Prediction of antimicrobial peptides using machine learning classifiers

I created machine learning classifiers to predict the antimicrobial activity of peptides. It was an end-to-end project in which I did the data pre-processing, feature engineering, training, hyperparameter tuning, and deployment of the best model as a web application with Streamlit.

Antiparasitic peptides discovery

This project was about the exploration of the antiparasitic peptides’ chemical space and prediction of new potential compounds through a new approach based on network science and similarity searching.

HerrCompBioinfo

This project aimed to create an open-source educational resource of computational tools (e.g., GNU/Linux, terminal usage, Bash-AWK scripting, etc) for Bioinformatics enthusiasts written in Spanish.

Exploring the Chemical Space of Antiparasitic Peptides and Discovery of New Promising Leads through a Novel Approach based on Network Science and Similarity Searching

Oral presentation in English about the Antiparasitic peptides discovery project.

Exploring the Chemical Space of Antiparasitic Peptides and Discovery of New Promising Leads through a Novel Approach based on Network Science and Similarity Searching

Oral presentation in English about the Antiparasitic peptides discovery project.

HerrCompBioinfo: An open-source educational resource of computational tools for Bioinformatics enthusiasts written in Spanish

Oral presentation in English about the HerrCompBioinfo project.

The molecular basis of JAZ-MYC coupling, a protein-protein interface essential for plant response to stressors

Oral presentation in English about The molecular basis of JAZ-MYC coupling, a key component of plant immunity project. This presentation was awarded as the best oral presentation of the symposium.

The molecular basis of JAZ-MYC coupling, a key component of plant immunity

In this project, we identified binding specificity between repressor proteins and a transcriptional factor associated with the Jasmonic Acid pathway in Arabidopsis thaliana through Molecular Dynamics simulations and Machine Learning algorithms.

Prediction of potential drug ligands that bind to Beta-Lactamases

In this project, I created machine learning classifiers (Logistic Regression, Random Forest, and XGBoost) to evaluate the activity of molecules that have been experimentally tested to bind or not bind to Beta-Lactamases. Also, I implemented a web service with the best model.

Horizontal gene transfer between arthropods and bacteria

In this project, we understood the impact of horizontal gene transfer in the genome of Streptomyces clavuligerus using Phylogenetics, RNAseq data, and other bioinformatics tools.

CRBN mutations

This project is a computational study of the structural changes of two Cereblon protein mutations using Molecular Dynamics and Quantum Mechanics simulations.

Modeling of protein-protein interaction and search for key residues by machine learning of the JAZ-MYC3 complex of Arabidopsis thaliana

Oral presentation in Spanish about The molecular basis of JAZ-MYC coupling, a key component of plant immunity project.

In silico detection of horizontal gene transfer in Streptomyces clavuligerus

Oral presentation in English about the Horizontal gene transfer between arthropods and bacteria project.

Modeling of Protein-protein interaction and search for key residues by machine learning of a protein complex in the jasmonic acid route in Arabidopsis thaliana

Poster presentation in Spanish about The molecular basis of JAZ-MYC coupling, a key component of plant immunity project

In silico detection of an antimicrobial peptide (AMP) transferred horizontally from arthropods to bacteria

Oral presentation in Spanish about the Horizontal gene transfer between arthropods and bacteria project.

Structural changes due to a mutation in Cereblon might be a cause for intellectual disability

Poster presentation in English about the CRBN mutations project.