Biology subjects

Martinez-Archundia, M.

Publications and source records attributed to Martinez-Archundia, M..

3 recordsLinked to original sources

bioRxiv2023

Atomistic molecular insight on Angiotensin-(1-7) inter-peptide interactions

Angiotensin-(1-7) is an endogenous peptide with vaso-protective, anti-oxidant, and anti-inflammatory effects which has been proposed as a potential therapeutic agent in a wide range of clinical conditions. Angiotensin-(1-7) presents a pH-dependent physical instability in aqueous solutions; however, it still lacks a proper atomistic study that provides insights into this behavior and its potential implications. Hence, we studied the formation of early Angiotensin-(1-7) oligomeric aggregates in an aqueous environment under acidic and neutral conditions; physiological and high ionic strength; and high and low peptide concentrations using all-atom Molecular Dynamics simulations. Our main findings are: 1) at acidic pH, there is a poor level of Angiotensin-(1-7) clustering, while, 2) at neutral pH, peptides aggregate in a unique cluster, in good trend with experimental physical instability reports and 3) an increase in salt concentration at acidic pH gives place to aggregation similar to the case at neutral pH. Our results open the route for the modulation of Angiotensin-(1-7) aggregation through a combination of salt concentration and pH conditions. Our protocol (MD + cluster analysis + amino acids interaction map analysis) is general and could be applied to other peptides to study the inter-peptide interaction mechanisms.

biophysics↗

bioRxiv2022

Unveiling the G4-PAMAM capacity to bind and protect Ang-(1-7) bioactive peptide

New therapies that allow natural healing processes are required. Such as the endogenous peptide called Angiotensin-(1-7), a safe and eff e drug, which is able to re-balance the Renin-Angiotensin system affected during several pathologies, including the new COVID-19; cardiovascular, renal, and pulmonary disease; diabetes; neuropathic pain; Alzheimer and cancer. However, one of the limiting factors for its application is its unfavorable pharmacokinetic profile. In this work, we propose the coupling of Angiotensin-(1-7) to PAMAM dendrimers in order to evaluate the capacity of the nanocarrier to improve isolated peptide features and to gain insight into the structural as well as the energetic basis of its molecular binding. The In Silico tests were performed in acidic and neutral pH conditions as well as amino-terminated and hydroxyl-terminated PAMAM dendrimers. High-rigor computational approaches, such as molecular dynamics and metadynamics simulations were used. We found that, at neutral pH, PAMAM dendrimers with both terminal types are able to interact stably with 3 Angioteinsin-(1-7) peptides through ASP1, TYR4 and PRO7 key aminoacids, however, there are some differences in the binding sites of the peptides. In general, they bind on the surface in the case of the hydroxyl-terminated compact dendrimer and in the internal zone in the case of the amino-terminated open dendrimer. At acidic pH, PAMAM dendrimers with both terminal groups are still able to interact with peptides either internalized or in its periphery, however, the number of contacts, the percentage of coverage and the number of HBs are lesser than at neutral pH, suggesting a state for peptide release. In summary, amino-terminated PAMAM dendrimer showed slightly better features to bind, load and protect Angiotensin-(1-7) peptides.

biophysics↗

bioRxiv2018

Ensemble Feature Selection and Meta-Analysis of Cancer miRNA Biomarkers

The role of microRNAs (miRNAs) in cellular processes captured the attention of many researchers, since their dysregulation is shown to affect the cancer disease landscape by sustaining proliferative signaling, evading program cell death, and inhibiting growth suppressors. Thus, miRNAs have been considered important diagnostic and prognostic biomarkers for several types of tumors. Machine learning algorithms have proven to be able to exploit the information contained in thousands of miRNAs to accurately predict and classify cancer types. Nevertheless, extracting the most relevant miRNA expressions is fundamental to allow human experts to validate and make sense of the results obtained by automatic algorithms. We propose a novel feature selection approach, able to identify the most important miRNAs for tumor classification, based on consensus on feature relevance from high-accuracy classifiers of different typologies. The proposed methodology is tested on a real-world dataset featuring 8,129 patients, 29 different types of tumors, and 1,046 miRNAs per patient, taken from The Cancer Genome Atlas (TCGA) database. A new miRNA signature is suggested, containing the 100 most important oncogenic miRNAs identified by the presented approach. Such a signature is proved to be sufficient to identify all 29 types of cancer considered in the study, with results nearly identical to those obtained using all 1,046 features in the original dataset. Subsequently, a meta-analysis of the medical literature is performed to find references to the most important biomarkers extracted by the methodology. Besides known oncomarkers, 15 new miRNAs previously not ranked as important biomarkers for diagnosis and prognosis in cancer pathologies are uncovered. Such miRNAs, considered relevant by the machine learning algorithms, but still relatively unexplored by specialized literature, could provide further insights in the biology of cancer.

bioinformatics↗