Comparative Analysis of Machine Learning Algorithms Evaluating the Single Nucleotide Polymorphisms of Metabolizing Enzymes with Clinical Outcomes Following Intravenous Paracetamol in Preterm Neonates with Patent Ductus Arteriosus.

AIMS: Pharmacogenomics has been identified to play a crucial role in determining drug response. The present study aimed to identify significant genetic predictor variables influencing the therapeutic effect of paracetamol for new indications in preterm neonates. BACKGROUND: Paracetamol has recently been preferred as a first-line drug for managing Patent Ductus Arteriosus (PDA) in preterm neonates. Single Nucleotide Polymorphisms (SNPs) in CYP1A2, CYP2A6, CYP2D6, CYP2E1, and CYP3A4 have been observed to influence the therapeutic concentrations of paracetamol. OBJECTIVES: The purpose of this study was to evaluate various Machine Learning Algorithms (MLAs) and bioinformatics tools for identifying the key genotype predictor of therapeutic outcomes following paracetamol administration in neonates with PDA. METHODS: Preterm neonates with hemodynamically significant PDA were recruited in this prospective, observational study. The following SNPs were evaluated: CYP2E1*5B, CYP2E1*2, CYP3A4*1B, CYP3A4*2, CYP3A4*3, CYP3A5*3, CYP3A5*7, CYP3A5*11, CYP1A2*1C, CYP1A2*1K, CYP1A2*3, CYP1A2*4, CYP1A2*6, and CYP2D6*10. Amongst the MLAs, Artificial Neural Network (ANN), C5.0 algorithm, Classification and Regression Tree analysis (CART), discriminant analysis, and logistic regression were evaluated for successful closure of PDA. Generalized linear regression, ANN, CART, and linear regression were used to evaluate maximum serum acetaminophen concentrations. A two-step cluster analysis was carried out for both outcomes. Area Under the Curve (AUC) and Relative Error (RE) were used as the accuracy estimates. Stability analysis was carried out using in silico tools, and Molecular Docking Studies (MDS) were carried out for the above-mentioned enzymes. RESULTS: Two-step cluster analyses have revealed CYP2D6*10 and CYP1A2*1C to be the key predictors of the successful closure of PDA and the maximum serum paracetamol concentrations in neonates. The ANN was observed with the maximum accuracy (AUC = 0.53) for predicting the successful closure of PDA with CYP2D6*10 as the most important predictor. Similarly, ANN was observed with the least RE (1.08) in predicting maximum serum paracetamol concentrations, with CYP2D6*10 as the most important predictor. Further MDS confirmed the conformational changes for P34A and P34S compared to the wildtype structure of CYP2D6 protein for stability, flexibility, compactness, hydrogen bond analysis, and the binding affinity when interacting with paracetamol, respectively. The alterations in enzyme activity of the mutant CYP2D6 were computed from the molecular simulation results. CONCLUSION: We have identified CYP2D6*10 and CYP1A2*1C polymorphisms to significantly predict the therapeutic outcomes following the administration of paracetamol in preterm neonates with PDA. Prospective studies are required for confirmation of the findings in the vulnerable population.

Evaluation of Supervised Machine Learning Algorithms and Computational Structural Validation of Single Nucleotide Polymorphisms Related to Acute Liver Injury with Paracetamol.

