Owing to their sessile nature, plants experience a variety of environmental stresses, but tolerance to these adverse conditions is a very complex phenomenon. Among all stresses, heat stress is the most important constraint that affects plant yield in rain-fed areas. To shed some light on candidate genes involved in heat stress, sequences potentially associated with heat shock resistance were retrieved and identified by in silico analysis using the public sequence database of various plants. A total of 30,000 EST sequences were mined and 24 putative ESTs associated with heat stress were picked up for further studies. In silico analysis revealed that all ESTs were linked with the HSP family. Gene Ontology (GO) analysis revealed that the deduced protein sequences of the heat-linked 24 ESTs were involved in various biological pathways regulating heat stress response. Hydropathy analysis revealed that all protein sequences were hydrophilic in nature. Based on the phylogenetic analysis, all HSP-related protein sequences were divided into seven groups. Analysis of cis-elements provides molecular evidence for the possible involvement of hydrophilic ESTs in the process of abiotic stress tolerance in sorghum. Based on these results, it was suggested that putative ESTs may play an important role in heat stress tolerance.

HIV-1 Protease (HIV-1 PR) enzymes are essential for accurate assembly and maturation of infectious HIV retroviruses. The significant role of HIV-1 protease in viral replication has made it a potential drug target. In the recent past, phytochemical Gallic Acid (GA) derivatives have been screened for protease inhibitor activity. The present work aims to design and evaluate potential GA-based HIV-1 PR phytoinhibitors by docking approach. The ligands were prepared by ChemDraw and docking was performed in HEX software. In this present study, one of the GA analogues (GA4) emerged as a potent drug candidate for HIV-1 PR inhibition, and docking results showed it to be comparable with anti-HIV drugs, darunavir and amprenavir. The GA4 derivative provided a lead for designing more effective HIV-1 PR inhibitors.

We describe an automatic segmentation method for polyproteins of the viruses belonging to the Potyviridae family. It uses machine learning techniques in order to predict the cleavage site which define the segments in which said polyproteins are cut in their process of functional maturation. The segmentation application is publicly available for use on a website and it can be accessed through the web service interface too. The prediction models have an average sensitivity of 0.79 and a Matthews correlation coefficient average of 0.23. This method is capable of predicting correctly (coinciding with previously published segmentation) the segmentation of sequences which come from Potyvirus and Rymovirus, genera. However accurate prediction capabilities are affected when faced with either atypical sequences or viruses belonging to less common genera in the Potyviridae family. Future work will focus on establishing greater flexibility in this sense.

Currently, internet is the best tool for distributed computing, which involves spreading of data geographically. But, retrieving information from huge data is critical and has no relevance unless it provides certain information. Prediction of missing associations can be viewed as fundamental problems in machine learning where the main objective is to determine decisions for the missing associations. Mathematical models such as naive Bayes structure, human composed network structure, Bayesian network modelling, etc., were developed to this end. But, it has certain limitations and failed to include uncertainties. Therefore, effort has been made to process inconsistencies in the data with the introduction of rough set theory. This paper uses two processes, pre-process and post-process, to predict the decisions for the missing associations in the attribute values. In preprocess, rough set is used to reduce the dimensionality, whereas neural network is used in postprocess to explore the decision for the missing associations. A real-life example is provided to show the viability of the proposed research.

Protein complexes are key molecular entities that perform a variety of essential cellular functions. The connectivity of proteins within a complex has been widely investigated with both experimental and computational techniques. We developed a computational approach to identify and characterise proteins that play a role in interconnecting complexes. We computed a measure of inter-complex centrality, the crossroad index, based on disjoint paths connecting proteins in distinct complexes and identified inter-complex hubs as proteins with a high value of the crossroad index. We applied the approach to a set of stable complexes in Saccharomyces cerevisiae and in Homo sapiens. Just as done for hubs, we evaluated the topological and biological properties of inter-complex hubs addressing the following questions. Do inter-complex hubs tend to be evolutionary conserved? What is the relation between crossroad index and essentiality? We found a good correlation between inter-complex hubs and both evolutionary conservation and essentiality.

Dengue viral attacks have been reported in various parts of India in recent years. In this paper we report on our studies of the characterisation and evolutionary aspects of gene sequences of the envelope glycoprotein of the prevalent Indian dengue virus type 1. Comparison with sequences from other countries shows that the envelope genes identified in India are closely related to strains from Malaysia. From the evolutionary point of view the envelope gene sequences of this dengue virus of India for past few years show that a marked mutational shift in the nucleotide sequences of the envelope gene have taken place from around the year 2000. Also, phylogenetic relationship with other three sera of dengue virus reported in India from 2005 shows that the dengue virus 1 is more closely related to dengue viruses 3 and 4 and relatively distantly to dengue virus 2.

Protein-Protein Interactions (PPI) play a crucial role in deciphering function besides identifying candidates. While the experimental analysis is often time consuming involving number of experiments like pulldown assays, they are not necessarily limiting the ability to detect novel protein interactors. In this work, we discuss the role and putative interactors of SNAI2, a slug protein which is involved in the development of cancer progression. The protein interactions have been deciphered by domain pair exclusion method which gives confidence to already precluded interaction pairs. Additionally, conservation patterns of the slug protein have also been analysed by estimating site-specific evolutionary rates at structural level. Based upon the computational analysis, we consider HNF4A could be a putative candidate to study zinc finger protein slug. We believe, this candidate study augmented with structural conservation will definitely provide novel insights into the design and discovery of new interactions for zinc finger class of proteins besides providing possible clues for discovery of various cancer types associated with this class of proteins.

