Effects of Environmental Pesticides on the Health of Rural Communities in the Malwa Region of Punjab,India: A Review

The Malwa region of Punjab, India, is facing an unprecedented crisis of environmental health linked to indiscriminate, excessive, and unsafe use of pesticides, fertilizers, and poor groundwater quality. The region has been described as India's “cancer capital” due to abnormally high number of cancer cases, which have increased 3-fold in the last 10 years. Studies of this region have also highlighted a sharp increase in many other pesticide-related diseases, such as mental retardation and reproductive disorders. The most affected individuals are the agricultural workers who are directly exposed to pesticides. The Malwa region of Punjab, India, is less than 15% of the total area of Punjab (only 0.5% of the total geographical area of India), but it consumes nearly 75% of the total pesticides used in Punjab. The high use of pesticides, along with environmental and social factors, is responsible for the high concentration of pesticide residues in the food chain of this region. Moreover, many banned and restricted pesticides are still in use in this region, warranting strict periodical health checkups and other interventions. The present review describes occupational, environmental, and social factors associated with pesticide use in the Malwa region of Punjab, India, and proposes some risk reduction interventions.     

Novel mononuclear η-pentamethylcyclopentadienyl complexes of platinum group metals bearing pyrazolylpyridazine ligands: Syntheses and spectral studies

Condensation of 3,6-dichloropyridazine with 3,5-dimethylpyrazole in 1:1 ratio yielded one side substituted pyrazolylpyridazine ligand 3-chloro-6-(3,5-dimethylpyrazolyl)pyridazine (L) while condensation of 3,6-dichloropyridazine with substituted pyrazoles in 1:2 ratio yielded both side substituted pyrazolylpyridazine ligands such as 3,6-bis(pyrazolyl)pyridazine (L1), 3,6-bis(3-methylpyrazolyl)pyridazine (L2) and 3,6-bis(3,5-dimethylpyrazolyl)pyridazine (L3). A new series of cationic mononuclear complexes of the type [(η⁵-Cp)M_a(L)(PPh₃)]PF₆, [(η⁵-Cp*)M_b(L)Cl]PF₆, [(η⁵-Cp*)Ru(L′)(PPh₃)]PF₆ and [(η⁵-Cp*)M_b(L′)Cl]⁺ (where M_a = Ru, Os; M_b = Rh, Ir and L′ = L1, L2, L3) bearing pyrazolylpyridazine and η⁵-cyclopentadienyl ligands are reported. The complexes have been completely characterized by spectral studies. The molecular structures of representative complexes have been determined by single crystal X-ray crystallography.     

Prediction of mitochondrial proteins using support vector machine and hidden Markov model

Mitochondria are considered as one of the core organelles of eukaryotic cells hence prediction of mitochondrial proteins is one of the major challenges in the field of genome annotation. This study describes a method, MitPred, developed for predicting mitochondrial proteins with high accuracy. The data set used in this study was obtained from Guda, C., Fahy, E. & Subramaniam, S. (2004) Bioinformatics 20, 1785-1794. First support vector machine-based modules/methods were developed using amino acid and dipeptide composition of proteins and achieved accuracy of 78.37 and 79.38%, respectively. The accuracy of prediction further improved to 83.74% when split amino acid composition (25 N-terminal, 25 C-terminal, and remaining residues) of proteins was used. Then BLAST search and support vector machine-based method were combined to get 88.22% accuracy. Finally we developed a hybrid approach that combined hidden Markov model profiles of domains (exclusively found in mitochondrial proteins) and the support vector machine-based method. We were able to predict mitochondrial protein with 100% specificity at a 56.36% sensitivity rate and with 80.50% specificity at 98.95% sensitivity. The method estimated 9.01, 6.35, 4.84, 3.95, and 4.25% of proteins as mitochondrial in Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, mouse, and human proteomes, respectively. MitPred was developed on the above hybrid approach.     

Prediction of C alpha-H...O and C alpha-H...pi interactions in proteins using recurrent neural network

In this study, an attempt has been made to develop a method for predicting weak hydrogen bonding interactions, namely, C alpha-H...O and C alpha-H...pi interactions in proteins using artificial neural network. Both standard feed-forward neural network (FNN) and recurrent neural networks (RNN) have been trained and tested using five-fold cross-validation on a non-homologous dataset of 2298 protein chains where no pair of sequences has more than 25% sequence identity. It has been found that the prediction accuracy varies with the separation distance between donor and acceptor residues. The maximum sensitivity achieved with RNN for C alpha-H...O is 51.2% when donor and acceptor residues are four residues apart (i.e. at delta D-A = 4) and for C alpha-H...pi is 82.1% at delta D-A = 3. The performance of RNN is increased by 1-3% for both types of interactions when PSIPRED predicted protein secondary structure is used. Overall, RNN performs better than feed-forward networks at all separation distances between donor-acceptor pair for both types of interactions. Based on the observations, a web server CHpredict (available at http://www.imtech.res.in/raghava/chpredict/) has been developed for predicting donor and acceptor residues in C alpha-H...O and C alpha-H...pi interactions in proteins.     

