Control of the expression and compartmentalization of (sigma)G activity during sporulation of Bacillus subtilis by regulators of (sigma)F and (sigma)E

During formation of spores by Bacillus subtilis the RNA polymerase factor sigma(G) ordinarily becomes active during spore formation exclusively in the prespore upon completion of engulfment of the prespore by the mother cell. Formation and activation of sigma(G) ordinarily requires prior activity of sigma(F) in the prespore and sigma(E) in the mother cell. Here we report that in spoIIA mutants lacking both sigma(F) and the anti-sigma factor SpoIIAB and in which sigma(E) is not active, sigma(G) nevertheless becomes active. Further, its activity is largely confined to the mother cell. Thus, there is a switch in the location of sigma(G) activity from prespore to mother cell. Factors contributing to the mother cell location are inferred to be read-through of spoIIIG, the structural gene for sigma(G), from the upstream spoIIG locus and the absence of SpoIIAB, which can act in the mother cell as an anti-sigma factor to sigma(G). When the spoIIIG locus was moved away from spoIIG to the distal amyE locus, sigma(G) became active earlier in sporulation in spoIIA deletion mutants, and the sporulation septum was not formed, suggesting that premature sigma(G) activation can block septum formation. We report a previously unrecognized control in which SpoIIGA can prevent the appearance of sigma(G) activity, and pro-sigma(E) (but not sigma(E)) can counteract this effect of SpoIIGA. We find that in strains lacking sigma(F) and SpoIIAB and engineered to produce active sigma(E) in the mother cell without the need for SpoIIGA, sigma(G) also becomes active in the mother cell.     

Apoptosis of lymphocytes in the presence of Cr(V) complexes: role in Cr(VI)-induced toxicity

Cr(VI) compounds have been declared as a potent occupational carcinogen by IARC (1990) through epidemiological studies among workers in chrome plating, stainless-steel, and pigment industries. Studies relating to the role of intermediate oxidation states such as Cr(V) and Cr(IV) in Cr(VI)-induced carcinogenicity are gaining importance. In this study, issues relating to toxicity elicited by Cr(V) have been addressed and comparisons made with those relating to Cr(VI) employing human peripheral blood lymphocytes. Lymphocytes have been isolated from heparinized blood by Ficoll-Hypaque density gradient centrifugation and exposed to Cr(V) complexes viz. sodium bis(2-ethyl-2-hydroxybutyrato)oxochromate(V), Na[Cr(V)O(ehba)(2)], 1 and sodium bis(2-hydroxy-2-methylbutyrato)oxochromate(V), Na[Cr(V)O(hmba)(2)], 2 and Cr(VI). The phytohemagglutinin (PHA)-induced proliferation of lymphocytes has been found to be inhibited by the two complexes of Cr(V) and chromate Cr(VI) in a time- and concentration-dependent manner. Viability of cells decreases in the presence of Cr(V). Apoptosis appears to be the mode of cell death in the presence of both Cr(V) and Cr(VI). Pretreatment of cells with antioxidants before exposure to chromium(V) complexes reverse apoptosis partially. Possibility for the formation and implication of reactive oxygen species in Cr(V)-induced apoptosis of human lymphocyte cells has been indicated in this investigation. The intermediates of Cr(V) and radical species in the biotoxic pathways elicited by Cr(VI) seems feasible.     

RNABindRPlus: A Predictor that Combines Machine Learning and Sequence Homology-Based Methods to Improve the Reliability of Predicted RNA-Binding Residues in Proteins