AIMS: To identify single nucleotide polymorphisms (SNPs) of paracetamol-metabolizing enzymes that can predict acute liver injury. BACKGROUND: Paracetamol is a commonly administered analgesic/antipyretic in critically ill and chronic renal failure patients and several SNPs influence the therapeutic and toxic effects. OBJECTIVE: To evaluate the role of machine learning algorithms (MLAs) and bioinformatics tools to delineate the predictor SNPs as well as to understand their molecular dynamics. METHODS: A cross-sectional study was undertaken by recruiting critically ill patients with chronic renal failure and administering intravenous paracetamol as a standard of care. Serum concentrations of paracetamol and the principal metabolites were estimated. Following SNPs were evaluated: CYP2E1*2, CYP2E1_-1295G>C, CYP2D6*10, CYP3A4*1B, CYP3A4*2, CYP1A2*1K, CYP1A2*6, CYP3A4*3, and CYP3A5*7. MLAs were used to identify the predictor genetic variable for acute liver failure. Bioinformatics tools such as Predict SNP2 and molecular docking (MD) were undertaken to evaluate the impact of the above SNPs with binding affinity to paracetamol. RESULTS: CYP2E1*2 and CYP1A2*1C genotypes were identified by MLAs to significantly predict hepatotoxicity. The predictSNP2 revealed that CYP1A2*3 was highly deleterious in all the tools. MD revealed binding energy of -5.5 Kcal/mol, -6.9 Kcal/mol, and -6.8 Kcal/mol for CYP1A2, CYP1A2*3, and CYP1A2*6 against paracetamol. MD simulations revealed that CYP1A2*3 and CYP1A2*6 missense variants in CYP1A2 affect the binding ability with paracetamol. In-silico techniques found that CYP1A2*2 and CYP1A2*6 are highly harmful. MD simulations revealed CYP3A4*2 (A>G) had decreased binding energy with paracetamol than CYP3A4, and CYP3A4*2(A>T) and CYP3A4*3 both have greater binding energy with paracetamol. CONCLUSION: Polymorphisms in CYP2E1, CYP1A2, CYP3A4, and CYP3A5 significantly influence paracetamol's clinical outcomes or binding affinity. Robust clinical studies are needed to identify these polymorphisms' clinical impact on the pharmacokinetics or pharmacodynamics of paracetamol.

Elucidating the mutational impact in causing Niemann-Pick disease type C: an in silico approach.

Niemann-Pick disease type C is a rare autosomal recessive of lysosomal storage disorder characterized by impaired intracellular lipid transport and has a tendency to accumulate the fatty acids and glycosphingolipids in a variety of neurovisceral tissues. This work includes computational tools to deciphere the mutational effect in NPC protein. The study initiated with the collection of 471 missense mutations from various databases, which were then analyzed using computational tools. The mutations (G549V, F703S, Q775P and L1244P) were said to be disease associated, altering the biophysical properties, in highly conserved regions and reduces the stability using several in silico methods and were subjected to molecular docking analysis. To analyze the ligand (Itraconazole: a small molecule of antifungal drug class, which is known to inhibit cholesterol export from lysosomes) activity Molecular docking study was performed for all the complex proteins. The average binding affinity was taken and found to be -10.76 kcal/mol (native) and -11.06 kcal/mol (Q775P was located in transmembrane region IV which impacts the sterol-sensing domain of the NPC1 protein and associated with a severe infantile neurological form). Finally, molecular dynamic simulation was performed in duplicate and trajectories were built for the backbone of the RMSD, RMSF, the number of intramolecular hydrogen bonds, the radius of gyration and the SSE percent for both the complex proteins. This work contributes to understand the effectiveness and may provide an insight on the stability of the drug with the complex variant structures.Communicated by Ramaswamy H. Sarma.

Designing, Synthesis, and Anti-Breast Cancer Activity of a Series of New Quinazolin-4(1H)-one Derivatives.

The inhibition of phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) protein could be a promising treatment for breast cancer. In this regard, docking studies were accomplished on various functionalized organic molecules. Among them, several derivatives of quinazolin-4(1H)-one exhibited anti-breast cancer activity and satisfied the drug likeliness properties. Further, the in vitro inhibitory studies by a series of 2-(2-phenoxyquinolin-3-yl)-2,3-dihydroquinazolin-4(1H)-one molecules showed strong anti-cancer activity than the currently available drug, wortmannin. The MTT cytotoxicity assay was used to predict the anti-proliferative activity of these drugs against MCF-7 cancer cells by inhibiting the PIK3CA protein. The dose-dependent analysis showed a striking decrease in cancer cell viability at 24 h with inhibitory concentrations (IC50 ) of 3b, 3c, 3d, 3f and 3m are 15±1, 17±1, 8±1, 10±1 and 60±1 (nanomoles), respectively. This is the first report in the literature on the inhibition of PIK3CA protein by quinazolinone derivatives that can be used in the treatment of cancer. Quinazolinone analogs have the potential to be safe and economically feasible scaffolds if they are produced using a chemical technique that is both straightforward and amenable to modification. From the cancer research perspective, this study can eventually offer better care for cancer patients.

