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.

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.

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.

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.

Genotype-phenotype correlation in patients with isovaleric acidaemia: comparative structural modelling and computational analysis of novel variants.

Isovaleric acidaemia (IVA) is an autosomal recessive inborn error of leucine metabolism. It is caused by a deficiency in the mitochondrial isovaleryl-CoA dehydrogenase (IVD) enzyme. In this study, we investigated eight patients with IVA. The patients' diagnoses were confirmed by urinary organic acid analysis and the blood C5-Carnitine value. A molecular genetic analysis of the IVD gene revealed nine different variants: five were missense variants (c.1193G > A; p. R398Q, c.1207T > A; p. Y403N, c.872C > T; p. A291V, c.749G > C; p. G250A, c.1136T > C; p.I379T), one was a frameshift variant (c.ins386 T; p. Y129fs), one was a splicing variant (c.465 + 2T > C), one was a polymorphism (c.732C > T; p. D244D), and one was an intronic benign variant (c.287 + 14T > C). Interestingly, all variants were in homozygous form, and four variants were novel (p. Y403N, p. Y129fs, p. A291V, p. G250A) and absent from 200 normal chromosomes. We performed protein modelling and dynamics analyses, pathogenicity and stability analyses, and a physiochemical properties analysis of the five missense variants (p.Y403N, R398Q, p.A291V, p.G250A, and p.I379T). Variants p.I379T and p.R398Q were found to be the most deleterious and destabilizing compared to variants p.A291V and p.Y403N. However, the four variants were predicted to be severe by the protein dynamic and in silico analysis, which was consistent with the patients' clinical phenotypes. The p.G250A variant was computationally predicted as mild, which was consistent with the severity of the clinical phenotype. This study reveals a potentially meaningful genotype-phenotype correlation for our patient cohort and highlights the development and use of this computational analysis for future assessments of genetic variants in the clinic.

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.

Binding and molecular dynamic studies of sesquiterpenes (2R-acetoxymethyl-1,3,3-trimethyl-4t-(3-methyl-2-buten-1-yl)-1t-cyclohexanol) derived from marine Streptomyces sp. VITJS8 as potential anticancer agent.

The main aim of the current study is to explore the bioactive potential of Streptomyces sp. VITJS8 isolated from the marine saltern. The cultural, biochemical, and morphological studies were performed to acquire the characteristic features of the potent isolate VITJS8. The 16Sr DNA sequencing was performed to investigate the phylogenetic relationship between the Streptomyces genera. The structure of the compound was elucidated by gas chromatography-mass spectrometry (GC-MS), infra-red (IR), and ultra-violet (UV) spectroscopic data analysis. The GC-MS showed the retention time at 22.39 with a single peak indicating the purity of the active compound, and the molecular formula was established as C14H9ONCl2 based on the peak at m/z 277 [M](+). Furthermore, separated by high-performance liquid chromatography (HPLC), their retention time (t r) 2.761 was observed with the absorption maxima at 310 nm. The active compound showed effective inhibitory potential against four clinical pathogens at 500 µg/mL. The antioxidant activity was found effective at the IC50 value of 500 µg/mL with 90 % inhibition. The 3-(4,5-dimethylthiazol-2-yl)-2,5-ditetrazolium bromide (MTT) assay revealed the cytotoxicity against HepG2 cells at IC50 of 250 µg/mL. The progression of apoptosis was evidenced by morphological changes by nuclear staining. The DNA fragmentation pattern was observed at 250 µg/mL concentration. Based on flow cytometric analysis, it was evident that the compound was effective in inhibiting the sub-G0/G1 phase of cell cycle. The in vitro findings were also supported by the binding mode molecular docking studies. The active compound revealed minimum binding energy of -7.84 and showed good affinity towards the active region of topoisomerase-2α that could be considered as a suitable inhibitor. Lastly, we performed 30 ns molecular dynamic simulation analysis using GROMACS to aid in better designing of anticancer drugs. Simulation result of root mean square deviation (RMSD) analysis showed that protein-ligand complex reaches equilibration state around 10 ns that illustrates the docked complex is stable. We propose the possible mechanism of sesquiterpenes to play a significant role in antitumor cascade. Hence, our studies open up a new facet for a potent drug as an anticancer agent.

CoagVDb: a comprehensive database for coagulation factors and their associated SAPs.

The current state of the art in medical genetics is to identify and classify the functional (deleterious) or non-functional (neutral) single amino acid substitutions (SAPs), also known as non-synonymous SNPs (nsSNPs). The primary goal is to elucidate the mechanisms through which functional SAPs exert their effects, and ultimately interrogating this information for association with complex phenotypes. This work focuses on coagulation factors involved in the coagulation cascade pathway which plays a vital role in the maintenance of homeostasis in the human system. We developed an integrated coagulation variation database, CoagVDb, which makes use of the biological information from various public databases such as NCBI, OMIM, UniProt, PDB and SAPs (rsIDs/variant). CoagVDb enriched with computational prediction scores classify SAPs as either deleterious or tolerated. Also, various other properties are incorporated such as amino acid composition, secondary structure elements, solvent accessibility, ordered/disordered regions, conservation, and the presence of disulfide bonds. This specialized database provides integration of various prediction scores from different computational methods along with gene, protein, and disease information. We hope our database will act as a useful reference resource for hematologists to reveal protein structure-function relationship and disease genotype-phenotype correlation.

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 and Dynamics Studies 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 Withania somnifera based supplement for immunomodulatory and antiviral properties against viral infection.

BACKGROUND: Viral mediated diseases are continuously posing potent threat to human health. Nutraceuticals are being employed as novel therapeutics during viral outbreaks. MAM granules consist of Curcuma longa, Withania somnifera, and Piper nigrum, is one such patented Siddha nutraceutical supplement that has been proposed to be a therapeutic agent against viral diseases. OBJECTIVE: We characterised MAM for their phytochemical and physicochemical properties and evaluated its cytotoxicity via in vivo acute toxicity studies using Wistar rats and by cell-based MTT assays. MATERIALS AND METHODS: The antiviral properties of the aqueous extract of MAM were investigated against SARS-CoV-2 and chikungunya virus (CHIKV). Further, using ABTS radical scavenging, SOD enzymatic assays and measurement of intracellular ROS, we investigated the antioxidant potential of MAM extract and its ingredients in RAW264.7 cells. Additionally, production of inflammatory mediators was evaluated via NO release, PGE2 production and release of pro-inflammatory cytokines (IL-1ß and TNFα). RESULTS: The MAM granules and aqueous extracts demonstrated no significant toxicity and demonstrated potent antiviral activity during co-incubation assay with SARS-CoV-2 and CHIKV. Moreover, we observed potent antioxidant and anti-inflammatory activity of MAM extract in a dose dependent manner. Further investigations on the individual ingredients with respect to their antioxidant and anti-inflammatory activities showed that all ingredients contributed synergistically and Withania somnifera showed most potent anti-oxidant activity. CONCLUSION: The overall in vitro, and in vivo analysis demonstrated that MAM granules were non-toxic and possessed potent antiviral activity. Additionally, observed significant anti-oxidant and anti-inflammatory properties of MAM suggested the modulation of innate immune response in the host validating its use as an effective nutraceutical during viral outbreaks.

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.

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.

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.

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.

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.

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.

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.

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.