LppDB - a Comprehensive Database for Lipopeptide based drugs: Exploring potential drug discovery aspects.

AIM: Lipopeptides are amphiphilic compounds consisting of a cyclic peptide attached to a fatty acid side chain. These are found with various medicinal properties ranging from broad-spectrum antimicrobial to anticancer properties. INTRODUCTION: Elaborated studies on the therapeutic properties of peptides are hindered due to a lack of databases specifically dedicated to such classes of compounds where all the medicinal information of compounds is collectively studied in one place, which can accelerate the future scope of research on peptidolipidic compounds. METHOD: The chemical information was retrieved from the PubChem compound database, and their metabolism information was generated through Swiss ADME. The application information about their particular pathways was obtained from different kinds of literature. RESULTS: A python script was developed to extract the information about lipopeptides from a CSV file, and the information was stored in MySQL Database. The database contains information for 70 lipopeptides and their biochemical information, structure, chemical and physical properties, information on industrial and medicinal applications, functions and references for lipopeptides. CONCLUSION: The development of web-based database applications allows the data stored in the database to be accessed and viewed publicly.

Opportunistic Challenges of Computer-aided Drug Discovery of Lipopeptides: New Insights for Large Molecule Therapeutics.

Computer-aided drug designing is a promising approach to defeating the dry pipeline of drug discovery. It aims at reduced experimental efforts with cost-effectiveness. Naturally occurring large molecules with molecular weight higher than 500 Dalton such as cationic peptides, cyclic peptides, glycopeptides and lipopeptides are a few examples of large molecules which have successful applications as the broad spectrum antibacterial, anticancer, antiviral, antifungal and antithrombotic drugs. Utilization of microbial metabolites as potential drug candidates incur cost effectiveness through large scale production of such molecules rather than a synthetic approach. Computational studies on such compounds generate tremendous possibilities to develop novel leads with challenges to handle these complex molecules with available computational tools. The opportunities begin with the desired structural modifications in the parent drug molecule. Virtual modifications followed by molecular interaction studies at the target site through molecular modeling simulations and identification of structure-activity relationship models to develop more prominent and potential drug molecules. Lead optimization studies to develop novel compounds with increased specificity and reduced off targeting is a big challenge computationally for large molecules. Prediction of optimized pharmacokinetic properties facilitates development of a compound with lower toxicity as compared to the natural compounds. Generating the library of compounds and studies for target specificity and ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) for large molecules are laborious and incur huge cost and chemical wastage through in-vitro methods. Hence, computational methods need to be explored to develop novel compounds from natural large molecules with higher specificity. This review article is focusing on possible challenges and opportunities in the pathway of computer-aided drug discovery of large molecule therapeutics.

Modern Paradigm Towards Potential Target Identification for Antiviral (SARS-nCoV-2) and Anticancer Lipopeptides: A Pharmacophore-Based Approach.

Background: Lipopeptides are potential microbial metabolites that are abandoned with broad spectrum biopharmaceutical properties ranging from antimicrobial, antiviral and anticancer, etc. Clinical studies are not much explored beyond the experimental methods to understand drug mechanisms on target proteins at the molecular level for large molecules. Due to the less available studies on potential target proteins of lipopeptide based drugs, their potential inhibitory role for more obvious treatment on disease have not been explored in the direction of lead optimization. However, Computational approaches need to be utilized to explore drug discovery aspects on lipopeptide based drugs, which are time saving and cost-effective techniques. Methods: Here a ligand-based drug discovery approach is coupled with reverse pharmacophore-mapping for the prediction of potential targets for antiviral (SARS-nCoV-2) and anticancer lipopeptides. Web-based servers PharmMapper and Swiss Target Prediction are used for the identification of target proteins for lipopeptides surfactin and iturin produced by Bacillus subtilis. Results: The studies have given the insight to treat the diseases with next-generation large molecule therapeutics. Results also indicate the affinity for Angiotensin-Converting Enzymes (ACE) and proteases as the potential viral targets for these categories of peptide therapeutics. A target protein for the Human Papilloma Virus (HPV) has also been mapped. Conclusion: The work will further help in exploring computer-aided drug designing of novel compounds with greater efficiency where the structure of the target proteins and lead compounds are known.

Purification and characterization of chitinase produced by thermophilic fungi Thermomyces lanuginosus.

