Comparative transcriptome analysis elucidates positive physiological effects of foliar application of pyraclostrobin on tomato (Solanum lycopersicum L.).

Strobilurins, including pyraclostrobin have frequently been reported showing positive physiological effects in various agricultural crops apart from fungicidal activity. Present study elucidates comparative transcriptome analysis of control and pyraclostrobin treated tomato leaf and identifies metabolic pathways and key genes responsible for positive effects of pyraclostrobin on tomato. Pair-end raw reads, generated by Illumina Hi-seq platform were pre-processed and good quality reads were mapped onto tomato reference genome using HISAT2 alignment programme. Transcript assembly and quantification were performed using StringTie assembler. Differential Gene Expression analysis by DESeq2 identified 1,952 upregulated genes including genes encoding pathogenesis related proteins and 835 downregulated genes. RT-PCR study showed increase in expression of RBCs (2.5-fold), GA20o (3-fold), and NR (1.4-fold) genes, which are the key genes of photosynthesis, gibberellic acid synthesis, and nitrogen assimilation pathways respectively identified in KEGG pathway analysis. Pyraclostrobin treated plants showed 1.6-folds increase in plant height, 3.3-folds increase in number of leaves, and 2.8-folds increase in number of flowers. Total protein content increased 1.7, 1.4, 1.2, 1.2, and 1.4 folds at 1 day after application (DAA), 4DAA, 7DAA, 10DAA, and 13DAA respectively in treated plants. Moreover, content of phenol also increased 1.14, 1.5, 2.4, and 1.5 folds in 4DAA, 7DAA, 10DAA, and 13DAA respectively. Nitrate reductase activity increased 2-fold, 1.8-fold, 1.5-fold and 1.15-fold in 1DAA, 7DAA, 10DAA and 13DAA respectively. Carbohydrate decreased in treated plants up to 7DAA. The present study is the first report of transcriptome analysis elucidating positive physiological effects of strobilurin on plant. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-022-01191-7.

ELIXIR and Toxicology: a community in development.

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.

Improving enzyme regulatory protein classification by means of SVM-RFE feature selection.

Enzyme regulation proteins are very important due to their involvement in many biological processes that sustain life. The complexity of these proteins, the impossibility of identifying direct quantification molecular properties associated with the regulation of enzymatic activities, and their structural diversity creates the necessity for new theoretical methods that can predict the enzyme regulatory function of new proteins. The current work presents the first classification model that predicts protein enzyme regulators using the Markov mean properties. These protein descriptors encode the topological information of the amino acid into contact networks based on amino acid distances and physicochemical properties. MInD-Prot software calculated these molecular descriptors for 2415 protein chains (350 enzyme regulators) using five atom physicochemical properties (Mulliken electronegativity, Kang-Jhon polarizability, vdW area, atom contribution to P) and the protein 3D regions. The best classification models to predict enzyme regulators have been obtained with machine learning algorithms from Weka using 18 features. K* has been demonstrated to be the most accurate algorithm for this protein function classification. Wrapper Subset Evaluator and SVM-RFE approaches were used to perform a feature subset selection with the best results obtained from SVM-RFE. Classification performance employing all the available features can be reached using only the 8 most relevant features selected by SVM-RFE. Thus, the current work has demonstrated the possibility of predicting new molecular targets involved in enzyme regulation using fast theoretical algorithms.

Metabolism evolution based on network degrees of orthologous enzymes.

The evolution of orthologous proteins opens a new era of research where the concepts of orthology and paralogy have become more and more substantial, as the whole-genome comparison allows their identification in complete genomes. Functional specificity of proteins is understood to be conserved among orthologs but it shows much more variability among paralogs. We used this laying claim to identify inter-species interactions based on orthologous protein networks which are crucial for understanding the evolution of orthologous proteins. We analyzed six classes of enzymatic protein sequence data using the node degrees of orthologous proteins. The results demonstrated the evolutionary importance of the fatty acid syntheses and the photosynthetic system in algae. Methods which have successfully exploited network structure at many different levels of detail are a cornerstone of systems biology.

ENZPRED-enzymatic protein class predicting by machine learning.

Recent times have seen flooding of biological data into the scientific community. Due to increase in large amounts of data from genome and other sequencing projects become available, being diverted on to Insilco approach for data collection and prediction has become a priority also progresses in sequencing technologies have found an exponential function rise in the number of newly found enzymes. Commonly, function of such enzymes is determined by experiments that can be time consuming and costly. As new approaches are needed to determine the functions of the proteins these genes encode. The protein parameters that can be used for an enzyme/ non-enzyme classification includes features of sequences like amino acid composition, dipeptide composition, grand Average of hydropathicity (GRAVY), probability of being in alpha helix, probability of being in beta sheet Probability of being in a turn. We show how large-scale computational analysis can help to address this challenge by help of java and support vector machine library. In this paper, a recently developed machine learning algorithm referred to as the svm library Learning Machine is used to classify protein sequences with six main classes of enzyme data downloaded from a public domain database. Comparative studies on different type of kernel methods like 1.radial basis function, 2.polynomial available in SVM library. Results show that RBF method take less time in training and give more accurate result then other kernel methods to also less training time compared to other kernel methods. The classification accuracy of RBF is also higher than various methods in respect of available sequences data.