Investigation of nonsynonymous mutations in the spike protein of SARS-CoV-2 and its interaction with the ACE2 receptor by molecular docking and MM/GBSA approach.

COVID-19 is an infectious and pathogenic viral disease caused by SARS-CoV-2 that leads to septic shock, coagulation dysfunction, and acute respiratory distress syndrome. The spreading rate of SARS-CoV-2 is higher than MERS-CoV and SARS-CoV. The receptor-binding domain (RBD) of the Spike-protein (S-protein) interacts with the human cells through the host angiotensin-converting enzyme 2 (ACE2) receptor. However, the molecular mechanism of pathological mutations of S-protein is still unclear. In this perspective, we investigated the impact of mutations in the S-protein and their interaction with the ACE2 receptor for SAR-CoV-2 viral infection. We examined the stability of pathological nonsynonymous mutations in the S-protein, and the binding behavior of the ACE2 receptor with the S-protein upon nonsynonymous mutations using the molecular docking and MM_GBSA approaches. Using the extensive bioinformatics pipeline, we screened the destabilizing (L8V, L8W, L18F, Y145H, M153T, F157S, G476S, L611F, A879S, C1247F, and C1254F) and stabilizing (H49Y, S50L, N501Y, D614G, A845V, and P1143L) nonsynonymous mutations in the S-protein. The docking and binding free energy (ddG) scores revealed that the stabilizing nonsynonymous mutations show increased interaction between the S-protein and the ACE2 receptor compared to native and destabilizing S-proteins and that they may have been responsible for the virulent high level. Further, the molecular dynamics simulation (MDS) approach reveals the structural transition of mutants (N501Y and D614G) S-protein. These insights might help researchers to understand the pathological mechanisms of the S-protein and provide clues regarding mutations in viral infection and disease propagation. Further, it helps researchers to develop an efficient treatment approach against this SARS-CoV-2 pandemic.

Analysis and effective separation of toxic pollutants from water resources using MBBR: Pathway prediction using alkaliphilic P. mendocina.

Azo dyes are highly toxic, which acts as a notable mutagen and carcinogen. This has a significant effect on human health, plants, animals, aquatic and terrestrial environments. Thus, the degradation of the azo dyes is exclusively studied using the conventional methods of which biodegradation is an eco-friendly approach. Hence, the present study is focused on the elucidation of reactive mixed azo dye degradation pathway using MBBR and laccase enzyme produced by an alkaliphilic bacterium P. mendocina. Synthetic wastewater treatment performed using MBBR was very effective which reduced the COD and BOD to 90 mg/L and 460 mg/L. The potential degrader P. mendocina was isolated and laccase enzyme was screened. Finally, the degradation pathway was elucidated. The in silico toxicity analysis predicted Reactive Red and Reactive Brown as developmental toxicants during Reactive Black as Developmental non-toxicant. Docking studies were performed to understand interaction of laccase with compounds evolved from dyes.

Effect of shape and anthocyanin capping on antibacterial activity of CuI particles.

The recent upsurge of antibiotic-resistant infections has posed to be a serious health concern worldwide. In the present paper, the effect of shape & capping agent on the antibacterial activity (on Skin and Urinary Tract Infection (UTI) causing bacteria) of copper iodide (CuI) particles was probed. CuI synthesized without a capping agent was leaf-like, and that with one was prismatic in shape. XRD of the synthesized CuI confirmed their high crystalline nature and purity. The average crystallite sizes of capped and uncapped CuI were calculated to be 91.10 nm and 89.01 nm respectively from X-Ray powder diffraction data. The average particle size of capped CuI was found to be 492.7 nm and that of uncapped CuI was found to be 2.96 µm using HR-SEM analysis. The crystals obtained were further characterized using EDAX, FTIR spectroscopy and UV-Visible spectroscopy. Antibacterial activity of prismatic CuI capped with the flower extract of Hibiscus rosa-sinensis showed better activity than that of uncapped CuI. AFM analysis was carried out to confirm the proposed mechanism for antibacterial activity through the morphological changes on the bacterial cell wall in the presence of capped CuI. Molecular docking studies were performed to reaffirm the enhanced antibacterial activity of prismatic CuI further. The present study demonstrates the superior antibacterial propensity of prismatic CuI, consequently making it a potent antibacterial agent.