BACKGROUND: In the past few years, the production of shrimp shell waste from the seafood processing industries has confronted a significant surge. Furthermore, insignificant dumping of waste has dangerous effects on both nature and human well-being. This marine waste contains a huge quantity of chitin which has several applications in different fields. The chitinase enzyme can achieve degradation of chitin, and the chitin itself can be used as the substrate as well for production of chitinase. In the current study, the chitinase enzyme was produced by Thermomyces lanuginosus. The extracellular chitinase was purified from crude extract using ammonium sulfate precipitation followed by DEAE-cellulose ion-exchange chromatography and Sephadex G-100 gel filtration chromatography. The stability and activity of chitinase with different pH, temperature, different times for a reaction, in the presence of different metal ions, and different concentration of enzyme and substrate were analyzed. RESULT: The chitinase activity was found to be highest at pH 6.5, 50 °C, and 60 min after the reaction began. and the chitinase showed the highest activity and stability in the presence of ß-mercaptoethanol (ME). The SDS-PAGE of denatured purified chitinase showed a protein band of 18 kDa. CONCLUSION: The characterization study concludes that Cu2+, Hg2+, and EDTA have an inhibitory effect on chitinase activity, whereas ß-ME acts as an activator for chitinase activity. The utilization of chitin to produce chitinase and the degradation of chitin using that chitinase enzyme would be an opportunity for bioremediation of shrimp shell waste.

Emerging strategies on in silico drug development against COVID-19: challenges and opportunities.

The importance of coronaviruses as human pathogen has been highlighted by the recent outbreak of SARS-CoV-2 leading to the search of suitable drugs to overcome respiratory infections caused by the virus. Due to the lack of specific drugs against coronavirus, the existing antiviral and antimalarial drugs are currently being administered to the patients infected with SARS-CoV-2. The scientists are also considering repurposing of some of the existing drugs as a suitable option in search of effective drugs against coronavirus till the establishment of a potent drug and/or vaccine. Computer-aided drug discovery provides a promising attempt to enable scientists to develop new and target specific drugs to combat any disease. The discovery of novel targets for COVID-19 using computer-aided drug discovery tools requires knowledge of the structure of coronavirus and various target proteins present in the virus. Targeting viral proteins will make the drug specific against the virus, thereby, increasing the chances of viral mortality. Hence, this review provides the structure of SARS-CoV-2 virus along with the important viral components involved in causing infection. It also focuses on the role of various target proteins in disease, the mechanism by which currently administered drugs act against the virus and the repurposing of few drugs. The gap arising from the absence of specific drugs is addressed by proposing potential antiviral drug targets which might provide insights into structure-based drug development against SARS-CoV-2.

Optimization of process parameters for improved chitinase activity from Thermomyces sp. by using artificial neural network and genetic algorithm.

Chitinase is responsible for the breaking down of chitin to N-acetyl-glucosamine units linked through (1-4)-glycosidic bond. The chitinases find several applications in waste management and pest control. The high yield with characteristics thermal stability of chitinase is the key to their industrial application. Therefore, the present work focuses on parameter optimization for chitinase production using fungus Thermomyces lanuginosus MTCC 9331. Three different optimization approaches, namely, response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) were used. The parameters under study were incubation time, pH and inoculum size. The central composite design with RSM was used for the optimization of the process parameters. Further, results were validated with GA and ANN. A multilayer feed-forward algorithm was performed for ANN, i.e., Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The ANN predicted values gave higher chitinase activity, i.e., 102.24 U/L as compared to RSM-predicted values, i.e., 88.38 U/L. The predicted chitinase activity was also closer to the observed data at these levels. The validation study suggested that the highest activity of chitinase as predicted by ANN is in line with experimental analysis. The comparison of three different statistical approaches suggested that ANN gives better optimization results compared to the GA and RSM study.

Artificial neural network and response surface methodology: a comparative analysis for optimizing rice straw pretreatment and saccharification.

The present study demonstrates a comparative analysis between the artificial neural network (ANN) and response surface methodology (RSM) as optimization tools for pretreatment and enzymatic hydrolysis of lignocellulosic rice straw. The efficacy for both the processes, that is, pretreatment and enzymatic hydrolysis was evaluated using correlation coefficient (R2) & mean squared error (MSE). The values of R2 obtained by ANN after training, validation, and testing were 1, 0.9005, and 0.997 for pretreatment and 0.962, 0.923, and 0.9941 for enzymatic saccharification, respectively. On the other hand, the R2 values obtained with RSM were 0.9965 for cellulose recovery and 0.9994 for saccharification efficiency. Thus, ANN and RSM together successfully identify the substantial process conditions for rice straw pretreatment and enzymatic saccharification. The percentage of error for ANN and RSM were 0.009 and 0.01 for cellulose recovery and for 0.004 and 0.005 for saccharification efficiency, respectively, which showed the authority of ANN in exemplifying the non-linear behavior of the system.

Prediction of stenosis behaviour in artery by neural network and multiple linear regressions.

Blood flow analysis in the artery is a paramount study in the field of arterial stenosis evaluation. Studies conducted so far have reported the analysis of blood flow parameters using different techniques, but the regression analysis is not adequately used. Artificial neural network is a nonlinear and nonparametric approach. It uses back-propagation algorithm for regression analysis, which is effective as compared to statistical model that requires a higher domain of statistics for prediction. In our manuscript, twofold analyses of data are done. First phase involves the determination of blood flow parameters using physiological flow pulse generator. The second phase includes regression modelling. The inputs to the model were axial length from stenosis, radial distance, inlet velocity, mean pressure, density, viscosity, time, and degree of blockage. Output included dependent variables in the form of output as mean velocity, root-mean-square (RMS) velocity, turbulent intensity, mean frequency, RMS frequency, frequency of turbulent intensity, gate time mean, gate time RMS. The temperature, density, and viscosity conditions were kept constant for various degrees of blockages. It was followed by regression analysis of variables using conventional statistical and neural network approach. The result shows that the neural network model is more appropriate, because value of percentage of response variation of dependent variable is almost approaching unity as compared to statistical analysis.

