Biology subjects

Alencar, S. A.

Publications and source records attributed to Alencar, S. A..

3 recordsLinked to original sources

bioRxiv2021

Structural effects driven by rare point mutations in amylin hormone, the type II diabetes associated peptide

BackgroundAmylin is a 37-amino-acid peptide hormone co-secreted with insulin, which participates in glucose homeostasis. This hormone is able to aggregate in a {beta}-sheet conformation and deposit in islet amyloids, a hallmark in type II diabetes. Since amylin is a gene-encoded hormone, this peptide has variants caused by point mutations that can impact its functions. MethodsHere, we analyzed the structural effects caused by S20G and G33R point mutations which, according to the 1000 Genomes Project, have frequency in East Asian and European populations, respectively. The analyses were performed by means of aggrescan server, SNP functional effect predictors, and molecular dynamics. ResultsWe found that both mutations have aggregation potential and cause changes in the monomeric forms when compared with wild-type amylin. Furthermore, comparative analyses with pramlintide, an amylin drug analogue, allowed us to infer that second -helix maintenance may be related to the aggregation potential. ConclusionsThe S20G mutation has been described as pathologically related, which is in agreement with our findings. In addition, our data suggest that the G33R mutation might have a deleterious effect. The data presented here also provide new therapy opportunities, whether for creating more effective drugs for diabetes or implementing specific treatment for patients with these mutations. General SignificanceOur data could help to better understand the impact of mutations on the wild-type amylin sequence, as a starting point for the evaluation and characterization of other variations. Moreover, these findings could improve the health of patients with type II diabetes.

bioinformatics↗

bioRxiv2021

In silico assessment of missense point mutations on human cathelicidin LL-37

Cathelicidin antimicrobial peptides are a diverse family of cationic amphipathic peptides with multiple activities. In humans, cathelicidin LL-37 is one of the main host defense peptides with a remarkable medical and biotechnological potential. Deregulation of LL-37 expression has been associated with inflammatory diseases. However the effects of point mutations driven by single nucleotide polymorphisms (SNPs) on LL-37 are unknown. Here we applied an array of computational tools to investigate the effects of such mutations on LL-37 structure and activity. Due to the fact that, on cathelicidins, the prodomain is more conserved than the mature peptide, the SNP effect predictions were biased and, overall, resulted in neutral effects; and due to the slight changes in physicochemical properties, the antimicrobial predictions indicated the maintenance of such activity. Nonetheless, R07P, R07W, R29Q, R29W mutations reduced the peptide net charge, which in turn could result in less active LL-37 variants. Molecular dynamics data indicated that R07Q and N30Y mutations altered the LL-37 structure, leading to potential deleterious effects. In addition, the helix dipole is altered in G03A, R07P, R07W and L31P mutations, which could also alter the antimicrobial activity. Our results indicated that despite the mutations did not alter the residues from LL-37 active core, they could influence the antimicrobial activity and consequently, could be involved in inflammatory diseases.

bioinformatics↗

bioRxiv2019

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↗