Protein-RNA interactions are central to essential cellular processes such as protein synthesis and regulation of gene expression and play roles in human infectious and genetic diseases. Reliable identification of protein-RNA interfaces is critical for understanding the structural bases and functional implications of such interactions and for developing effective approaches to rational drug design. Sequence-based computational methods offer a viable, cost-effective way to identify putative RNA-binding residues in RNA-binding proteins. Here we report two novel approaches: (i) HomPRIP, a sequence homology-based method for predicting RNA-binding sites in proteins; (ii) RNABindRPlus, a new method that combines predictions from HomPRIP with those from an optimized Support Vector Machine (SVM) classifier trained on a benchmark dataset of 198 RNA-binding proteins. Although highly reliable, HomPRIP cannot make predictions for the unaligned parts of query proteins and its coverage is limited by the availability of close sequence homologs of the query protein with experimentally determined RNA-binding sites. RNABindRPlus overcomes these limitations. We compared the performance of HomPRIP and RNABindRPlus with that of several state-of-the-art predictors on two test sets, RB44 and RB111. On a subset of proteins for which homologs with experimentally determined interfaces could be reliably identified, HomPRIP outperformed all other methods achieving an MCC of 0.63 on RB44 and 0.83 on RB111. RNABindRPlus was able to predict RNA-binding residues of all proteins in both test sets, achieving an MCC of 0.55 and 0.37, respectively, and outperforming all other methods, including those that make use of structure-derived features of proteins. More importantly, RNABindRPlus outperforms all other methods for any choice of tradeoff between precision and recall. An important advantage of both HomPRIP and RNABindRPlus is that they rely on readily available sequence and sequence-derived features of RNA-binding proteins. A webserver implementation of both methods is freely available at http://einstein.cs.iastate.edu/RNABindRPlus/.     

Protein ligation: an enabling technology for the biophysical analysis of proteins

Biophysical techniques such as fluorescence spectroscopy and nuclear magnetic resonance (NMR) spectroscopy provide a window into the inner workings of proteins. These approaches make use of probes that can either be naturally present within the protein or introduced through a labeling procedure. In general, the more control one has over the type, location and number of probes in a protein, then the more information one can extract from a given biophysical analysis. Recently, two related approaches have emerged that allow proteins to be labeled with a broad range of physical probes. Expressed protein ligation (EPL) and protein trans-splicing (PTS) are both intein-based approaches that permit the assembly of a protein from smaller synthetic and/or recombinant pieces. Here we provide some guidelines for the use of EPL and PTS, and highlight how the dovetailing of these new protein chemistry methods with standard biophysical techniques has improved our ability to interrogate protein function, structure and folding.     

Solution structure and folding characteristics of the C-terminal SH3 domain of c-Crk-II

Crk-II is a signaling adaptor protein that is involved in many cellular processes including apoptosis, proliferation, and differentiation. It has a modular domain architecture consisting of an Src homology 2 domain (SH2) followed by two Src homology 3 (SH3) domains. The structures and ligand-binding properties of the SH2 and the middle SH3 domains are well-characterized. Several studies suggest that the C-terminal SH3 domain plays an important regulatory role in the protein; however, no structural information is available on this domain, and relatively little is known about its binding partners. In the current work, we have solved the solution NMR structure of the C-terminal SH3 domain. The domain adopts the standard SH3 fold comprising a five-stranded beta barrel. In agreement with alignment and modeling studies, the structure indicates that the canonical-binding surface of the SH3 domain is unusually polar and suggests that this domain may not bind typical PXXP ligands or that it may bind them with reduced affinity. Thermodynamic and kinetic studies show that the domain folds in a reversible two-state manner and that the stability of the fold is similar to that observed for other SH3 domains. These studies offer some insight into the likely structural and thermodynamic consequences of point mutations in the cSH3 domain that are known to deregulate Crk-II function. Our results set the stage for a better understanding the role of the cSH3 domain in the context of the full-length protein.     