Extraction and optimization of exopolysaccharide from Lactobacillus sp. using response surface methodology and artificial neural networks.

The microbial polysaccharides secreted and produced from various microbes into their extracellular environment is known as exopolysaccharide. These polysaccharides can be secreted from the microbes either in a soluble or insoluble form.Lactobacillus sp. is one of the organisms that have been found to produce exopolysaccharide. Exo-polysaccharides (EPS) have various applications such as drug delivery, antimicrobial activity, surgical implants and many more in different fields. Medium composition is one of the major aspects for the production of EPS from Lactobacillus sp., optimization of medium components can help to enhance the synthesis of EPS . In the present work, the production of exopolysaccharide with different medium composition was optimized by response surface methodology (RSM) followed by tested for fitting with artificial neural networks (ANN). Three algorithms of ANN were compared to investigate the highest yeild of EPS. The highest yeild of EPS production in RSM was achieved by the medium composition that consists of (g/L) dextrose 15, sodium dihydrogen phosphate 3, potassium dihydrogen phosphate 2.5, triammonium citrate 1.5, and, magnesium sulfate 0.25. The output of 32 sets of RSM experiments were tested for fitting with ANN with three algorithms viz. Levenberg-Marquardt Algorithm (LMA), Bayesian Regularization Algorithm (BRA) and Scaled Conjugate Gradient Algorithm (SCGA) among them LMA found to have best fit with the experiments as compared to the SCGA and BRA.

Homology Modeling and Molecular Docking Studies of Bacillomycin and Iturin Synthetases with Novel Ligands for the Production of Therapeutic Lipopeptides.

BACKGROUND: Lipopeptide synthetases play an important role in the production of lipopeptides. Lipopeptides are molecules made up of peptides and fatty acid moieties and have shown to have a broad range of antimicrobial activity. As infectious diseases have caused severe health problems mainly resulting from the development of antibiotic resistant strains of disease causing microorganisms there is a need of alternatives to antibiotics. The lipopeptide synthetase of the corresponding lipopeptides can be used as template to design these as drugs using computational techniques. OBJECTIVE: The objective of this study was homology modeling and molecular docking of two lipopeptide synthetases, bacillomycin D synthetase and iturin A synthetase, with their ligands as a means of drug design. METHOD: Schrödinger software was used for homology modeling and molecular docking. RESULTS: After the identification of ligands, molecular docking of these ligands with the lipopeptide (bacillomycin and iturin) synthetases was performed. The docking was tested on the parameters of docking score and glide energy. 5 out of 21 ligands were found to dock with bacillomycin D synthetase whereas 8 out of 20 ligands docked with the iturin A synthetase. CONCLUSION: The knowledge of the docking sites and docking characteristics of the lipopeptide synthetases mentioned in the paper with the ligands can provide advantages of high speed and reliability, reduced costs on chemicals and experiments and the ethical issues concerned with the use of animal models for screening of drug toxicity.

Therapeutic cyclic lipopeptides mining from microbes: latest strides and hurdles.

Infectious diseases impose serious public health burdens and often have devastating consequences. The cyclic lipopeptides elaborated by bacteria Bacillus, Paenibacillus, Pseudomonas, Streptomyces, Serratia, Propionibacterium and fungus Fusarium are very crucial in restraining the pathogens. Composed of a peptide and a fatty acyl moiety these amphiphilic metabolites exhibit broad spectrum antimicrobial effects. Among the plethora of cyclic lipopeptides, only selective few have emerged as robust antibiotics. For their functional vigor, polymyxin, daptomycin, surfactin, iturin, fengysin, paenibacterin and pseudofactin have been integrated in mainstream healthcare. Daptomycin has been a significant part of antimicrobial arsenal since the past decade. As the magnitude of drug resistance rises in unprecedented manner, the urgency of prospecting novel cyclic lipopeptides is being perceived. Intense research has revealed the implication of these bioactive compounds stretching beyond antibacterial and antifungal. Anticancer, immunomodulatory, prosthetic parts disinfection and vaccine adjuvancy are some of the validated prospects. This review discusses the emerging applications, mechanisms governing the biological actions, role of genomics in refining structure and function, semi-synthetic analog discovery, novel strain isolation, setbacks etc. Though its beyond the scope of the current topic, for holistic purpose, the role of lipopeptides in bioremediation and crop biotechnology has been briefly outlined. This updated critique is expected to galvanize innovations and diversify therapeutic recruitment of microbial lipopeptides.