Biology subjects

Porto, W. F.

Publications and source records attributed to Porto, W. F..

7 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↗

bioRxiv2020

Senotherapeutic peptide reduces skin biological age and improves skin health

Skin aging has been primarily related to aesthetics and beauty. Therefore, interventions have focused on reestablishing skin appearance, but not necessarily skin health, function, and resilience. Recently, cellular senescence was shown to play a role in age-related skin function deterioration and influence organismal health and, potentially, longevity. In the present study, a two-step screening was performed to identify peptides capable of reducing cellular senescence in human dermal fibroblasts (HDF) from Hutchinson-Gilford Progeria (HGPS) patients. From the top four peptides of the first round of screening, we built a 764-peptide library using amino acid scanning, of which the second screen led to the identification of peptide 14. Peptide 14 effectively decreased HDF senescence induced by HGPS, chronological aging, ultraviolet-B radiation, and etoposide treatment, without inducing significant cell death, and likely by modulating longevity and senescence pathways. We further validated the effectiveness of peptide 14 using human skin equivalents and skin biopsies, where peptide 14 promoted skin health and reduced senescent cell markers, as well as the biological age of samples, according to the Skin-Specific DNA methylation clock, MolClock. Topical application of peptide 14 outperformed Retinol treatment, the current gold-standard in "anti-aging" skin care. Finally, we determined that peptide 14 is safe for long-term applications and also significantly extends both the lifespan and healthspan of C. elegans worms tested in two independent testings. This highlights the potential for geroprotective applications of the senotherapeutic compounds identified using our screening platform beyond the skin.

bioengineering↗

bioRxiv2020

Sense the Moment: a highly sensitive antimicrobial activity predictor based on hydrophobic moment

BackgroundComputer-aided identification and design tools are indispensable for developing antimicrobial agents for controlling antibiotic-resistant bacteria. Antimicrobial peptides (AMPs) have aroused intense interest, since they have a broad spectrum of activity, and therefore, several systems for predicting antimicrobial peptides have been developed, using scalar physicochemical properties; however, regardless of the machine learning algorithm, these systems often fail in discriminating AMPs from their shuffled versions, leading to the need for new training methods to overcome this bias. Aiming to solve this bias, here we present "Sense the Moment", a prediction system capable of discriminating AMPs and shuffled versions. MethodsThe system was trained using 776 entries: 388 from known AMPs and another 388 based on shuffled versions of known AMPs. Each entry contained the geometric average of three hydrophobic moments measured with different scales. ResultsThe model showed good accuracy (>80 %) and excellent sensitivity (>90 %) for AMP prediction, exceeding deep-learning-based methods. ConclusionOur results demonstrate the systems applicability, aiding in identifying and discarding non-AMPs, since the number of false negatives is lower than false positives. General Significance: The application of this model in virtual screening protocols for identifying and/or creating antimicrobial agents could aid in the identification of potential drugs to control pathogenic microorganisms and in solving the antibiotic resistance crisis. AvailabilityThe system was implemented as a web application, available at .

bioinformatics↗

bioRxiv2020

In silico Structural Characterization of Class II Plant Defensins from Arabidopsis thaliana

Defensins comprise a polyphyletic group of multifunctional defense peptides. Cis-defensins, also known as cysteine stabilized {beta} (CS{beta}) defensins, are one of the most ancient defense peptide families. In plants, these peptides have been divided into two classes, according to their precursor organization. Class I defensins are composed of the signal peptide and the mature sequence, while class II defensins have an additional C-terminal prodomain, which is proteolytically cleaved. Class II defensins have been described in Solanaceae and Poaceae species, indicating this class could be spread among all flowering plants. Here, a search by regular expression (RegEx) was applied to the Arabidopsis thaliana proteome, a model plant with more than 300 predicted defensin genes. Two sequences were identified, A7REG2 and A7REG4, which have a typical plant defensin structure and an additional C-terminal prodomain. TraVA database indicated they are expressed in flower, ovules and seeds, and being duplicated genes, this indicates they could be a result of a subfunctionalization process. The presence of class II defensin sequences in Brassicaceae and Solanaceae and evolutionary distance between them suggest class II defensins may be present in other eudicots. Discovery of class II defensins in other plants could shed some light on flower, ovules and seed physiology, as this class is expressed in these locations.

bioinformatics↗

bioRxiv2019

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↗

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↗