Structure-based virtual screening,molecular docking,ADMET and molecular simulations to develop benzoxaborole analogs as potential inhibitor against Leishmania donovani trypanothione reductase

Visceral leishmaniasis (VL) is the most fatal form of leishmaniasis and it affects 70 countries worldwide. Increasing drug resistant for antileishmanial drugs such as miltefosine, sodium stibogluconate and pentamidine has been reported in the VL endemic region. Amphotericin B has shown potential antileishmanial activity in different formulations but its cost of treatment and associated nephrotoxicity have limited its use by affected people living in the endemic zone. To control the VL infection in the affected countries, it is necessary to develop new antileishmanial compounds with high efficacy and negligible toxicity. Computer aided programs such as binding free energy estimation; ADMET prediction and molecular dynamics simulation can be used to investigate novel antileishmanial molecules in shorter duration. To develop antileishmanial lead molecule, we performed standard precision (SP) docking for 1160 benzoxaborole analogs along with reference inhibitors against trypanothione reductase of Leishmania parasite. Furthermore, extra precision (XP) docking, ADMET prediction, prime MM-GBSA was conducted over 115 ligands, showing better docking score than reference inhibitors to get potential antileishmanial compounds. Simultaneously, area under the curve (AUC) was estimated using ROC plot to validate the SP and XP docking protocol. Later on, two benzoxaborole analogs with best MM-GBSA ΔG-bind were subjected to molecular simulation and docking confirmation to ensure the ligand interaction with TR. The presented drug discovery based on computational study confirms that BOB27 can be used as a potential drug candidate and warrants further experimental investigation to fight against VL in endemic areas.     

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

ABSTRACT: BACKGROUND: RNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition 'code' that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction. RESULTS: We provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naive Bayes (NB) and Support Vector Machine (SVM)) using three different data representations in which features are encoded using either sequence- or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix (PSSM)-based representation (PSSMSeq) outperform those that use an amino acid identity based representation (IDSeq) or a smoothed PSSM (SmoPSSMSeq); (ii) Structure-based classifiers that use smoothed PSSM representation (SmoPSSMStr) outperform those that use PSSM (PSSMStr) as well as sequence identity based representation (IDStr). PSSMSeq classifiers, when tested on an independent test set of 44 proteins, achieve performance that is comparable to that of three state-of-the-art structure-based predictors (including those that exploit geometric features) in terms of Matthews Correlation Coefficient (MCC), although the structure-based methods achieve substantially higher Specificity (albeit at the expense of Sensitivity) compared to sequence-based methods. We also find that the expected performance of the classifiers on a residue level can be markedly different from that on a protein level. Our experiments show that the classifiers trained on three different non-redundant protein-RNA interface datasets achieve comparable cross-validation performance. However, we find that the results are significantly affected by differences in the distance threshold used to define interface residues. CONCLUSIONS: Our results demonstrate that protein-RNA interface residue predictors that use a PSSM-based encoding of sequence windows outperform classifiers that use other encodings of sequence windows. While structure-based methods that exploit geometric features can yield significant increases in the Specificity of protein-RNA interface residue predictions, such increases are offset by decreases in Sensitivity. These results underscore the importance of comparing alternative methods using rigorous statistical procedures, multiple performance measures, and datasets that are constructed based on several alternative definitions of interface residues and redundancy cutoffs as well as including evaluations on independent test sets into the comparisons.     

Nitrate associated health risks from groundwater of Kadava River Basin Nashik,Maharashtra, India

Abstract

The present study was carried out to evaluate the nitrate contamination in groundwater and ascertain the associated health risks to rural populations in the agricultural area of the Kadava River basin. A total of (80) eighty representative samples from rural habitat located in agricultural fields were collected during pre- and postmonsoon seasons of 2011, which are mainly used for drinking and irrigation. The chemical results confirm that, 52.5 and 65% groundwater samples from pre- and postmonsoon season are unfit for drinking because of high nitrate contents exceeding the limit of nitrate (>45?mg L^?1) recommended by the BIS. The oral and dermal exposure pathways were calculated for different age group based on US EPA and ICMR standards. HQ₁ is much higher than the critical limit of 1 which increases the risk from 92.5 to 95% groundwater samples, while value of HQ₂ is far below to the critical value of 1; hence, all age groups free from risk. THQ values depicts that, children were at greater risk followed by infants and adults. Therefore, it is immensely important to regulate the use of nitrogen complex fertilizer and groundwater management practices should be implemented to prevent the associated risks to human health.