In this work we described a bacterial open reading frame with two different directions of nucleotide usage biases in its two parts. The level of GC-content in third codon positions (3GC) is equal to 40.17 ± 0.22% during the most of the length of Corynebacterium diphtheriae spaC gene. However, in the 3'-end of the same gene (from codon #1600 to codon #1873) 3GC level is equal to 64.61 ± 0.91%. Using original methodology ('VVTAK Sliding window' and 'VVTAK VarInvar') we approved that there is an ongoing mutational AT-pressure during the most of the length of spaC gene (up to codon #1599), and there is an ongoing mutational G-pressure in the 3′-end of spaC. Intragenic promoters predicted by three different methods may be the cause of the differences in preferable types of nucleotide mutations in spaC parts because of their autonomous transcription.

Design of selective matrix metalloproteinases (MMPs) inhibitors is still a challenging task because of binding pocket similarities and flexibility among MMPs family. To overcome this issue we try to generate a (three-dimensional quantitative structure activity relationship) 3D-QSAR model that might reflect, at least in part, the differential properties of MMP-12 and MMP-13 active sites compared to each other. The different alignment rules were applied for CoMFA/CoMSIA model development. In an approach the best docked poses were followed by alignment based on their zinc binding group. As it was suggested by comparison of CoMSIA contour maps of MMP-12 with MMP-13, the ligand based approach can find more detailed features of specificity for MMPs that have similar highly flexible active sites, than solely analysis of available crystal structures. The residues Val(194), Leu(214) and Thr(220) of MMP-13 were suggested to be investigated for flexibility upon binding of different ligands.

A wide variety of human population is infected with Listeria monocytogenes, which causes listeriosis, a deadly disease with mortality rate of about 30%. The major hindrance to cure listeriosis is the unavailability of specific or selectable drug targets. At present, antibiotics used to cure the disease are not specific and insufficient to manage the disease efficiently. Therefore, in order to search specific drugs, here, we used interactome analysis to search specific drug targets which may provide novel templates for drug designing having better efficacy without any potential adverse effects. The complete genome of L. monocytogenes having 2846 proteins has been analysed. We found 11 proteins as putative drug targets. The sequence and interactome analyses revealed that 11 proteins are non-homologous to human, but essential for pathogen and hence may be considered as potential therapeutic targets.

A Probabilistic Neural Network (PNN) is a statistical algorithm and consists of a grouping of multi-class data. The conventional method of detection of DNA mutations by the human eye may not detect the minute variations in PCR-SSCP bands, which may lead to false positive or false negative results. The detection by photographic images may contain a blare (noise) caused during the time of photography; therefore, image processing techniques were used to reduce image noise. PCR-SSCP gels of T2DM patients (n = 100) and controls (n = 100) were initially photographed with equal ratio of pixels and later subjected to a two-stage analysis: feature extraction and PNN. The evaluation of the results was done by quality training and the accuracy was up to 95%, and the human eye analysis showed 80% mutation detection rate. This study proves to be very reliable and gives accurate and fast detection for mutation analysis in diabetes. This method could be extended for analysis in other human diseases.

In virology context, a particular virus may prevail in different forms of serotypes (as in the case of dengue 1-4 viral strains) with common and distinct genomic features. Finding such genomic details of a serogroup is useful in knowing related information for unique vaccine designs compatible for immunity across the viral diversity. For robust comparison of genomes of serovars of a virus in order to decide on their common and differential genomic details, proposed here is a set of sequence analyses exercised side-by-side via entropy, energetic and spectral-domain methods. Results obtained thereof with dengue viral serotypes, namely DEN1, DEN2, DEN3 and DEN4, are presented. Hence, inferences on distinct as well as common features extracted are annotated and indicated for possible vaccine design applications.

The naïve use of expression ratios in high-throughput biological studies can greatly limit analytical outcome especially when sample size is small. In the worst-case scenario, with only one reference and one test state each (often due to the severe lack of study material); such limitations make it difficult to perform statistically meaningful analysis. Workarounds include the single sample Z-test or through network inference. Here, we describe a complementary plot-based approach for analysing such extremely small sized ratio (ESSR) data - a generalisation of the Bland-Altman plot, which we shall refer to as the Dodeca-Panels. Included in this paper is an R implementation of the Dodeca-Panels method.

In a cDNA microarray experiment, the final measurement is intensity ratio at a spot in the microarray chip. The objective of the present study is to estimate the uncertainty associated with the final intensity ratio at each spot in cDNA microarray chips and also to explore the role of pixel intensity correlations in statistical inferences of gene expression levels. We estimate uncertainty at each spot using the theory of error propagation under two different situations: (1) when there is no correlation between pixel intensities and (2) when the pixel intensities are positively correlated. The inverses of these estimated uncertainties are used as weights in downstream analysis to test the significance of each gene. The analysis was verified on a data downloaded from the GEO database. Our study shows that the uncertainty and statistical inference of gene expression levels depend on correlation between pixel intensities within a spot.

BioInt, a biological programming application framework and interpreter, is an attempt to equip the researchers with seamless integration, efficient extraction and effortless analysis of the data from various biological databases and algorithms. Based on the type of biological data, algorithms and related functionalities, a biology-specific framework was developed which has nine modules. The modules are a compilation of numerous reusable BioADTs. This software ecosystem containing more than 450 biological objects underneath the interpreter makes it flexible, integrative and comprehensive. Similar to Python, BioInt eliminates the compilation and linking steps cutting the time significantly. The researcher can write the scripts using available BioADTs (following C++ syntax) and execute them interactively or use as a command line application. It has features that enable automation, extension of the framework with new/external BioADTs/libraries and deployment of complex work flows.