Background 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. Methods 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. Results 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. Conclusions 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. Electronic supplementary material The online version of this article (10.1186/s12967-019-1918-z) contains supplementary material, which is available to authorized users.

Background Post-translational modification (PTM) is a biological process that alters proteins and is therefore involved in the regulation of various cellular activities and pathogenesis. Protein phosphorylation is an essential process and one of the most-studied PTMs: it occurs when a phosphate group is added to serine (Ser, S), threonine (Thr, T), or tyrosine (Tyr, Y) residue. Dysregulation of protein phosphorylation can lead to various diseases—most commonly neurological disorders, Alzheimer’s disease, and Parkinson’s disease—thus necessitating the prediction of S/T/Y residues that can be phosphorylated in an uncharacterized amino acid sequence. Despite a surplus of sequencing data, current experimental methods of PTM prediction are time-consuming, costly, and error-prone, so a number of computational methods have been proposed to replace them. However, phosphorylation prediction remains limited, owing to substrate specificity, performance, and the diversity of its features. Methods In the present study we propose machine-learning-based predictors that use the physicochemical, sequence, structural, and functional information of proteins to classify S/T/Y phosphorylation sites. Rigorous feature selection, the minimum redundancy/maximum relevance approach, and the symmetrical uncertainty method were employed to extract the most informative features to train the models. Results The RF and SVM models generated using diverse feature types in the present study were highly accurate as is evident from good values for different statistical measures. Moreover, independent test sets and benchmark validations indicated that the proposed method clearly outperformed the existing methods, demonstrating its ability to accurately predict protein phosphorylation. Conclusions The results obtained in the present work indicate that the proposed computational methodology can be effectively used for predicting putative phosphorylation sites further facilitating discovery of various biological processes mechanisms.

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Tuberculosis (TB) is a leading cause of death worldwide and its impact has intensified due to the emergence of multi drug-resistant (MDR) and extensively drug-resistant (XDR) tB strains. protein phosphorylation plays a vital role in the virulence of Mycobacterium tuberculosis (M.tb) mediated by protein kinases. protein tyrosine phosphatase A (MptpA) undergoes phosphorylation by a unique tyrosine-specific kinase, protein tyrosine kinase A (PtkA), identified in the M.tb genome. ptkA phosphorylates PtpA on the tyrosine residues at positions 128 and 129, thereby increasing PtpA activity and promoting pathogenicity of MptpA. In the present study, we performed an extensive investigation of the conformational behavior of the intrinsically disordered domain (iDD) of ptkA using replica exchange molecular dynamics simulations. Long-term molecular dynamics (MD) simulations were performed to elucidate the role of iDD on the catalytic activity of kinase core domain (KcD) of ptkA. This was followed by identification of the probable inhibitors of PtkA using drug repurposing to block the PtpA-PtkA interaction. The inhibitory role of IDD on KCD has already been established; however, various analyses conducted in the present study showed that iDD ptkA had a greater inhibitory effect on the catalytic activity of KcD ptkA in the presence of the drugs esculin and inosine pranobex. the binding of drugs to PtkA resulted in formation of stable complexes, indicating that these two drugs are potentially useful as inhibitors of M.tb. Mycobacterium tuberculosis (M.tb) is an infectious agent that causes tuberculosis (TB) which is an airborne disease that affects almost one third of the world population. The Global Tuberculosis Report (2018), published by World Health Organization, estimated 1.3 million deaths and around 10 million new cases due to TB 1. The currently available treatments for TB include first-line drugs (isoniazid, rifampicin, ethambutol, and pyrazinamide), second-line injectable drugs (amikacin, kanamycin, and capreomycin), and fluoroquinolones in combination with the second-line drugs. Drug-resistant TB has emerged due to factors that include improper treatment; poor quality, limited supply and cost of drugs; person-to-person transmission of resistant bacteria; and poor compliance. Drug resistance is now a major obstacle to effective global TB management and prevention 2. In 2017, 30% of the 6.7 million new or TB relapse cases were reported to have resistance to rifampicin (the first-line drug of treatment) worldwide. Thus, the need of the hour is to develop novel, effective, and safer treatment strategies for the treatment of multidrug resistant (MDR) and extensively drug resistant (XDR) TB. However, the process of drug development, which includes a series of steps starting from pre-clinical studies to clinical trials, is time, cost, and labor extensive. This has resulted in a lack of new molecules that could be further developed and approved by the FDA for successful incorporation into anti-TB treatment strategies. Drug r...