Comparison of potential inhibitors and targeting fat mass and obesity-associated protein causing diabesity through docking and molecular dynamics strategies.

Genome-wide association studies (GWAS) have identified an association between polymorphisms in the FTO gene and obesity. The FTO: rs9939609, an intronic variant, is considered a risk allele for developing diabesity in homozygous and heterozygous forms. This study aimed to investigate the molecular structure of the available inhibitors specific to the FTO mutations along with the rs9939609 variant. We identified the best-suited inhibitor molecules for each mutant type containing the rs9939609 risk allele. Missense mutations unique to obesity and containing the risk allele of rs9939609 were retrieved from dbSNP for this study. Further stability testing for the mutations were carried out using DynaMut and iStable tools. Three mutations (G187A, M223V, and I492V) were highly destabilizing the FTO structure. These three mutants and native FTO were docked with each of the nine-inhibitor molecules collected from literature studies with the help of PyRx and AutoDock. Further structural behavior of the mutants and native FTO were identified with molecular dynamics simulations and MM-PBSA analyses, along with the 19complex inhibitor compound. We found the compound 19complex exhibited better binding interactions and is the top candidate inhibitor for the M223V and I492V mutants. This study provided insights into the structural changes caused due to mutations in FTO, and the binding mechanism of the inhibitor molecules. It could aid in developing antiobesity drugs for treating patients with mutations and risk alleles predisposing to obesity.

Inhibition of MMP2-PEX by a novel ester of dihydroxy cinnamic and linoleic acid from the seagrass Cymodocea serrulata.

Matrix metalloproteinases (MMPs) are pivotal for cancer cell migration and metastasis which are generally over-expressed in such cell types. Many drugs targeting MMPs do so by binding to the conserved catalytic domains and thus exhibit poor selectivity due to domain-similarities with other proteases. We report herein the binding of a novel compound [3-(E-3,4-dihydroxycinnamaoyloxyl)-2-hydroxypropyl 9Z, 12Z-octadeca-9, 12-dienoate; Mol. wt: 516.67 Da], (C1), isolated from a seagrass, Cymodocea serrulata to the unconserved hemopexin-like (PEX) domain of MMP2 (- 9.258 kcal/mol). MD simulations for 25 ns, suggest stable ligand-target binding. In addition, C1 killed an ovarian cancer cell line, PA1 at IC50: 5.8 µM (lesser than Doxorubicin: 8.6 µM) and formed micronuclei, apoptotic bodies and nucleoplasmic bridges whilst causing DNA laddering, S and G2/M phase dual arrests and MMP disturbance, suggesting intrinsic apoptosis. The molecule increased mRNA transcripts of BAX and BAD and down-regulated cell survival genes, Bcl-xL, Bcl-2, MMP2 and MMP9. The chemical and structural details of C1 were deduced through FT-IR, GC-MS, ESI-MS, 1H and 13C NMR [both 1D and 2D] spectra.

Integrated approach in LDPE degradation - An application using Winogradsky column, computational modeling, and pathway prediction.

