent enough to cause significant medical challenges due to the alteration of protein structure and function. The current study aimed at characterizing and analyzing high-risk nsSNPs associated with the human HMOX1 gene. Preliminary screening of the total available 288 missense SNPs was performed through the lens of deleteriousness and stability prediction tools. Finally, a total of seven nsSNPs (Y58D, A131T, Y134H, F166S, F167S, R183S and M186V) were found to be most deleterious by all tools that are present at highly conserved positions. Molecular dynamics simulations (MDS) analysis explained the mutational effects on the dynamic action of the wild-type and mutant proteins. In a nutshell, R183S (rs749644285) was identified as a highly detrimental mutation that could significantly render the enzymatic activity of HMOX1. The finding of this computational analysis might help subject the experimental confirmatory analysis to characterize the role of nsSNPs in HMOX1.Communicated by Ramaswamy H. Sarma.

Exploration of interaction interface of TRKβ/BDNF through fingerprint analysis to disinter potential agonists

Tyrosine Kinase beta (TRKβ), is a type I membrane receptor which plays a major role in various signalling pathways. TRKβ was found to be upregulated in various cancers and contrastingly downregulated in various neurodegenerative disorders. Hitherto, contemporary drug research is oriented towards discovery of TRKβ inhibitors, thus neglecting the development of TRKβ agonists. This research is aimed at identifying FDA approved drugs exhibiting repurposable potential as TRKβ agonists by mapping them with fingerprints of the BDNF/TRKβ interaction interface. Initially, crucial interacting residues were retrieved and a receptor grid was generated around it. TRKβ agonists were retrieved from literature search and a drug library was created for each agonist based on its structural and side effect similarities. Subsequently, molecular docking and dynamics were performed for each library to identify the drugs possessing affinity towards the binding pocket of TRKβ. The study revealed molecular interactions of Perospirone, Droperidol, Urapidil, and Clobenzorex with the crucial amino acids lining the active binding pocket of TRKβ. Subsequent network pharmacological analysis of the above drugs revealed their interactions with key proteins involved in neurotransmitter signalling pathways. Clobenzorex displayed high stability in dynamics simulation and therefore this drug is recommended for further experimental evaluations to attain better mechanistic insights and predict its implications in correcting neuropathological aberrations. This study's focus on the interaction interface between TRKβ and BDNF, combined with the utilization of fingerprint analysis for drug repurposing, contributes to our understanding of neurotrophic signalling and holds potential for identifying new therapeutic options for neurological disorders.

Comprehensive bioinformatics analysis of structural and functional consequences of deleterious missense mutations in the human QDPR gene

Quinonoid dihydropteridine reductase (QDPR) is an enzyme that regulates tetrahydrobiopterin (BH4), a cofactor for enzymes involved in neurotransmitter synthesis and blood pressure regulation. Reduced QDPR activity can cause dihydrobiopterin (BH2) accumulation and BH4 depletion, leading to impaired neurotransmitter synthesis, oxidative stress, and increased risk of Parkinson's disease. A total of 10,236 SNPs were identified in the QDPR gene, with 217 being missense SNPs. Over 18 different sequence-based and structure-based tools were employed to assess the protein's biological activity, with several computational tools identifying deleterious SNPs. Additionally, the article provides detailed information about the QDPR gene and protein structure and conservation analysis. The results showed that 10 mutations were harmful and linked to brain and central nervous system disorders, and were predicted to be oncogenic by Dr. Cancer and CScape. Following conservation analysis, the HOPE server was used to analyse the effect of six selected mutations (L14P, V15G, G23S, V54G, M107K, G151S) on the protein structure. Overall, the study provides insights into the biological and functional impact of nsSNPs on QDPR activity and the potential induced pathogenicity and oncogenicity. In the future, research can be conducted to systematically evaluate QDPR gene variation through clinical studies, investigate mutation prevalence across different geographical regions, and validate computational results with conclusive experiments.Communicated by Ramaswamy H. Sarma.