Conotoxins: review and docking studies to determine potentials of conotoxin as an anticancer drug molecule.

It is known that potassium channels are important for cell proliferation. HERG, a potassium channel protein, is a transmembrane protein, which increases in concentration on the cell surface of cancer cells. Apart from cancer cells, this protein is found only in the brain & heart tissue, in very low number. The proliferation of cells in cancer is dependent on activation of this protein, and it has been noted that blocking of this protein with drug molecule, helps inhibit the proliferation of the cells further. The current work aims to study the binding potentials of κ-PVIIA, conotoxin isolated from Conus purpurascens venom with HERG K+ channel of tumor cells, where HERG mutation has been noted. The toxin under consideration i.e. κ-conotoxins-PVIIA (κ-PVIIA) is a 27 residue peptide. The docking studies suggest that the conotoxin binds stably to the HERG protein. The study shows that the peptide interacts with the charged extracellular unit of the HERG protein, i.e. the extracellular portion of the S5 domain named S5-P extracellular linker. Study of binding of toxins of similar origin, with normal potassium channels has been studied in silico. Further, wet laboratory work needs to be conducted for development of a drug molecule from this toxin, to treat some number of cancers.

Review on chemogenomics approach: interpreting antagonist activity of secreted frizzled-related protein 1 in glaucoma disease with in-silico docking.

Computer-aided drug discovery is a growing frontier in science. It covers different sub areas like chemoinformatics and chemogenomics. Chemogenomics is one of the emerging inter-disciplinary approaches in drug discovery, which combines conventional ligand based approach with biological information of drug targets. The main goal of this review is to check effective application of chemogenomics in understanding interactions between all possible ligands and their potential drug targets at molecular level. Recent studies revealed that increased expression of sFRP1an inhibitor of Wnt signalling pathway, seems to be responsible for Elevated Intracellular Pressure (IOP) in glaucoma patients. Glaucoma is a worldwide spread disease. Here, secreted frizzled-related protein-1 (sFRP1) has been used as a target protein. An important role of sFRP1, an antagonist of Wnt signalling pathway, has been found in regulating IOP. Wnt3a ligand protein and a natural compound from marine source Mycaperoxide H - have been used as ligands. In-silico docking of these ligands with sFRP family implies answers to many intricate queries in drug development field. Using above mentioned ligand-protein model in this study, application of chemogenomics has tried to explore the interaction of active site of proteins with the novel ligands. Henceforth, the present review will focus on predictive in-silico chemogenomic approaches with computer aided drug design could be used in drug design domain in identifying new targets in various diseases, in time and cost effective manner.

Revision of QSAR, docking, and molecular modeling studies of anti-influenza virus A (H1N1) drugs and targets: analysis of hemagglutinins 3D structure.

Recently WHO and NREVSS collaborating laboratories located in all 50 states, and Washington D.C reported that out of 3,588 specimens,164 were found positive for influenza type (i.e. 4.6%) and from these 164 specimens 162 (i.e. 98.8 %) were of influenza A H1N1 subtype. Comparative study of the past and current reports gives a general idea that the influenza activity deserves high attention from public health authorities in the U.S. In this connection, presently some groups are developing intensive computer-aided research in QSAR, Docking, Molecular Modeling and Drug Design, Sequence Analysis and Phylogenetic analysis of candidate compounds and/or targets; in order to advance in the treatment and/or prevention of this pandemic Flu. In this work, primarily we carry out a mini-review of the more important theoretical studies reported until now within this area, followed by the study of a specific type of target. Keeping in view the nature of this virus, we can conclude that there is always a need to find other target protein as inhibitor other than the existing one. So that this lethal pandemic flu can be treated and prevented further. Therefore, after Neuraminidase and M2 ion channels the surface protein that we can target in H1N1 strain is Hemagglutinins (HA). We use comparative modeling; which is one of the methods that can reliably generate a 3D model for HA protein. Multiple structures of this subtype of Influenza Virus are available at PDB, but we are focused on Influenza A (H1N1). Therefore, methodology of analysis mainly focuses on modeling the structure of this protein and, if possible, finding a probable active sites and inhibitors to it.