Plastic pollution in the current scenario requires a sustainable and eco-friendly treatment process. Single-use plastics accumulate more than recyclable plastic wastes. Low-Density Polyethylene (LDPE) is one among the plastic family with inert characteristics. The traditional method, such as landfilling, develops pollution resistant micro-organisms. It is involved in the exploitation of the native microbes to the fullest. The soil of the Kodungaiyur, agriculture site, and Otteri dumpyard were used, which resulted in nearly 22.97 ± 2.7115%, 15.91667 ± 2.73775%, and 10.74 ± 0.502925% of LDPE degradation in 30 days without nutrient supplements. The enrichment of the column by organic nutrients increased the degradation of LDPE. The column enrichment was confirmed by the sulfur oxidizing bacteria (SOB) Escherichia coli and Pseudomonas stutzeri, which produced 195 mg/mL of sulfate ions. The FTIR of the LDPE degradation showed the polymer's oxygenation, while the electron microscopic images revealed cracks. In addition, an attempt was made to fit the experimental time-series data into suitable mathematical models to look at prediction and elementary forecasting. Three mathematical models, namely the customized moving averages model (CMAM), simple liinear regression model (SLRM), and a modified linear regression model (MLRM) with a lag, were able to represent the real experimental data complementarily.

Effective utilisation of influence maximization technique for the identification of significant nodes in breast cancer gene networks.

BACKGROUND: Identifying the most important genes in a cancer gene network is a crucial step in understanding the disease's functional characteristics and finding an effective drug. METHOD: In this study, a popular influence maximization technique was applied on a large breast cancer gene network to identify the most influential genes computationally. The novel approach involved incorporating gene expression data and protein to protein interaction network to create a customized pruned and weighted gene network. This was then readily provided to the influence maximization procedure. The weighted gene network was also processed through a widely accepted framework that identified essential proteins to benchmark the proposed method. RESULTS: The proposed method's results had matched with the majority of the output from the benchmarked framework. The key takeaway from the experiment was that the influential genes identified by the proposed method, which did not match favorably with the widely accepted framework, were found to be very important by previous in-vivo studies on breast cancer. INTERPRETATION & CONCLUSION: The new findings generated from the proposed method give us a favorable reason to infer that influence maximization added a more diversified approach to define and identify important genes and could be incorporated with other popular computational techniques for more relevant results.

Structure-Based Virtual Screening to Identify Novel Potential Compound as an Alternative to Remdesivir to Overcome the RdRp Protein Mutations in SARS-CoV-2.

The number of confirmed COVID-19 cases is rapidly increasing with no direct treatment for the disease. Few repurposed drugs, such as Remdesivir, Chloroquine, Hydroxychloroquine, Lopinavir, and Ritonavir, are being tested against SARS-CoV-2. Remdesivir is the drug of choice for Ebola virus disease and has been authorized for emergency use. This drug acts against SARS-CoV-2 by inhibiting the RNA-dependent-RNA-polymerase (RdRp) of SARS-CoV-2. RdRp of viruses is prone to mutations that confer drug resistance. A recent study by Pachetti et al. in 2020 identified the P323L mutation in the RdRp protein of SARS-CoV-2. In this study, we aimed to determine the potency of lead compounds similar to Remdesivir, which can be used as an alternative when variants of SARS-CoV-2 develop resistance due to RdRp mutations. The initial screening yielded 704 compounds that were 90% similar to the control drug, Remdesivir. On further evaluation through drugability and antiviral inhibition percentage analyses, we shortlisted 32 and seven compounds, respectively. These seven compounds were further analyzed for their molecular interactions, which revealed that all seven compounds interacted with RdRp with higher affinity than Remdesivir under native conditions. However, three compounds failed to interact with the mutant protein with higher affinity than Remdesivir. Dynamic cross-correlation matrix (DCCM) and vector field collective motions analyses were performed to identify the precise movements of docked complexes' residues. Furthermore, the compound SCHEMBL20144212 showed a high affinity for native and mutant proteins and might provide an alternative against SARS-CoV-2 variants that might confer resistance to Remdesivir. Further validations by in vitro and in vivo studies are needed to confirm the efficacy of our lead compounds for their inhibition against SARS-CoV-2.

In Silico Identification of Multi-target Anti-SARS-CoV-2 Peptides from Quinoa Seed Proteins.

