Genome-wide identification, characterization and primer designing of simple sequence repeats across Leguminosae family.

Legumes are important clade of commercially important family Leguminosae that mainly include medicinal, flowering and edible plants. Although the genomic sequence of legumes is accessible, only the limited number of effective simple sequence repeat markers has been identified by prior research. Additional polymorphic simple sequence repeats marker discovery will aid in the genetics and breeding of legumes. In this study, 13 complete genome sequences were screened for the identification of chromosome-wise simple sequence repeats (SSRs) and 1,866,861 SSRs were identified. Based on the study, it was observed that the number of SSRs in non-coding region was more as compared to coding region and frequency of mononucleotides was highest followed by di-nucleotides while penta- and hexa-nucleotide repeats were least frequent one. The identified genome-wide SSRs and newly developed SSR markers, primers and their mapping will provide a powerful means for genetic researches across Leguminosae plants, including genetic diversity and evolutionary origin analysis, fingerprinting, QTL mapping and marker-assisted selection for breeding as well as comparative genomic analysis studies.

Artificial Intelligence for Computer-Aided Drug Discovery.

The continuous implementation of Artificial Intelligence (AI) in multiple scientific domains and the rapid advancement in computer software and hardware, along with other parameters, have rapidly fuelled this development. The technology can contribute effectively in solving many challenges and constraints in the traditional development of the drug. Traditionally, large-scale chemical libraries are screened to find one promising medicine. In recent years, more reasonable structure-based drug design approaches have avoided the first screening phases while still requiring chemists to design, synthesize, and test a wide range of compounds to produce possible novel medications. The process of turning a promising chemical into a medicinal candidate can be expensive and time-consuming. Additionally, a new medication candidate may still fail in clinical trials even after demonstrating promise in laboratory research. In fact, less than 10% of medication candidates that undergo Phase I trials really reach the market. As a consequence, the unmatched data processing power of AI systems may expedite and enhance the drug development process in four different ways: by opening up links to novel biological systems, superior or distinctive chemistry, greater success rates, and faster and less expensive innovation trials. Since these technologies may be used to address a variety of discovery scenarios and biological targets, it is essential to comprehend and distinguish between use cases. As a result, we have emphasized how AI may be used in a variety of areas of the pharmaceutical sciences, including in-depth opportunities for drug research and development.

Molecular characterization and protein structure prediction of heat shock transcriptional factors in goat (Capra hircus) and sheep (Ovis aries).

The heat shock factors are important as they are master regulator of heat shock response. There are only few mammalian HSFs which have been characterized, namely HSF-1, HSF-2, HSF-4 and HSF-5. The present study was aimed to clone and sequence characterize the partial open reading frames (ORFs) of HSF-2 and HSF-5 gene from cDNA isolated from testicular tissue of sheep (Macheri) and goat (Beetal). The partial ORFs of HSF-2 gene was observed to be 1627 bp in sheep and 1179 bp in goat and for HSF-5 it is 1137 bp in sheep and 1027 bp in goat. HSF-2 and HSF-5 encode a putative protein of 593 and 461 amino acid in goat and 568 and 553 amino acid in sheep, respectively. Phylogenetic analysis between the different orthologs suggested that these proteins are conserved from bovine to humans as well as in other mammals. Further, domain analyses using PredictNLS, MARCOIL and NetNES revealed that the members of HSF-2 protein orthologs contained all major domains, i.e., DNA-binding domain (DBD) and oligomerization domain (HR-A/B, and HR-C). The 3D structure of sheep and goat HSF-2 protein was predicted using SWISS-MODEL, which showed similar confirmation with the human HSF-2 protein sequence showing functional similarity between them.

Development of parthenium based activated carbon and its utilization for adsorptive removal of p-cresol from aqueous solution.

The activated carbon was prepared from carbonaceous agriculture waste Parthenium hysterophorous by chemical activation using concentrated H2SO4 at 130+/-5 degrees C. The prepared activated carbon was characterized and was found as an effective adsorbent material. In order to test the efficacy of parthenium based activated carbon (PAC), batch experiments were performed to carryout the adsorption studies on PAC for the removal of highly toxic pollutant p-cresol from aqueous solution. The p-cresol adsorption studies were also carried out on commercial grade activated carbon (AC) to facilitate comparison between the adsorption capabilities of PAC and AC. For PAC and AC, the predictive capabilities of two types of kinetic models and six types of adsorption equilibrium isotherm models were examined. The effect of pH of solution, adsorbent dose and initial p-cresol concentration on adsorption behaviour was investigated, as well. The adsorption on PAC and on AC was found to follow pseudo-first order kinetics with rate constant 0.0016 min(-1) and 0.0050 min(-1), respectively. The highest adsorptive capacity of PAC and AC for p-cresol solution was attained at pH 6.0. Further, as an adsorbent PAC was found to be as good as AC for removal of p-cresol upto a concentration of 500 mg/l in aqueous solution. Freundlich, Redlich-Peterson, and Fritz-Schlunder models were found to be appropriate isotherm models for PAC while Toth, Radke-Prausnitz and Fritz-Schlunder were suitable models for AC to remove p-cresol from aqueous solution.