摘要
In the era of antibiotic resistance,in silico prediction of bacterial resistome pro-files,likely to be associated with inactivation of new potential antibiotics is of utmost impor-tance.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 devel-oped two models,a deterministic model and a stochastic model,to predict the bacterial re-sistome 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,uCAREChemSuite-CLI 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 pre-defined drug classes,perform pairwise alignment,and cluster with database compounds using a graphical user interface.
