Putative Drug and Vaccine Target Identification in Leishmania donovani Membrane Proteins Using Naïve Bayes Probabilistic Classifier

In silico and immunoinformatics based multiepitope subunit vaccine design for protection against visceral leishmaniasis

Visceral leishmaniasis (VL) is a vector-borne neglected tropical protozoan disease with high fatality and no certified vaccine. Conventional vaccine preparation is challenging and tedious. Here in this work, we created a global multiepitope subunit vaccination against VL utilizing innovative immunoinformatics technique based on the extensively conserved epitopic regions of the PrimPol protein of Leishmania donovani consisting of four subunits which were analyzed and studied, out of which DNA primase large subunit and DNA polymerase α subunit B were evaluated as antigens by Vaxijen 2.0. The multiepitope vaccine design includes a single adjuvant β-defensins, eight CTL epitopes, eight HTL epitopes, seven linear BCL epitopes and one discontinuous BCL epitope to induce innate, cellular and humoral immune responses against VL. The Expasy ProtParam tool characterized the physiochemical parameters of the vaccine. At the same time, SOLpro evaluated our vaccine constructs to be soluble upon expression. We also modeled the stable tertiary structure of our vaccine construct through Robetta modeling for molecular docking studies with toll-like receptor proteins through HADDOCK 2.4. Simulations based on molecular dynamics revealed an intact vaccine and TLR8 complex, supporting our vaccine design's immunogenicity. Also, the immune simulation of our vaccine by the C-ImmSim server demonstrated the potency of the multiepitope vaccine construct to induce proper immune response for host defense. Codon optimization and in silico cloning of our vaccine further assured high expression. The outcomes of our study on multiepitope vaccine design significantly produced a potential candidate against VL and can potentially eradicate the disease in the future after clinical investigations.Communicated by Ramaswamy H. Sarma.

Deciphering Cathepsin K inhibitors: a combined QSAR, docking and MD simulation based machine learning approaches for drug design

Cathepsin K (CatK), a lysosomal cysteine protease, contributes to skeletal abnormalities, heart diseases, lung inflammation, and central nervous system and immune disorders. Currently, CatK inhibitors are associated with severe adverse effects, therefore limiting their clinical utility. This study focuses on exploring quantitative structure-activity relationships (QSAR) on a dataset of CatK inhibitors (1804) compiled from the ChEMBL database to predict the inhibitory activities. After data cleaning and pre-processing, a total of 1568 structures were selected for exploratory data analysis which revealed physicochemical properties, distributions and statistical significance between the two groups of inhibitors. PubChem fingerprinting with 11 different machine-learning classification models was computed. The comparative analysis showed the ET model performed well with accuracy values for the training set (0.999), cross-validation (0.970) and test set (0.977) in line with OECD guidelines. Moreover, to gain structural insights on the origin of CatK inhibition, 15 diverse molecules were selected for molecular docking. The CatK inhibitors (1 and 2) exhibited strong binding energies of -8.3 and -7.2 kcal/mol, respectively. MD simulation (300 ns) showed strong structural stability, flexibility and interactions in selected complexes. This synergy between QSAR, docking, MD simulation and machine learning models strengthen our evidence for developing novel and resilient CatK inhibitors.

Immunoinformatics Approach to Design a Novel Subunit Vaccine Against Visceral Leishmaniasis

