Biosurfactants are amphiphilic compounds produced by micro-organisms that are excreted extracellularly and contain hydrophobic as well as hydrophilic moieties which reduce surface tension. These extracellular membrane vesicles aid in the uptake of organic molecules that are used as a source of energy for growth of micro-organisms. These micro-organisms have the ability to grow and proliferate on the oil-based medium like petro-contaminated soil and water. Oil spills and discharge from industry contaminates the water bodies and surrounding land. Many chemical-based technologies and manual cleaning are involved to reduce water and land pollution. Chemicals used are the surfactants that lead to the generation of toxic by-products. Hence, considerable attention has been given to surface-active molecules of biological origin called biosurfactants, produced by microbial species in and around the contaminated area. The present study focuses on identifying a novel biosurfactant-producing bacterial strain from brackish water. The bacterial consortium was isolated from targeted brackish water site. Each bacterial species was tested for its biosurfactant production ability using various assays. Haemolysis, oil spreading, cetyltrimethyl ammonium bromide agar plate, drop collapse, hydrocarbon overlay agar, hydrocarbon assay and lipase production were a few qualitative methods performed. Further, the most efficient biosurfactant producer was identified by measuring emulsification index, quantifying biosurfactant production and efficient reduction in surface tension. 16S rRNA gene sequencing was performed for phenotypic characterization of the biosurfactant-producing strain, using Sanger dideoxy sequencing method followed by the phylogenetic assessment. The isolate was found to be a novel Bacillus tequilensis strain, named as ANSKLAB04. The novel isolate was deposited in GenBank with Accession number KU529483.

Neurodegenerative diseases, such as Parkinson's and Alzheimer's, are understood as occurring through genetic, cellular, and multifactor pathophysiological mechanisms. Several natural products such as flavonoids have been reported in the literature for having the capacity to cross the blood-brain barrier and slow the progression of such diseases. The present article reports on in silico enzymatic target studies and natural products as inhibitors for the treatment of Parkinson's and Alzheimer's diseases. In this study we evaluated 39 flavonoids using prediction of molecular properties and in silico docking studies, while comparing against 7 standard reference compounds: 4 for Parkinson's and 3 for Alzheimer's. Osiris analysis revealed that most of the flavonoids presented no toxicity and good absorption parameters. The Parkinson's docking results using selected flavonoids as compared to the standards with four proteins revealed similar binding energies, indicating that the compounds 8-prenylnaringenin, europinidin, epicatechin gallate, homoeriodictyol, capensinidin, and rosinidin are potential leads with the necessary pharmacological and structural properties to be drug candidates. The Alzheimer's docking results suggested that seven of the 39 flavonoids studied, being those with the best molecular docking results, presenting no toxicity risks, and having good absorption rates (8-prenylnaringenin, europinidin, epicatechin gallate, homoeriodictyol, aspalathin, butin, and norartocarpetin) for the targets analyzed, are the flavonoids which possess the most adequate pharmacological profiles.

Degradation of Petroleum-plastics like Low Density Polyethylene (LDPE) is a budding challenge due to increasing white pollution. The present investigation has focused the aspect through microbial assisted biodegradation. Various indigenous microorganisms were isolated from collected municipal landfill soil. Growth medium enriched with 0.2 g of LDPE powder was used to screen the soil bacteria with biodegradation potential. The screened bacteria were subjected to biodegradation assay in presence of LDPE sheets in growth medium. Four strains gave 5%, 17.8%, 0.9% and 0.6% degradation rate based on weight loss in the conducted in vitro assay for four days. The maximum degraded sheet was analyzed through Scanning Electron Microscopy, Fourier transform infrared spectroscopy and Thermogravimetry, taking undegraded LDPE sheet as control. Results illustrated one-step weight loss with control and three-step weight loss with test. Thus, it proved the efficacy of isolated strain. The strain identification was carried out by genomic DNA isolation followed by PCR and 16S rRNA sequencing. Genotypic identification revealed the bacterium as Pseudomonas citronellolis. BLAST gave a similarity with the database of 96%, thus phylogenetic assessment clarified the bacterium as a novel strain. The isolate was named as Pseudomonas citronellolis EMBS027 and sequence was deposited as LDPE degrading species, in GenBank with accession number KF361478.Electronic supplementary materialThe online version of this article (doi:10.1186/2193-1801-3-497) contains supplementary material, which is available to authorized users.

Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short span of time. The present review is an overview based on some applications of Machine Learning based tools such as GOLD, DeepPVP, LIBSVM, etc and the algorithms involved such as support vector machine (SVM), random forest (RF), decision trees and artificial neural networks (ANN) etc in the various stages of drug designing and development. These techniques can be employed in SNP discoveries, drug repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure-based virtual screening (SBVS), Lead identification, quantitative structure-activity relationship (QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance in indicating that the classification model will have great applications on human intesti-nal absorption (HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and RF model in identifying JFD00950 as a novel compound targeting against a colon cancer cell line, DLD-1 by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also used to predicts flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus (DM), using ANN. Hence, in the era of big data, ML approaches evolved as a powerful and efficient way to deal with the huge amounts of generated data from modern drug discovery in order to model small-molecule drugs, Gene Biomarkers, and identifying the novel drug targets for various diseases.