Peptides are promising antagonists against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). To expedite drug discovery, a computational approach is widely employed for the initial screening of anti-SARS-CoV-2 candidates. This study aimed to investigate the potential of peptides from quinoa seed proteins as multi-target antagonists against SARS-CoV-2 spike glycoprotein receptor-binding domain, main protease, and papain-like protease. Five quinoa proteins were hydrolyzed in silico by papain and subtilisin. Among the 1465 peptides generated, seven could interact stably with the key binding residues and catalytic residues of the viral targets, mainly via hydrogen bonds and hydrophobic interactions. The seven peptides were comparable or superior to previously reported anti-SARS-CoV-2 peptides based on docking scores. Key residues in the seven peptides contributing to binding to viral targets were determined by computational alanine scanning. The seven peptides were predicted in silico to be non-toxic and non-allergenic. The peptides ranged between 546.66 and 3974.87 g/mol in molecular mass, besides exhibiting basic and cationic properties (isoelectric points: 8.26-12.10; net charges: 0.1-4.0). Among the seven peptides, VEDKGMMHQQRMMEKAMNIPRMCGTMQRKCRMS was found to bind the largest number of key residues on the targets. In conclusion, seven putative non-toxic, non-allergenic, multi-target anti-SARS-CoV-2 peptides were identified from quinoa seed proteins. The in vitro and in vivo efficacies of the seven peptides against SARS-CoV-2 deserve attention in future bench-top testing. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10989-021-10214-y.

A review of novel coronavirus disease (COVID-19): based on genomic structure, phylogeny, current shreds of evidence, candidate vaccines, and drug repurposing.

Coronavirus disease (COVID-19) pandemic is instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of March 13, 2021, more than 118.9 million cases were infected with COVID-19 worldwide. SARS-CoV-2 is a positive-sense single-stranded RNA beta-CoV. Most COVID-19 infected individuals recover within 1-3 weeks. Nevertheless, approximately 5% of patients develop acute respiratory distress syndrome and other systemic complications, leading to death. Structural genetic analyses of SARS-CoV-2 have shown genomic resemblances but a low evolutionary correlation to SARS-CoV-1 responsible for the 2002-2004 outbreak. The S glycoprotein is critical for cell adhesion and the entrance of the virus into the host. The process of cell entry uses the cellular receptor named angiotensin-converting enzyme 2. Recent evidence proposed that the CD147 as a SARS-CoV-2's potential receptor. The viral genome is mainly held by two non-structural proteins (NSPs), ORF1a and ORF1ab, along with structural proteins. Although NSPs are conserved among the ßCoVs, mutations in NSP2 and NSP3 may play critical roles in transmitting the virus and cell tropism. To date, no specific/targeted anti-viral treatments exist. Notably, more than 50 COVID-19 candidate vaccines in clinical trials, and a few being administered. Preventive precautions are the primary strategy to limit the viral load transmission and spread, emphasizing the urgent need for developing significant drug targets and vaccines against COVID-19. This review provides a cumulative overview of the genomic structure, transmission, phylogeny of SARS-CoV-2 from Indian clusters, treatment options, updated discoveries, and future standpoints for COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-021-02749-0.

Mixed azo dyes degradation by an intracellular azoreductase enzyme from alkaliphilic Bacillus subtilis: a molecular docking study.

The rise of pollution due to the dye industries and textile wastes are evolving rapidly every day. The dyes are used in different trade names by the textile industries. The actual chemistry of dye is vague and difficult to understand even today though we are equipped technically. The toxic effects of the dyes and the reasons behind the acute toxicity are also an undiscovered mystery; therefore, no effective measures can be employed to degrade dyes. Deploying physical or chemical methods to pre-treat the azo dyes are expensive, extremely energy-consuming, and are not environment friendly. Hence, the use of microbes for textile dye degradation will be eco-friendly and is probably a cost-effective alternative to physicochemical methods. The present study was conducted to investigate the degradation of azo dyes isolated from textile effluent contaminated soil by employing the bacterial strains for degradation. The bacterial strains could degrade the optimum concentration of mixed azo dyes (200 mg/L) with an incubation up to 5 days. The decolourization of the dyes was expressed in terms of percentage of decolourization, and was found that about 87.35% of degradation by Bacillus subtilis strain. The enzyme responsible was analyzed as intracellular azoreductase involved in the degradation of mixed azo dyes. The enzymatic pathway and 1-phenyl-2-4(4-methyl phenyl)-diazene 1-oxide was observed as the major metabolite by GC-MS analysis. The in silico study determined the binding of mixed azo dye with azoreductase and hypothesized that their linking could be the main reason for the degradation of mixed azo dye.