Ligand-based pharmacophore modeling and QSAR approach to identify potential dengue protease inhibitors

The viral disease dengue is transmitted by the Aedes mosquito and is commonly seen to occur in the tropical and subtropical regions of the world. It is a growing public health concern. To date, other than supportive treatments, there are no specific antiviral treatments to combat the infection. Therefore, finding potential compounds that have antiviral activity against the dengue virus is essential. The NS2B-NS3 dengue protease plays a vital role in the replication and viral assembly. If the functioning of this protease were to be obstructed then viral replication would be halted. As a result, this NS2B-NS3 proves to be a promising target in the process of anti-viral drug design. Through this study, we aim to provide suggestions for compounds that may serve as potent inhibitors of the dengue NS2B-NS3 protein. Here, a ligand-based pharmacophore model was generated and the ZINC database was screened through ZINCPharmer to identify molecules with similar features. 2D QSAR model was developed and validated using reported 4-Benzyloxy Phenyl Glycine derivatives and was utilized to predict the IC50 values of unknown compounds. Further, the study is extended to molecular docking to investigate interactions at the active pocket of the target protein. ZINC36596404 and ZINC22973642 showed a predicted pIC50 of 6.477 and 7.872, respectively. They also showed excellent binding with NS3 protease as is evident from their binding energy of -8.3and -8.1 kcal/mol, respectively. ADMET predictionsofcompounds have shown high drug-likeness. Finally, the molecular dynamic simulations integrated with MM-PBSA binding energy calculations confirmedboth identified ZINC compounds as potential hit moleculeswith good stability.

Articulating target-mining techniques to disinter Alzheimer's specific targets for drug repurposing

BACKGROUND AND OBJECTIVES

Alzheimer's Disease (AD), an extremely progressive neurodegenerative disorder is an amalgamation of numerous intricate pathological networks. This century old disease is still an unmet medical condition owing to the modest efficacy of existing therapeutic agents in antagonizing the multi-targeted pathological pathways underlying AD. Given the paucity in AD specific drugs, fabricating comprehensive research strategies to envision disease specific targets to channelize and expedite drug discovery are mandated. However, the dwindling approval rates and stringent regulatory constraints concerning the approval of a new chemical entity is daunting the pharmaceutical industries from effectuating de novo research. To bridge the existing gaps in AD drug research, a promising contemporary way out could be drug repurposing. This drug repurposing investigation is intended to envisage AD specific targets and create drug libraries pertinent to the shortlisted targets via a series of avant-garde bioinformatics and computational strategies.

METHODS

Transcriptomic analysis of three AD specific datasets viz., GSE122063, GSE15222 and GSE5281 revealed significant Differentially Expressed Genes (DEGs) and subsequent Protein-Protein Interactions (PPI) network analysis captured crucial AD targets. Later, homology model was constructed through I-TASSER for a shortlisted target protein which lacked X-ray crystallographic structure and the built protein model was validated by molecular dynamic simulations. Further, drug library was created for the shortlisted target based on structural and side effect similarity with respective standard drugs. Finally, molecular docking, binding energy calculations and molecular dynamics studies were carried out to unravel the interactions exhibited by drugs from the created library with amino acids in active binding pocket of RGS4.

RESULTS

SST and RGS4 were shortlisted as potentially significant AD specific targets, however, the less explored target RGS4 was considered for further sequential analysis. Homology model constructed for RGS4 displayed best quality when validated through Ramachandran plot and ERRAT plot. Subsequent docking and molecular dynamics studies showcased substantial affinity demonstrated by three drugs viz., Ziprasidone, Melfoquine and Metaxalone from the created drug libraries, towards RGS4.

CONCLUSION

This virtual analysis forecasted the repurposable potential of Ziprasidone, Melfoquine and Metaxalone against AD based on their affinity towards RGS4, a key AD-specific target.

Computational screening of natural compounds from Salvia plebeia R. Br. for inhibition of SARS-CoV-2 main protease