Visceral leishmaniasis (VL) infection is mostly caused by Leishmania donovani and affects countries worldwide. Despite the need for a safe and effective vaccine against leishmaniasis due to the increased drug resistance, however, no vaccine has yet been licensed for clinical use. This study revolves around the immunoinformatics approach to design a multi-epitope vaccine against VL infection. In this case, the proteome of L. donovani has been investigated, and three host non-homologous and antigenic extracellular secretory proteins have been identified as potential vaccine candidates with low transmembrane helices (≤ 1). The multi-epitope subunit vaccine construct consists of T-cell (cytotoxic T-lymphocyte (CTL) and helper T-lymphocyte (HTL)) epitopes accompanied by appropriate adjuvant and linkers. A 372-amino acid vaccine construct has been established with specific characteristics, such as soluble, stable, antigenic, non-allergenic, non-toxic, and non-host homologous. Besides, the tertiary structure of the designed vaccine was modeled and validated. Also, the stability and affinity of the vaccine- TLR4 complex were confirmed by using molecular docking and molecular dynamics (MD) simulation. In addition, in silico immunization assay showed the efficiency of this candidate vaccine to stimulate an effective immune response. Furthermore, the refined vaccine was optimized and cloned in the pET28a (+) vector, and its successful expression was confirmed virtually. However, the experimental validation is required to verify the multi-epitope vaccine efficacy against VL infection.

Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences

Advances in high-throughput techniques lead to evolving a large number of unknown protein sequences (UPS). Functional characterization of UPS is significant for the investigation of disease symptoms and drug repositioning. Protein subcellular localization is imperative for the functional characterization of protein sequences. Diverse techniques are used on protein sequences for feature extraction. However, many times a single feature extraction technique leads to poor prediction performance. In this paper, two feature augmentations are described through sequence induced, physicochemical, and evolutionary information of the amino acid residues. While augmented features preserve the sequence-order-information and protein-residue-properties. Two bacterial protein datasets Gram-Positive (G +) and Gram-Negative (G-) are utilized for the experimental work. After performing essential preprocessing on protein datasets, two sets of feature vectors are obtained. These feature vectors are used separately to train the different individual and ensembles such as decision tree (C 4.5), k-nearest neighbor (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), support vector machine (SVM), AdaBoost, gradient boosting machine (GBM), and random forest (RF) with fivefold cross-validation. Prediction results of the model demonstrate that overall accuracy reported by C4.5 is highest 99.57% on G + and 97.47% on G- datasets with known protein sequences. Similarly, for the UPS overall accuracy of G + is 85.17% with SVM and 82.45% with G- dataset using MLP.

IFN-γ+ CD4+T cell-driven prophylactic potential of recombinant LDBPK_252400 hypothetical protein of Leishmania donovani against visceral leishmaniasis

Visceral leishmaniasis (VL) is a potentially fatal parasitic disease causing high morbidity and mortality in developing countries. Vaccination is considered the most effective and powerful tool for blocking transmission and control of diseases. However, no vaccine is available so far in the market for humans. In the present study, we characterized the hypothetical protein LDBPK_252400 of Leishmania donovani (LdHyP) and explored its prophylactic behavior as a potential vaccine candidate against VL. We found reduced hepato-splenomegaly along with more than 50% parasite reduction in spleen and liver after vaccination in mice. Protection in vaccinated mice after the antigen challenge correlated with the stimulation of antigen specific IFN-γ expressing CD4⁺T cell (~4.6 fold) and CD8⁺T cells (~2.1 fold) in vaccinated mice in compared to infected mice, even after 2-3 months of immunization. Importantly, antigen-mediated humoral immunity correlated with high antigen specific IgG2/IgG1 responses in vaccinated mice. In vitro re-stimulation of splenocytes with LdHyP enhances the expression of TNF-α, IFN-γ, IL-12 and IL-10 cytokines along with lower IL-4 cytokine and IL-10/IFN-γ ratio in vaccinated mice. Importantly, we observed ~3.5 fold high NO production through activated macrophages validates antigen mediated cellular immunity induction, which is critical in controlling infection progression. These findings suggest that immunization with LdHyP mount a very robust immunity (from IL-10 towards TFN-γ mediated responses) against L. donovani infection and could be explored further as a putative vaccine candidate against VL.