Molecular dynamics, residue network analysis, and cross-correlation matrix to characterize the deleterious missense mutations in GALE causing galactosemia III.

Epimerase-deficiency galactosemia (EDG) is caused by mutations in the UDP-galactose 4'-epimerase enzyme, encoded by gene GALE. Catalyzing the last reaction in the Leloir pathway, UDP-galactose-4-epimerase catalyzes the interconversion of UDP-galactose and UDP-glucose. This study aimed to use in-depth computational strategies to prioritize the pathogenic missense mutations in GALE protein and investigate the systemic behavior, conformational spaces, atomic motions, and cross-correlation matrix of the GALE protein. We searched four databases (dbSNP, ClinVar, UniProt, and HGMD) and major biological literature databases (PubMed, Science Direct, and Google Scholar), for missense mutations that are associated with EDG patients, our search yielded 190 missense mutations. We applied a systematic computational prediction pipeline, including pathogenicity, stability, biochemical, conservational, protein residue contacts, and structural analysis, to predict the pathogenicity of these mutations. We found three mutations (p.K161N, p.R239W, and p.G302D) with a severe phenotype in patients with EDG that correlated with our computational prediction analysis; thus, they were selected for further structural and simulation analyses to compute the flexibility and stability of the mutant GALE proteins. The three mutants were subjected to molecular dynamics simulation (MDS) with native protein for 200 ns using GROMACS. The MDS demonstrated that these mutations affected the beta-sheets and helical region that are responsible for the catalytic activity; subsequently, affects the stability and flexibility of the mutant proteins along with a decrease and more deviations in compactness when compared to that of a native. Also, three mutations created major variations in the combined atomic motions of the catalytic and C-terminal regions. The network analysis of the residues in the native and three mutant protein structures showed disturbed residue contacts occurred owing to the missense mutations. Our findings help to understand the structural behavior of a protein owing to mutation and are intended to serve as a platform for prioritizing mutations, which could be potential targets for drug discovery and development of targeted therapeutics.

Plant Bioactive Peptides: Current Status and Prospects Towards Use on Human Health.

Large numbers of bioactive peptides with potential applications in protecting against human diseases have been identified from plant sources. In this review, we summarized recent progress in the research of plant-derived bioactive peptides, encompassing their production, biological effects, and mechanisms. This review focuses on antioxidant, antimicrobial, antidiabetic, and anticancer peptides, giving special attention to evidence derived from cellular and animal models. Studies investigating peptides with known sequences and well-characterized peptidic fractions or protein hydrolysates will be discussed. The use of molecular docking tools to elucidate inter-molecular interactions between bioactive peptides and target proteins is highlighted. In conclusion, the accumulating evidence from in silico, in vitro and in vivo studies to date supports the envisioned applications of plant peptides as natural antioxidants as well as health-promoting agents. Notwithstanding, much work is still required before the envisioned applications of plant peptides can be realized. To this end, future researches for addressing current gaps were proposed.

The Rise and Impact of COVID-19 in India.

