J Chem Inf Model - Jointly handling potency and toxicity of antimicrobial peptidomimetics by simple rules from desirability theory and chemoinformatics.


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Today, emerging and increasing resistance to antibiotics has become a threat to public health worldwide. Antimicrobial peptides have unique action mechanisms making them an attractive therapeutic prospect to be applied against resistant bacteria. However, the major drawback is related with their high hemolytic activity which cancels out the safety requirements for a human antibiotic. Therefore, additional efforts are needed to develop new antimicrobial peptides that possess a greater potency for bacterial cells and less or no toxicity over erythrocytes. In this paper, we introduce a practical approach to simultaneously deal with these two conflicting properties. The convergence of machine learning techniques and desirability theory allowed us to derive a simple, predictive, and interpretable multicriteria classification rule for simultaneously handling the antibacterial and hemolytic properties of a set of cyclic ?-hairpin cationic peptidomimetics (C?-HCPs). The multicriteria classification rule exhibited a prediction accuracy of about 80% on training and external validation sets. Results from an additional concordance test have shown an excellent agreement between the multicriteria classification rule predictions and the predictions from independent classifiers for complementary antibacterial and hemolytic activities, respectively, evidencing the reliability of the multicriteria classification rule. The rule was also consistent with the general mode of action of cationic peptides pointing out its biophysical relevance. We also propose a multicriteria virtual screening strategy based on the joint use of the multicriteria classification rule, desirability, similarity, and chemometrics concepts. The ability of such a virtual screening strategy to prioritize selective (nonhemolytic) antibacterial C?-HCPs was assessed and challenged for their predictivity regarding the training, validation, and overall data. In doing so, we were able to rank a selective antibacterial C?-HCP earlier than a biologically inactive or nonselective antibacterial C?-HCP with a probability of ca. 0.9. Our results thus indicate that promising chemoinformatics tools were obtained by considering both the multicriteria classification rule and the virtual screening strategy, which could, for instance, be used to aid the discovery and development of potent and nontoxic antimicrobial peptides.

Resumo Limpo

today emerg increas resist antibiot becom threat public health worldwid antimicrobi peptid uniqu action mechan make attract therapeut prospect appli resist bacteria howev major drawback relat high hemolyt activ cancel safeti requir human antibiot therefor addit effort need develop new antimicrobi peptid possess greater potenc bacteri cell less toxic erythrocyt paper introduc practic approach simultan deal two conflict properti converg machin learn techniqu desir theori allow us deriv simpl predict interpret multicriteria classif rule simultan handl antibacteri hemolyt properti set cyclic hairpin cation peptidomimet chcps multicriteria classif rule exhibit predict accuraci train extern valid set result addit concord test shown excel agreement multicriteria classif rule predict predict independ classifi complementari antibacteri hemolyt activ respect evidenc reliabl multicriteria classif rule rule also consist general mode action cation peptid point biophys relev also propos multicriteria virtual screen strategi base joint use multicriteria classif rule desir similar chemometr concept abil virtual screen strategi priorit select nonhemolyt antibacteri chcps assess challeng predict regard train valid overal data abl rank select antibacteri chcp earlier biolog inact nonselect antibacteri chcp probabl ca result thus indic promis chemoinformat tool obtain consid multicriteria classif rule virtual screen strategi instanc use aid discoveri develop potent nontox antimicrobi peptid

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