جستجوی مقالات مرتبط با کلیدواژه "lipopeptides" در نشریات گروه "پزشکی"

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.

 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.

 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 SwissTargetPrediction are used for the identification of target proteins for lipopeptides surfactin and iturin produced by Bacillus subtilis.

 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.

 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.

Biosurfactants constitute a structurally diverse group of surface-active compounds derived from microorganisms. They are widely used industrially in various industrial applications such as pharmaceutical and environmental sectors. Major limiting factor in biosurfactant production is their production cost. The aim of this study was to investigate biosurfactant production under laboratory conditions with potato peels as the sole source of carbon source. A biosurfactant-producing bacterial strain (Bacillus pumilus DSVP18, NCBI GenBank accession no. GQ865643) was isolated from motor oil contaminated soil samples. Biochemical characteristics of the purified biosurfactant were determined and its chemical structure was analyzed. Stability studies were performed and biological activity of the biosurfactant was also evaluated. The strain, when grown on modified minimal salt media supplemented with 2% potato peels as the sole carbon source, showed the ability to reduce Surface Tension (ST) value of the medium from 72 to 28.7 mN/m. The isolated biosurfactant (3.2 ± 0.32 g/L) was stable over a wide range of temperatures (20 - 120 ºC), pH (2-12) and salt concentrations (2 - 12%). When characterized using high-performance liquid chromatography (HPLC) and Fourier transform infrared spectroscopy, it was found to be a lipopeptide in nature, which was further confirmed by Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (mass peak 1044.60) and nuclear magnetic resonance (NMR) studies. Data showed that the isolated biosurfactant at the concentration range of 30 - 35 µg/ml had strong antimicrobial activity when tested against standard strains of Bacillus cereus, Escherichia coli, Salmonella enteritidis, Staphylococcus aureus and Paenibacillus larvae. Potato peels were proved to be potentially useful substrates for biosurfactant production by B. pumilus DSVP18. The strain possessed a unique property to reduce surface tension of the media from 72 to 28.7 mN/m. In addition, it showed a stable surface activity over a wide range of temperatures, pH, and saline conditions and had strong antimicrobial activity. This potential of the identified biosurfactant can be exploited by pharmaceutical industries for its commercial usage.