The coronavirus disease (COVID-19) pandemic, which originated in the city of Wuhan, China, has quickly spread to various countries, with many cases having been reported worldwide. As of May 8th, 2020, in India, 56,342 positive cases have been reported. India, with a population of more than 1.34 billion-the second largest population in the world-will have difficulty in controlling the transmission of severe acute respiratory syndrome coronavirus 2 among its population. Multiple strategies would be highly necessary to handle the current outbreak; these include computational modeling, statistical tools, and quantitative analyses to control the spread as well as the rapid development of a new treatment. The Ministry of Health and Family Welfare of India has raised awareness about the recent outbreak and has taken necessary actions to control the spread of COVID-19. The central and state governments are taking several measures and formulating several wartime protocols to achieve this goal. Moreover, the Indian government implemented a 55-days lockdown throughout the country that started on March 25th, 2020, to reduce the transmission of the virus. This outbreak is inextricably linked to the economy of the nation, as it has dramatically impeded industrial sectors because people worldwide are currently cautious about engaging in business in the affected regions.

An extensive computational approach to analyze and characterize the functional mutations in the galactose-1-phosphate uridyl transferase (GALT) protein responsible for classical galactosemia.

Type I galactosemia is a very rare autosomal recessive genetic metabolic disorder that occurs because of the mutations present in the galactose-1-phosphate uridyl transferase (GALT) gene, resulting in a deficiency of the GALT enzyme. The action of the GALT enzyme is to convert galactose-1-phosphate and uridine diphosphate glucose into glucose-1-phosphate (G1P) and uridine diphosphate-galactose, a crucial second step of the Leloir pathway. A missense mutation in the GALT enzyme leads to variable galactosemia's clinical presentations, ranging from mild to severe. Our study aimed to employ a comprehensive computational pipeline to analyze the most prevalent missense mutations (p.S135L, p.K285 N, p.Q188R, and p.N314D) responsible for galactosemia; these genes could serve as potential targets for chaperone therapy. We analyzed the four mutations through different computational analyses, including amino acid conservation, in silico pathogenicity and stability predictions, and macromolecular simulations (MMS) at 50 ns The stability and pathogenicity predictors showed that the p.Q188R and p.S135L mutants are the most pathogenic and destabilizing. In agreement with these results, MMS analysis demonstrated that the p.Q188R and p.S135L mutants possess higher deviation patterns, reduced compactness, and intramolecular H-bonds of the protein. This could be due to the physicochemical modifications that occurred in the mutants p.S135L and p.Q188R compared to the native. Evolutionary conservation analysis revealed that the most prevalent mutations positions were conserved among different species except N314. The proposed research study is intended to provide a basis for the therapeutic development of drugs and future treatment of classical galactosemia and possibly other genetic diseases using chaperone therapy.

Bioinformatics classification of mutations in patients with Mucopolysaccharidosis IIIA.

Mucopolysaccharidosis (MPS) IIIA, also known as Sanfilippo syndrome type A, is a severe, progressive disease that affects the central nervous system (CNS). MPS IIIA is inherited in an autosomal recessive manner and is caused by a deficiency in the lysosomal enzyme sulfamidase, which is required for the degradation of heparan sulfate. The sulfamidase is produced by the N-sulphoglucosamine sulphohydrolase (SGSH) gene. In MPS IIIA patients, the excess of lysosomal storage of heparan sulfate often leads to mental retardation, hyperactive behavior, and connective tissue impairments, which occur due to various known missense mutations in the SGSH, leading to protein dysfunction. In this study, we focused on three mutations (R74C, S66W, and R245H) based on in silico pathogenic, conservation, and stability prediction tool studies. The three mutations were further subjected to molecular dynamic simulation (MDS) analysis using GROMACS simulation software to observe the structural changes they induced, and all the mutants exhibited maximum deviation patterns compared with the native protein. Conformational changes were observed in the mutants based on various geometrical parameters, such as conformational stability, fluctuation, and compactness, followed by hydrogen bonding, physicochemical properties, principal component analysis (PCA), and salt bridge analyses, which further validated the underlying cause of the protein instability. Additionally, secondary structure and surrounding amino acid analyses further confirmed the above results indicating the loss of protein function in the mutants compared with the native protein. The present results reveal the effects of three mutations on the enzymatic activity of sulfamidase, providing a molecular explanation for the cause of the disease. Thus, this study allows for a better understanding of the effect of SGSH mutations through the use of various computational approaches in terms of both structure and functions and provides a platform for the development of therapeutic drugs and potential disease treatments.