GlycoPP: a webserver for prediction of N- and O-glycosites in prokaryotic protein sequences

Glycosylation is one of the most abundant post-translational modifications (PTMs) required for various structure/function modulations of proteins in a living cell. Although elucidated recently in prokaryotes, this type of PTM is present across all three domains of life. In prokaryotes, two types of protein glycan linkages are more widespread namely, N- linked, where a glycan moiety is attached to the amide group of Asn, and O- linked, where a glycan moiety is attached to the hydroxyl group of Ser/Thr/Tyr. For their biologically ubiquitous nature, significance, and technology applications, the study of prokaryotic glycoproteins is a fast emerging area of research. Here we describe new Support Vector Machine (SVM) based algorithms (models) developed for predicting glycosylated-residues (glycosites) with high accuracy in prokaryotic protein sequences. The models are based on binary profile of patterns, composition profile of patterns, and position-specific scoring matrix profile of patterns as training features. The study employ an extensive dataset of 107 N-linked and 116 O-linked glycosites extracted from 59 experimentally characterized glycoproteins of prokaryotes. This dataset includes validated N-glycosites from phyla Crenarchaeota, Euryarchaeota (domain Archaea), Proteobacteria (domain Bacteria) and validated O-glycosites from phyla Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria (domain Bacteria). In view of the current understanding that glycosylation occurs on folded proteins in bacteria, hybrid models have been developed using information on predicted secondary structures and accessible surface area in various combinations with training features. Using these models, N-glycosites and O-glycosites could be predicted with an accuracy of 82.71% (MCC 0.65) and 73.71% (MCC 0.48), respectively. An evaluation of the best performing models with 28 independent prokaryotic glycoproteins confirms the suitability of these models in predicting N- and O-glycosites in potential glycoproteins from aforementioned organisms, with reasonably high confidence. A web server GlycoPP, implementing these models is available freely at http:/www.imtech.res.in/raghava/glycopp/.     

Genome sequence of the halotolerant bacterium Imtechella halotolerans K1T

We report the 3.087-Mb genome sequence of Imtechella halotolerans K1(T), isolated from an estuarine water sample collected from Kochi, Kerala, India. Strain K1 was recently reported as a novel genus of the family Flavobacteriaceae.     

Evaluation of Protein Dihedral Angle Prediction Methods

Tertiary structure prediction of a protein from its amino acid sequence is one of the major challenges in the field of bioinformatics. Hierarchical approach is one of the persuasive techniques used for predicting protein tertiary structure, especially in the absence of homologous protein structures. In hierarchical approach, intermediate states are predicted like secondary structure, dihedral angles, C^α-C^α distance bounds, etc. These intermediate states are used to restraint the protein backbone and assist its correct folding. In the recent years, several methods have been developed for predicting dihedral angles of a protein, but it is difficult to conclude which method is better than others. In this study, we benchmarked the performance of dihedral prediction methods ANGLOR and SPINE X on various datasets, including independent datasets. TANGLE dihedral prediction method was not benchmarked (due to unavailability of its standalone) and was compared with SPINE X and ANGLOR on only ANGLOR dataset on which TANGLE has reported its results. It was observed that SPINE X performed better than ANGLOR and TANGLE, especially in case of prediction of dihedral angles of glycine and proline residues. The analysis suggested that angle shifting was the foremost reason of better performance of SPINE X. We further evaluated the performance of the methods on independent ccPDB30 dataset and observed that SPINE X performed better than ANGLOR.     

Designing of highly effective complementary and mismatch siRNAs for silencing a gene

In past, numerous methods have been developed for predicting efficacy of short interfering RNA (siRNA). However these methods have been developed for predicting efficacy of fully complementary siRNA against a gene. Best of author's knowledge no method has been developed for predicting efficacy of mismatch siRNA against a gene. In this study, a systematic attempt has been made to identify highly effective complementary as well as mismatch siRNAs for silencing a gene.Support vector machine (SVM) based models have been developed for predicting efficacy of siRNAs using composition, binary and hybrid pattern siRNAs. We achieved maximum correlation 0.67 between predicted and actual efficacy of siRNAs using hybrid model. All models were trained and tested on a dataset of 2182 siRNAs and performance was evaluated using five-fold cross validation techniques. The performance of our method desiRm is comparable to other well-known methods. In this study, first time attempt has been made to design mutant siRNAs (mismatch siRNAs). In this approach we mutated a given siRNA on all possible sites/positions with all possible nucleotides. Efficacy of each mutated siRNA is predicted using our method desiRm. It is well known from literature that mismatches between siRNA and target affects the silencing efficacy. Thus we have incorporated the rules derived from base mismatches experimental data to find out over all efficacy of mutated or mismatch siRNAs. Finally we developed a webserver, desiRm (http://www.imtech.res.in/raghava/desirm/) for designing highly effective siRNA for silencing a gene. This tool will be helpful to design siRNA to degrade disease isoform of heterozygous single nucleotide polymorphism gene without depleting the wild type protein.     

PolysacDB: a database of microbial polysaccharide antigens and their antibodies

Vaccines based on microbial cell surface polysaccharides have long been considered as attractive means to control infectious diseases. To realize this goal, detailed systematic information about the antigenic polysaccharide is necessary. However, only a few databases that provide limited knowledge in this area are available. This paper describes PolysacDB, a manually curated database of antigenic polysaccharides. We collected and compiled comprehensive information from literature and web resources about antigenic polysaccharides of microbial origin. The current version of the database has 1,554 entries of 149 different antigenic polysaccharides from 347 different microbes. Each entry provides comprehensive information about an antigenic polysaccharide, i.e., its origin, function, protocols for its conjugation to carriers, antibodies produced, details of assay systems, specificities of antibodies, proposed epitopes involved and antibody utilities. For convenience to the user, we have integrated web interface for searching, advanced searching and browsing data in database. This database will be useful for researchers working on polysaccharide-based vaccines. It is freely available from the URL: http://crdd.osdd.net/raghava/polysacdb/.