Tuberculosis is one the oldest known affliction of mankind caused by the pathogenMycobacterium tuberculosis. Till date, there is no absolute single treatment available to deal with the pathogen, which has acquired a great potential to develop drug resistance rapidly. BCG is the only anti-tuberculosis vaccine available till date which displays limited global efficacy due to genetic variation and concurrent pathogen infections. Extracellular vesicles or exosomes vesicle (EVs) lie at the frontier cellular talk between pathogen and the host, and therefore play a significant role in establishing pathogenesis. In the present study, an in-silico approach has been adopted to construct a multi-epitope vaccine from selected immunogenic EVs proteins to elicit a cellular as well as a humoral immune response. Our designed vaccine has wide population coverage and can effectively compensate for the genetic variation among different populations. For maximum efficacy and minimum adverse effects possibilities the antigenic, non-allergenic and non-toxic B-cell, HTL and CTL epitopes from experimentally proven EVs proteins were selected for the vaccine construct. TLR4 agonist RpfE served as an adjuvant for the vaccine construct. The vaccine construct structure was modelled, refined and docked on TLR4 immune receptor. The designed vaccine construct displayed safe usage and exhibits a high probability to elicit the critical immune regulators, like B cells, T-cells and memory cells as displayed by the in-silico immunization assays. Therefore, it can be further corroborated using in vitro and in vivo assays to fulfil the global need for a more efficacious anti-tuberculosis vaccine.

Mycobacterium tuberculosis (M.tb) is a successful pathogen that can reside within the alveolar macrophages of the host and can survive in a latent stage. The pathogen has evolved and developed multiple strategies to resist the host immune responses. M.tb escapes from host macrophage through evasion or subversion of immune effector functions. M.tb genome codes for PE/PPE/PE_PGRS proteins, which are intrinsically disordered, redundant and antigenic in nature. These proteins perform multiple functions that intensify the virulence competence of M.tb majorly by modulating immune responses, thereby affecting immune mediated clearance of the pathogen. The highly repetitive, redundant and antigenic nature of PE/PPE/PE_PGRS proteins provide a critical edge over other M.tb proteins in terms of imparting a higher level of virulence and also as a decoy molecule that masks the effect of effector molecules, thereby modulating immuno-surveillance. An understanding of how these proteins subvert the host immunological machinery may add to the current knowledge about M.tb virulence and pathogenesis. This can help in redirecting our strategies for tackling M.tb infections.

The devastating impact of Tuberculosis (TB) has been a menace to mankind for decades. The World Health Organization (WHO) End TB Strategy aims to reduce TB mortality up to 95% and 90% of overall TB cases worldwide, by 2035. This incessant urge will be achieved with a breakthrough in either a new TB vaccine or novel drugs with higher efficacy. However, the development of novel drugs is a laborious process involving a timeline of almost 20–30 years with huge expenditure; on the other hand, repurposing previously approved drugs is a viable technique for overcoming current bottlenecks in the identification of new anti-TB agents. The present comprehensive review discusses the progress of almost all the repurposed drugs that have been identified to the present day (∼100) and are in the development or clinical testing phase against TB. We have also emphasized the efficacy of repurposed drugs in combination with already available frontline anti-TB medications along with the scope of future investigations. This study would provide the researchers a detailed overview of nearly all identified anti-TB repurposed drugs and may assist them in selecting the lead compounds for further in vivo/clinical research.

Alzheimer’s disease (AD) is a neurodegenerative disorder in which the death of brain cells takes place leading to loss of memory and decreased cognitive ability. AD is a leading cause of death worldwide and is progressive in nature with symptoms worsening over time. Machine learning–based computational predictive models based on 2D and 3D descriptors have been effective in identifying potential active compounds. However, the use of data from molecular dynamics (MD) trajectories for training machine learning models still needs to be explored. In the present study, descriptors have been extracted from the MD trajectories of caspase-8 ligand complexes to train models using artificial neural networks and random forest algorithms. Caspase-8 plays a key role in causing AD by cleaving amyloid precursor proteins during apoptosis leading to increased formation of the amyloid-beta peptide. A total of 43 ligands were docked using the glide module of Schrodinger software, and short MD simulations of 10 ns were performed for the calculation of MD descriptors. The MD descriptors were also combined with the 2D and 3D descriptors of chemical compounds, and individual descriptor based as well as combination models were generated. This study demonstrated that MD descriptors could be effectively used for the characterization of bioactive compounds along with lead prioritization and optimization.

Tuberculosis is a devastating disease, taking one human life every 20 seconds globally. We hypothesize that professional pathogens such as M.tb have acquired specific features that might assist in causing infection, persistence and transmissible pathology in their host. We have identified 121 methyltransferases (MTases) in the M.tb proteome, which use a variety of substrates – DNA, RNA, protein, intermediates of mycolic acid biosynthesis and other fatty acids – that are involved in cellular maintenance within the host. A comparative analysis of the proteome of the virulent strain H₃₇Rv and the avirulent strain H₃₇Ra identified 3 MTases, which displayed significant variations in terms of N-terminal extension/deletion and point mutations, possibly impacting various physicochemical properties. The cross-proteomic comparison of MTases of M.tb H₃₇Rv with 15 different Mycobacterium species revealed the acquisition of novel MTases in a MTB complex as a function of evolution. Phylogenetic analysis revealed that these newly acquired MTases showed common roots with certain extremophiles such as halophilic and acidophilic organisms. Our results establish an evolutionary relationship of M.tb with halotolerant organisms and also the role of MTases of M.tb in withstanding the host osmotic stress, thereby pointing to their likely role in pathogenesis, virulence and niche adaptation.

Predicting adverse drug reactions (ADRs) has become very important owing to the huge global health burden and failure of drugs. This indicates a need for prior prediction of probable ADRs in preclinical stages which can improve drug failures and reduce the time and cost of development thus providing efficient and safer therapeutic options for patients. Though several approaches have been put forward for in silico ADR prediction, there is still room for improvement. In the present work, we have used machine learning based approach for cardiovascular (CV) ADRs prediction by integrating different features of drugs, biological (drug transporters, targets and enzymes), chemical (substructure fingerprints) and phenotypic (therapeutic indications and other identified ADRs), and their two and three level combinations. To recognize quality and important features, we used minimum redundancy maximum relevance approach while synthetic minority over-sampling technique balancing method was used to introduce a balance in the training sets. This is a rigorous and comprehensive study which involved the generation of a total of 504 computational models for 36 CV ADRs using two state-of-the-art machine-learning algorithms: random forest and sequential minimization optimization. All the models had an accuracy of around 90% and the biological and chemical features models were more informative as compared to the models generated using chemical features. The results obtained demonstrated that the predictive models generated in the present study were highly accurate, and the phenotypic information of the drugs played the most important role in drug ADRs prediction. Furthermore, the results also showed that using the proposed method, different drugs properties can be combined to build computational predictive models which can effectively predict potential ADRs during early stages of drug development.