The novel Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) has emerged to be the reason behind the COVID-19 pandemic. It was discovered in Wuhan, China and then began spreading around the world, impacting the health of millions. Efforts for treatment have been hampered as there are no antiviral drugs that are effective against this virus. In the present study, we have explored the phytochemical constituents of Salvia plebeia R. Br., in terms of its binding affinity by targeting COVID-19 main protease (M^pro) using computational analysis. Molecular docking analysis was performed using PyRx software. The ADMET and drug-likeness properties of the top 10 compounds showing binding affinity greater than or equal to - 8.0 kcal/mol were analysed using pkCSM and DruLiTo, respectively. Based on the docking studies, it was confirmed that Rutin and Plebeiosides B were the most potent inhibitors of the main protease of SARS-CoV-2 with the best binding affinities of - 9.1 kcal/mol and - 8.9 kcal/mol, respectively. Further, the two compounds were analysed by studying their biological activity using the PASS webserver. Molecular dynamics simulation analysis was performed for the selected protein-ligand complexes to confirm their stability at 300 ns. MM-PBSA provided the basis for analyzing the affinity of the phytochemicals towards M^pro by calculating the binding energy, and secondary structure analysis indicated the stability of protease structure when it is bound to Rutin and Plebeiosides B. Altogether, the study identifies Rutin and Plebeiosides B to be potent M^pro inhibitors of SARS-CoV-2.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s42535-021-00304-z.

Application of comprehensive bioinformatics approaches to reconnoiter crucial genes and pathways underpinning hepatocellular carcinoma: a drug repurposing endeavor

Hepatocellular carcinoma (HCC) is the fifth most common neoplasm in the world. Chronic inflammation of liver and associated wound healing processes collectively contribute to the development of cirrhosis which further progresses to dysplastic nodule and then to HCC. Etiological mediators and ongoing manipulations at cellular level in HCC are well established; however, key protein interactions and genetic alterations involved in stepwise hepatocarcinogenic pathways are seldom explored. This study aims to unravel novel targets of HCC and repurpose the FDA-approved drugs against the same. Genetic data pertinent to different stages of HCC were retrieved from GSE6764 dataset and analyzed via GEO2R. Subsequently, protein-protein interaction network analysis of differentially expressed genes was performed to identify the hub genes with significant interaction. Hub genes displaying higher interactions were considered as potential HCC targets and were validated thorough UALCAN and GEPIA databases. These targets were screened against FDA-approved drugs through molecular docking and dynamics simulation studies to capture the drugs with potential activity against HCC. Finally, cytotoxicity of the shortlisted drug was confirmed in vitro by MTT assay. CDC20 was identified as potential druggable target. Docking, binding energy calculations, and dynamic studies revealed significant interaction exhibited by Labetalol with CDC20. Further, in MTT assay, Labetalol demonstrated an IC₅₀ of 200.29 µg/ml in inhibiting the cell growth of HepG2 cell line. In conclusion, this study discloses a series of key genetic underpinnings of HCC and recommends the pertinence of labetalol as a potential repurposable drug against HCC.

Screening of natural compounds from Cyperus rotundus Linn against SARS-CoV-2 main protease (Mpro): An integrated computational approach

Coronavirus disease 2019 (COVID-19) is a viral respiratory disease that has been spreading across the globe. The World Health Organization (WHO) declared it as a public health emergency. The treatment of COVID-19 has been hampered due to the lack of effective therapeutic efforts. Main Protease (M^pro) is a key enzyme in the viral replication cycle and its non-specificity to human protease makes it a potential drug target. Cyperus rotundus Linn, which belongs to the Cyperaceae family, is a traditional herbal medicine that has been widely studied for its antiviral properties. In this study, a computational approach was used to screen natural compounds from C. rotundus Linn using BIOVIA Discovery Suite and novel potential molecules against M^pro of SARS-CoV-2 were predicted. Molecular docking was performed using LibDock protocol and selected ligands were further subjected to docking analysis by CDOCKER. The docking scores of the selected ligands were compared with standard antiretroviral drugs such as lopinavir and ritonavir to assess their binding potentials. Interaction pharmacophore analysis was then performed for the compounds exhibiting good binding scores to evaluate their protein–ligand interactions. The selected protein–ligand complexes were subjected to molecular dynamics simulation for 50 ns. Results of binding free energy analysis revealed that two compounds—β-amyrin and stigmasta-5,22-dien-3-ol—exhibited the best binding interactions and stability. Finally, absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies were performed to understand the pharmacokinetic properties and safety profile of the compounds. The overall results indicate that the phytochemicals from Cyperus rotundus Linn, namely β-amyrin and stigmasta-5,22-dien-3-ol, can be screened as potential inhibitors of SARS-CoV-2 M^pro.

