Proteins embody epitopes that serve as their antigenic determinants. Epitopes occupy a central place in integrative biology, not to mention as targets for novel vaccine, pharmaceutical, and systems diagnostics development. The presence of T-cell and B-cell epitopes has been extensively studied due to their potential in synthetic vaccine design. However, reliable prediction of linear B-cell epitope remains a formidable challenge. Earlier studies have reported discrepancy in amino acid composition between the epitopes and non-epitopes. Hence, this study proposed and developed a novel amino acid composition-based feature descriptor, Dipeptide Deviation from Expected Mean (DDE), to distinguish the linear B-cell epitopes from non-epitopes effectively. In this study, for the first time, only exact linear B-cell epitopes and non-epitopes have been utilized for developing the prediction method, unlike the use of epitope-containing regions in earlier reports. To evaluate the performance of the DDE feature vector, models have been developed with two widely used machine-learning techniques Support Vector Machine and AdaBoost-Random Forest. Five-fold cross-validation performance of the proposed method with error-free dataset and dataset from other studies achieved an overall accuracy between nearly 61% and 73%, with balance between sensitivity and specificity metrics. Performance of the DDE feature vector was better (with accuracy difference of about 2% to 12%), in comparison to other amino acid-derived features on different datasets. This study reflects the efficiency of the DDE feature vector in enhancing the linear B-cell epitope prediction performance, compared to other feature representations. The proposed method is made as a stand-alone tool available freely for researchers, particularly for those interested in vaccine design and novel molecular target development for systems therapeutics and diagnostics: https://github.com/brsaran/LBEEP.

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Plants in nature may face a wide range of favorable or unfavorable biotic and abiotic factors during their life cycle. Any of these factors may cause stress in plants; therefore, they have to be more adaptable to stressful environments and must acquire greater response to different stresses. The objective of this study is to retrieve and arrange data from the literature in a standardized electronic format for the development of information resources on potential stress responsive genes in Arabidopsis thaliana. This provides a powerful mean for manipulation, comparison, search, and retrieval of records describing the nature of various stress responsive genes in Arabidopsis thaliana. The database is based exclusively on published stress tolerance genes associated with plants.

Visceral leishmaniasis is characterized by mixed production of Th1/2 cytokines and the disease is established by an enhanced level of Th2 cytokine. CD4+ T cells are main cell type which produces Th1/2 cytokine in the host upon Leishmania infection. However, the regulatory mechanism for Th1/2 production is not well understood. In this study, we co-cultured mice CD4+ T cells with Leishmania donovani infected and uninfected macrophage for the identification of dysregulated miRNAs in CD4+ T cells by next-generation sequencing. Here, we identified 604 and 613 known miRNAs in CD4+ T cells in control and infected samples respectively and a total of only 503 miRNAs were common in both groups. The expression analysis revealed that 112 miRNAs were up and 96 were down-regulated in infected groups, compared to uninfected control. Nineteen up-regulated and 17 down-regulated miRNAs were statistically significant (p < 0.05), which were validated by qPCR. Further, using insilco approach, we identified the gene targets of significant miRNAs on the basis of CD4+ T cell biology. Eleven up-regulated miRNAs and 9 down-regulated miRNAs were associated with the cellular immune responses and Th1/2 dichotomy upon Leishmania donovani infection. The up-regulated miRNAs targeted transcription factors that promote differentiation of CD4+ T cells towards Th1 phenotype. While down-regulated miRNAs targeted the transcription factors that facilitate differentiation of CD4+ T cells towards Th2 populations. The GO and pathway enrichment analysis also showed that the identified miRNAs target the pathway and genes related to CD4+ T cell biology which plays important role in Leishmania donovani infection.