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.