uCARE Chem Suite and uCAREChemSuiteCLI: Tools for bacterial resistome prediction.

In the era of antibiotic resistance, in silico prediction of bacterial resistome profiles, likely to be associated with inactivation of new potential antibiotics is of utmost importance. Despite this, to the best of our knowledge, no tool exists for such prediction. Therefore, under the rationale that drugs with similar structures have similar resistome profiles, we developed two models, a deterministic model and a stochastic model, to predict the bacterial resistome likely to neutralize uncharacterized but potential chemical structures. The current version of the tool involves the prediction of a resistome for Escherichia coli and Pseudomonas aeruginosa. The deterministic model on omitting two diverse but relatively less characterized drug classes, polyketides and polypeptides showed an accuracy of 87%, a sensitivity of 85%, and a precision of 89%, whereas the stochastic model predicted antibiotic classes of the test set compounds with an accuracy of 72%, a sensitivity of 75%, and a precision of 83%. The models have been implemented in both a standalone package and an online server, uCAREChemSuiteCLI and uCARE Chem Suite, respectively. In addition to resistome prediction, the online version of the suite enables the user to visualize the chemical structure, classify compounds in 19 predefined drug classes, perform pairwise alignment, and cluster with database compounds using a graphical user interface. AVAILABILITY: uCARE Chem Suite can be browsed at: https://sauravsaha.shinyapps.io/ucarechemsuite2/, and uCAREChemSuiteCLI can be installed from:1. CRAN (https://cran.r-project.org/package=uCAREChemSuiteCLI) and2. GitHub (https://github.com/sauravbsaha/uCAREChemSuiteCLI).

in Silico analysis of Escherichia coli polyphosphate kinase (PPK) as a novel antimicrobial drug target and its high throughput virtual screening against PubChem library.

Multiple drug resistance (MDR) in bacteria is a global health challenge that needs urgent attention. The 2011 outbreak caused by Escherichia coli O104:H4 in Europe has exposed the inability of present antibiotic arsenal to tackle the problem of antimicrobial infections. It has further posed a tremendous burden on entire pharmaceutical industry to find novel drugs and/or drug targets. Polyphosphate kinase (PPK) in bacteria plays a crucial role in helping latter to adapt to stringent conditions of low nutritional availability thus making it a good target for antibacterials. In spite of this critical role, to best of our knowledge no in-silico work has been carried out to develop PPK as an antibiotic target. In the present study, virtual screening of PPK was carried out against all the 3D compounds with pharmacological action present in PubChem database. Our screening results were further refined by interaction maps to eliminate the false positive data respectively. From our results, compound number 5281927 (PubChem ID) has been found to have significant affinity towards affinity towards PPK active ATP-binding site indicating its therapeutic relevance.