The search for novel antigens suitable for improved vaccines and diagnostic reagents against leptospirosis led to the identification of LigA and LigB. LigA and LigB expression were not detectable at the translation level but were detectable at the transcription level in leptospires grown in vitro. Lig genes were present in pathogenic serovars of Leptospira, but not in non-pathogenic Leptospira biflexa. The conserved and variable regions of LigA and LigB (Con, VarA and VarB) were cloned, expressed and purified as GST-fusion proteins. Purified recombinant LigA and LigB were evaluated for their diagnostic potential in a kinetic ELISA (KELA) using sera from vaccinated and microscopic agglutination test (MAT)-positive dogs. Sera from vaccinated dogs showed reactivity to whole-cell antigens of leptospires but did not show reactivity in the KELA assay with recombinant antigens, suggesting a lack of antibodies to Lig proteins in the vaccinated animals. The diagnostic potential of recombinant Lig antigens in the KELA assay was evaluated by using 67 serum samples with MAT >1600, which showed reactivity of 76, 41 and 35 % to rConA, rVarA and rVarB, respectively. These findings suggest that recombinant antigen to the conserved region of LigA and LigB can differentiate between vaccinated and naturally infected animals.

Background Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning. Method We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity. Results We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software. Conclusion This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem.

No abstract

No abstract

No abstract

No abstract

No abstract

Background: Peptidases are a group of enzymes which catalyzes the cleavage of peptide bonds. Around 2-3% of the whole genome codes for proteases and about one third of all known proteases are serine proteases which are divided into 13 clans and 40 families. They are involved in diverse physiological roles such as digestion, coagulation of blood, fibrinolysis, processing of proteins and prohormones, signaling pathways, complement fixation and have a vital role in immune defense system. Based on their functions, they can broadly be divided into two classes; GASPIDs (Granule Associated Serine Peptidases involved in Immune Defense System) and Non-GASPIDs. GASPIDs, in particular are involved in immune associated functions i.e. initiating apoptosis to kill virally infected and cancerous cells, cytokine modulation for generation of inflammatory responses and direct killing of pathogens through phagosomes. Methods: In this study, sequence-based characterization of these two types of serine proteases is performed. We first identified sequences by analyzing multiple online databases as well as by analyzing whole genomes of different species from different orthologous and nonorthologous species. Sequences were identified by devising a distinct criterion to differentiate GASPIDs from Non-GASPIDs. The translated version of these sequences were then subjected to feature extraction. Using these distinctive features, we differentiated GASPIDs from NonGASPIDs by applying multiple supervised machine learning models. Results and Conclusion: Our results show that, among the three classifiers used in this study, SVM classifier coupled with tripeptide as feature method, has shown the best accuracy in classification of sequences as GASPIDs and Non-GASPIDs.