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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 computeraided drug discovery of large molecule therapeutics. [ABSTRACT FROM AUTHOR]

The endeavor has been attempted to present a review on the evolution of modern age drug discovery in India. The contribution of next generation therapeutics options microbial metabolites and the computational drug discovery aspects to the global market from India have been represented. Microbial metabolites such as lipopeptides and peptide therapeutics are gaining worldwide importance due to their multiple applications as broad-spectrum antimicrobial, antiviral, anticancer properties etc. Due to the surge of microbial resistance, tumor resistance, and ongoing pandemic due to constantly mutating corona virus, there is a need to develop next-generation therapeutics options from natural origin, less toxic to the environment, and have higher specificity towards target. Small molecule therapeutics are certainly less specific towards cancer targets hence the cytotoxicity is a major issue in cancer treatment while drug resistance due to the mutations are coming as challenges every day for drug discovery researchers. Microbial lipopeptide reserves a sweet spot in between the small molecule inhibitors and peptide therapeutics because of their amphiphilic compounds consist of a fatty acid side chain and a cyclic peptide moiety of hydrophilic nature. The computational drug discovery approach accelerates the drug discovery process due to the advancement in supercomputer facilities provided by various funding agencies such as the Department of Biotechnology (DBT) and the Department of Science and Technology (DST) in India. The current review article is focusing light on the research contribution of Indian Scientists and Govt. of India in the field of lipopeptide-based research and applications of Computer-aided drug discovery. [ABSTRACT FROM AUTHOR]

BACKGROUND The current system in the processing of seafood leads to accumulation of many waste products, such as shells, tails, heads and bones. This seafood waste can be exploited for the extraction of chitin, with numerous applications in different fields. Seafood waste treatment can produce some valuable products. For the valorization of chitin, its degradation is an important step that can be achieved using the chitinase enzyme. Interestingly, chitin can also be used as a significant substrate for chitinase production. In this study, chitinase activity was enhanced by optimizing the fermentation medium, and chitin was used as the substrate. The polynomial model obtained by central composite design was employed in a particle swarm optimization algorithm and artificial neural network to optimize the final optimal concentration factors. The optimization results were compared for the better activity of chitinase. From the authors' best knowledge, the optimization of fermentation medium for chitinase production by particle swarm optimization was performed for the first time. RESULTS: The highest activity optimized by particle swarm optimization and artificial neural network/ Bayesian regularization algorithm) was 115.8 and 124.78 U L–1, respectively, with the optimized variables. CONCLUSION: This study concluded that particle swarm optimization and artificial neural network are the best optimization methods for medium optimization. Among the multilayer feed‐forward algorithms in the artificial neural network, the Bayesian regularization algorithm was useful in optimizing medium components. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Worldwide, marine crustacean waste is a major problem for environmental pollution, and it is a severe risk to the coastline area. Shellfish wastes consist of some commercially valuable products, mainly the chitin. The extraction of chitin from the shellfish waste is very complicated and required a successive pretreatment process. Sonication can improve the process of extraction of chitin from the shrimp shell waste. In this study, the conventional and ultrasonication method of pretreatment was applied and compared for the extraction of chitin. By the conventional method, 12 h was required for the removal of calcium and proteins each. In contrast, only 6 h was required for the complete removal of calcium and proteins each, by the ultrasonication assisted method. After pretreatment, the results were analyzed and compared by the already purified commercial chitin using Fourier-transform infrared spectroscopy. Ultrasonication improves the rate of reaction of the pretreatment by the process of cavitation. By this work, the ultrasonication technique was proved to be much faster than the conventional method for the pretreatment process. [ABSTRACT FROM AUTHOR]

A combination of methods was applied in this study to evaluate the removal of pentachlorophenol (PCP) from an Indian agricultural soil. Treatment involved PCP-resistant microorganism Vibrio sp. (SK1); their activity was combined with the adsorbing capacity of biochar. First, PCP-resistant microorganism was used to transform PCP at batch level and further extended to farmland soil. Experimental results indicated that bacterial activity was found to have pronounced positive effect on PCP removal, and decreased the PCP level up to 75% of soil spiked with 100 mg/kg, compared to control within 1-week period. Remaining PCP in soil was stabilized probably by sorption using biochar prepared from Pistia stratiotes; biochar was added at concentrations of 1%, 3%, and 5%. We observed that the 5% biochar strongly influenced the stabilization of PCP in the soil. 16s rRNA gene analysis indicated that bacterial strain belongs to Vibrio sp., which was not reported earlier for the PCP degradation. In our study, a parameter estimation method was used in mathematical models along with kinetic models. By using the nonlinear graphical regression method with the logistic growth curve, Monod, Contois, Moser, and the Tessier models, kinetic parameters were estimated. [ABSTRACT FROM AUTHOR]

The present study discusses optimization of cellulase production from isolated cellulolytic bacterium. A simulated annealing (SA) algorithm is proposed for optimization of these processes to achieve the desired production goal. The approach was compared to the use of evolutionary algorithms, i.e., genetic algorithms (GAs) and response surface methodology (RSM).Ochrobactrum haematophilumwas identified as the isolated bacteria. Carboxymethyl cellulose (CMC) concentration, yeast extract, pH, and incubation temperature were the significant factors screened by Plackett–Burman design and further optimized using a central composite design. The optimum values obtained were CMC concentration = 4.76% (w/v), yeast extract = 2.03% (w/v), pH = 6.3, and temperature = 44.2°C. Carboxy methyl cellulase (CMCase) activity at these values was experimentally determined to be 3.55 ± 0.16 U/ml, which was 2.8 times than the unoptimized system (1.23 U/ml). The growth-associated and non-growth-associated Leudeking–Piret constants, α and β, were respectively determined to be 0.3943 and 0.0105. The Michaelis–Menten constants,VmaxandKm, were determined to be 0.67 µmol/min and 2.42 mg CMC/ml, respectively. The variable-sized SA seems to be the best alternative, outperforming the GAs, showing a fast convergence and low variability among the several runs for optimized production cellulose recovery. The SA models are found to be capable of better predictions of cellulase production. The results of the SA-based RSM model indicate that it is much more robust and accurate in estimating the values of dependent variables when compared with the GA-based RSM models and only RSM models. [ABSTRACT FROM AUTHOR]

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., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)