Structure-based design approach of potential BCL-2 inhibitors for cancer chemotherapy

B-cell lymphoma 2 (BCL-2) family is one of the chief regulators of cellular apoptosis. The intricate interactions between pro-apoptotic and anti-apoptotic genes of the BCL-2 family dictate the apoptotic balance of the cell. An overexpression of the anti-apoptotic members of BCL-2 is indicative of cell death evasion and cancer metastasis. Among the four BCL-2 homology domains, the BH3 domain plays a key role in the suppression of BCL-2 expression. Therefore, BH3-mimetic drugs are currently investigated for their suitability as BCL-2 inhibitors. In the present study, we followed a structure-based pharmacophore modelling approach to identify BH3-mimetic small molecules, to formulate a more precise and targeted cancer treatment regimen. To identify proteins with similar binding features, a structure-based pharmacophore model was generated based on the structure of Bcl-2 complexed with Venetoclax (PDB-ID:6O0K). Compounds with good fitness score and pharmacophore features, screened from the ZINC database, were subjected to (i) molecular docking studies, (ii) molecular mechanics-generalized Born surface area (MM-GBSA), and (iii) absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction. From the analysis, two molecules were identified: ZINC68728276 and ZINC14166367, with docking scores of -7.323 and -8.649 kcal/mol and free binding energies (MM-GBSA) of -72.913 and -72.291 kcal/mol, respectively. The structural parameters and binding affinity of these complexes were validated through molecular dynamics simulation and molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) free energy calculations and compared with Venetoclax. The results indicated stability and good binding affinity of both the compounds. The study identified ZINC68728276 and ZINC14166367 as in silico potential Bcl-2 inhibitors, which can be further considered for in vitro studies.

Glycerol mediated solution combustion synthesis of nano magnesia and its application in the adsorptive removal of anionic dyes

This study reports solution combustion synthesis of magnesia nanoparticles (nMgO) using magnesium nitrate as oxidiser and glycerol as fuel. Size, morphology, crystal structure and surface properties of synthesised nMgO were analysed by PXRD, SEM, TEM, FTIR and Point of Zero Charge. The XRD pattern of nMgO confirmed prepared samples were single cubic-phase without any impurities. TEM analysis proved nMgO was in nano regime with an average particle diameter of 20–40 nm. FTIR spectra show the presence of characteristic peaks of nMgO and support the XRD results. The prepared nMgO was employed as an adsorbent for the removal of two anionic dyes viz. Indigo Carmine (IC) and Orange G (OG). Furthermore, various adsorption isotherms and kinetic models were performed to understand the kinetics and mechanism of the adsorption process. Experimental results demonstrated that the adsorption equilibrium data fit well to Sips isotherm (R2 > 0.98) and the saturated adsorption capacities of nMgO were found to be 262 mg g−1 for IC and 126 mg g−1 for OG. Adsorption kinetics analysis revealed that the adsorption followed pseudo-first-order model, with both film and pore diffusion governing the rate of adsorption. Excellent adsorption capacity combined with efficient regeneration proved the potential of the prepared nMgO as an adsorbent for the removal of harmful dyes from industrial effluent.

In silico approach towards the identification of potential inhibitors from Curcuma amada Roxb against H. pylori: ADMET screening and molecular docking studies

Introduction: The present study attempts to identify potential targets of H. pylori for novel inhibitors from therapeutic herb, mango ginger (Curcuma amada Roxb.).

Methods: Crystal structure of all the selected drug targets obtained from Protein Data Bank (PDB) were subjected to molecular docking against a total of 130 compounds (found to have biological activity against H. pylori ) were retrieved from public databases. Compounds with good binding affinity were selected for Prime MM-GBSA rescoring and molecular dynamics (MD) simulation. Final list of compounds were taken for ADMET predictions.

Results: Based on binding affinity denoted by glide score and ligand efficiency, mango ginger compounds were found selective to shikimate kinase and type II dehydroquinase through hydrogen bonding and salt bridge interactions. Stability of the interactions and free energy calculations by Prime MM-GBSA results confirmed the affinity of mango ginger compounds towards both shikimate kinase and type II dehydroquinase. From the above results, 15 compounds were calculated for ADMET parameters, Lipinski’s rule of five, and the results were found promising without any limitations. MD simulations identified gentisic acid as hit compound for shikimate kinase of H. pylori.

Conclusion: Current study could identify the in silico potential of mango ginger compounds against shikimate kinase and type II dehydroquinase targets for H. pylori infections and are suitable for in vitro and in vivo evaluation.