Computational Biophysics & Protein Engineering
Aldo Herrera, PhD
Molecular Modeling & Dynamics | AI-driven Drug Discovery
Molecular Modeling & Dynamics | AI-driven Drug Discovery
I like to think of myself as a curious and self-motivated scientist that enjoys being surprised by biology.
I have focused my career on computational tools for the analysis of biomolecular systems. My projects are strongly related to molecular modelling, simulations of protein dynamics and protein-ligand interactions, including steered molecular dynamics and free energy landscape calculations.
Beyond biophysics, I have also participated in other projects for the analysis (and/or code developing for the analysis) of genetic, biochemical, and chemical data. Including approaches of machine and deep learning as well as bio- and chemo- informatics.
Center for Research and Advanced Studies of the IPN, Monterrey Unit (CINVESTAV)
2020 - 2025
Thesis: The Role of Conformational Dynamics Conservation in the Structure-Function Paradigm: A Case Study on the RBD of SARS-CoV-2 Spike Protein Variants.
Autonomous University of Nuevo León (UANL)
2018 - 2020
Thesis: Analysis of N-acetyltransferase 2 (NAT2) gene polymorphisms as markers of liver damage from first-line tuberculosis treatment in a population from northeastern Mexico, and molecular dynamics studies.
Autonomous University of Nuevo León (UANL)
2013 - 2018
As an undergraduate volunteer in Prof. Francisco Ávalos Alanís's Natural Products Chemistry Laboratory at Faculty of Chemistry at UANL, I synthesized, characterized, and screened compounds for potential anti-tuberculosis and anti-cancer activity. This experience refined my medicinal chemistry knowledge, synthetic methodologies, stereochemistry, and scaffold design for pharmacological applications.
Intrigued by molecular biology and genomics, I focused on how genetic mutations may drive disease outcomes during my master's. At the Immunogenetics lab from the Northeast Biomedical Research Center (CIBIN-IMSS), under the supervision of Dr. Beatriz Silva Ramírez, we investigated genomic biomarkers in the Mexican population for precision medicine and drug-related toxicity. Our experimental findings correlated NAT2 gene mutations with hepatotoxic outcomes in tuberculosis treatment. Recognizing a critical gap in the molecular-level explanation, together with Dr. Mauricio Carrillo-Tripp, we developed a computational framework integrating molecular modeling, HPC, and molecular dynamics to elucidate the atomic-level impact of these non-synonymous mutations, leading to a peer-reviewed publication.
Under Dr. Mauricio Carrillo-Tripp, I developed comprehensive expertise in Computational Biophysics and Molecular Modeling & Dynamics. My research showed that evolutionary pressure influences viral protein function by constraining conformational dynamics and preserving essential correlated motions. This involved conducting large-scale Molecular Dynamics simulations, automating workflows, enhanced sampling (AWH, umbrella sampling), free energy calculations (PMF), and flexibility assessments (clustering, RMSF, PCA).
Beyond biophysics, I possess advanced programming and HPC management skills, evidenced by first-author publications at the development of an in-house Bash-based pipeline for high-throughput, cavity-targeting molecular docking of protein-ligand complexes, ensuring convergence to minimal binding scores, as well as its python version. This framework showcases my expertise in managing high-volume, automated workflows to deliver biologically relevant results, as demonstrated by our findings on garlic compounds for COVID-19 and a SARS-CoV-2 drug repurposing pipeline.
My research history reflects a clear collaborative mindset, a relentless drive for unraveling fundamental biological mechanisms, and an ambition to explore new biological frontiers, where interdisciplinary skills are paramount. I consistently demonstrate a strong ability to adapt and tackle complex challenges by pushing methodological boundaries, excelling in areas requiring novel computational approaches for biological discovery and insight.
My core research interest lies at the intersection of protein dynamics, computational biophysics for biomedical solutions, and rational protein design. Protein engineering is a primary interest, driven by the need to translate fundamental understanding into the creative design of novel molecular functions. I am particularly interested in applying knowledge of protein dynamics and conformational landscapes to engineer proteins for specific desired functions and biomedical applications.
Herrera-Rodulfo, A., Andrade-Medina, M., García-Delgado, M.S., & Carrillo-Tripp, M.
Journal of Computational Biophysics and Chemistry (In Press, 2025)
Granados-Tristán, A. L., Carrillo-Tripp, M., Hernández-Luna, C. E., Herrera-Rodulfo, A., et al.
PLOS Computational Biology (In Press, 2025)
Herrera-Rodulfo, A., Andrade-Medina, M., & Carrillo-Tripp, M.
Biomedical Engineering, IntechOpen. https://doi.org/10.5772/intechopen.105792
del Rayo Camacho-Corona, M., Camacho-Morales, A., Góngora-Rivera, F., Escamilla-García, E., Morales-Landa, J. L., Andrade-Medina, M., Herrera-Rodulfo, A., et al.
Current Topics in Medicinal Chemistry, 22(2), 109–131. https://doi.org/10.2174/1568026621666211122163156
Herrera-Rodulfo, A., Carrillo-Tripp, M., Laura Yeverino-Gutierrez, M., Peñuelas-Urquides, K., Adiene González-Escalante, L., Bermúdez de León, M., & Silva-Ramirez, B.
Clinica Chimica Acta, 519, 153–162. https://doi.org/10.1016/j.cca.2021.04.017
Extracts protein-ligand information from PDB IDs into graph format for Graph Neural Network input.
Automates multiple-cycle molecular docking analysis, processes files into AutoDock VINA inputs, formats output into CSV for further analysis.
Analyzes distribution of molecular docking sets, visualizes protein frequent electrostatic interactions.
Protein-DNA docking & Protein-Ligand molecular dynamics simulations.
I'm always open to discussing new research opportunities, collaborations, or potential projects.
Send me an email