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Franco, O. L.

Publications and source records attributed to Franco, O. L..

2 recordsLinked to original sources

EcDBS1R6: A new broad-spectrum cationic antibacterial peptide derived from a signal peptide sequence

Bacterial infections represent a major worldwide health problem, with an special highlight on Gram-negative bacteria, which were assigned by the World Health Organization (WHO) as the most critical priority for development of novel antimicrobial compounds. Antimicrobial peptides (AMPs) have been considered as potential alternative agents for treating these infections. Here we demonstrated the broad-spectrum activity of EcDBS1R6, a peptide derived from a signal peptide sequence of Escherichia coli that we previously turned into an AMP by making changes predicted through the Joker algorithm. Signal peptides are known to naturally interact with membranes; however, the modifications introduced by Joker made this peptide capable of killing bacteria. Membrane damage of the bacterial cells was observed by measuring membrane integrity using fluorescent probes and through scanning electron microscopy imaging. Structural analysis revealed that the C-terminus was unable to fold into an -helix, indicating that the EcDBS1R6 antibacterial activity core was located at the N-terminus, corresponding to the signal peptide portion of the parent peptide. Therefore, the strategy of transforming signal peptides into AMPs seems to be promising and could be used for producing novel antimicrobial agents.

bioinformatics

Benchmarking analysis of deleterious SNP prediction tools on CYP2D6 enzyme

The cytochrome P450 family is composed of hemeproteins involved in the metabolic transformation of endogenous and exogenous substances. The CYP2D6 enzyme is responsible for the metabolism of approximately 25% of clinically used drugs and is mainly expressed in the liver. The CYP2D6 gene is known to have a large number of Single Nucleotide Polymorphisms (SNPs) and the majority of them do not present clinical consequences. Nevertheless, these variations could modify the CYP2D6 enzymes function, resulting in poor metabolizing or ultra-extensive metabolizing phenotypes, when metabolism is slower or accelerated, respectively. Currently, there are several computational tools for predicting functional changes caused by genetic variations. Here, using 20 web servers, we evaluated the impact of 21 missense SNPs (6 neutral and 15 deleterious) previously validated by the literature. Only seven predictors presented sensitivity higher than 70%, while four showed specificity higher than 70% and only one reached the Matthews correlation coefficient of 0.39. Combinations of tools with greater sensitivity and specificity were made to improve the Matthews correlation coefficient, which increased the coefficient of five tools (Provean, FatHMM, SDM, PoPMuSiC and HotMuSiC). The results suggest that the most appropriate tool for CYP2D6 SNP prediction is FATHMM, which could aid in the classification of novel missense SNPs in this gene, providing the identification of mutations potentially associated with drug metabolism.

bioinformatics