Design of a multi-epitope subunit vaccine for immune-protection against Leishmania parasite

Visceral Leishmaniasis (VL) is an insect-borne neglected disease caused by the protozoan parasite Leishmania donovani. In the absence of a commercial vaccine against VL, chemotherapy is currently the only option used for the treatment of VL. Vaccination has been considered as the most effective and powerful tool for complete eradication and control of infectious diseases. In this study, we aimed to design a peptide-based vaccine against L. donovani using immuno-bioinformatic tools. We identified 6 HTL, 18 CTL, and 25 B-cell epitopes from three hypothetical membrane proteins of L. donovani. All these epitopes were used to make a vaccine construct along with linkers. An adjuvant was also added at the N-terminal to enhance its immunogenicity. After that, we checked the quality of this vaccine construct and found that it is nontoxic, nonallergic, and thermally stable. A 3D structure of the vaccine construct was also generated by homology modeling to evaluate its interaction with innate immune receptors (TLR). Molecular docking was performed, which confirmed its binding with a toll-like receptor-2 (TLR-2). The stability of vaccine-TLR-2 complex and underlying interactions were evaluated using molecular dynamic simulation. Lastly, we carried out in silico cloning to check the expression of the final designed vaccine. The designed vaccine construct needs further experimental and clinical investigations to develop it as a safe and effective vaccine against VL infection.

Insights on a putative aminoacyl-tRNA-protein transferase of Leishmania major

The N-end rule pathway leads to regulated proteolysis as an adaptive response to external stress and is ubiquitous from bacteria to mammals. In this study, we investigated a gene coding for a putative core enzyme of this post-translational regulatory pathway in Leishmania major, which may be crucial during cytodifferentiation and the environment adaptive responses of the parasite. Leucyl, phenylalanyl-tRNA protein transferase and arginyl-tRNA protein transferase are key components of this pathway in E. coli and eukaryotes, respectively. They catalyze the specific conjugation of leucine, phenylalanine or arginine to proteins containing exposed N-terminal amino acid residues, which are recognized by the machinery for the targeted proteolysis. Here, we characterized a conserved hypothetical protein coded by the LmjF.21.0725 gene in L. major. In silico analysis suggests that the LmjF.21.0725 protein is highly conserved among species of Leishmania and might belong to the Acyl CoA-N-acyltransferases (NAT) superfamily of proteins. Immunofluorescence cell imaging indicates that the cytosolic localization of the studied protein and the endogenous levels of the protein in promastigotes are barely detectable by western blotting assay. The knockout of the two alleles of LmjF.21.0725 by homologous recombination was only possible in the heterozygous transfectant expressing LmjF.21.0725 as a transgene from a plasmid. Moreover, the kinetics of loss of the plasmid in the absence of drug pressure suggests that maintenance of the gene is essential for promastigote survival. Here, evidence is provided that this putative aminoacyl tRNA-protein transferase is essential for parasite survival. The enzyme activity and corresponding post-translational regulatory pathway are yet to be investigated.

Rough set method accurately predicts unknown protein class/family of Leishmania donovani membrane proteome

Leishmania donovani is the primary cause of a fatal disease visceral leishmaniasis (VL) in East Africa and in the Indian subcontinent. Human beings are the only known reservoir of L. donovani and due to the emergence and the spread of drug resistance control for this disease is become worse. Therefore, identification of novel drug target is very important to develop new drug and combat drug resistance issue. Experimental determination of target is costly and time-consuming, hence it is necessary to first identify the efficient target with the accurate mathematical method and then further go for in vitro/in vivo study. Earlier we have predicted the role of protein in term of the target with Naïve Bayes probabilistic classifier on the proteins identified in our L. donovani membrane proteomics study. This time we have used alternative and the popular method named as a Rough Set method (an important part of soft computing method relevance in many real-world applications) and tried to re-visit/validate our earlier findings of L. donovani membrane proteomics and additionally decipher the unknown class/family of membrane proteins as known one. Comparing this result with other classifiers (NB, SVM, RF, C4.5 decision tree) Rough Set method has outperformed and we found the accuracy was 89.28%. This study further validates our previous finding strongly and predicts the class/family of unknown proteins which are very important for the identification and selection toward some novel drug target (still unexplored) and ultimately move in the direction of development of effective antileishmanials.