Biology subjects

Munoz-Camargo, C.

Publications and source records attributed to Munoz-Camargo, C..

3 recordsLinked to original sources

bioRxiv2026

Assessing the translation of AI-prioritized genome-derived peptide fragments into validated antimicrobial candidates

The emergence of antibiotic-resistant pathogens such as Staphylococcus aureus demands accelerated antimicrobial discovery strategies. Artificial intelligence (AI) enables large-scale inference of candidate antimicrobial peptides (AMPs), yet experimental validation remains essential to determine whether predictions translate into biological function. Genome-guided mining, rather than unconstrained or randomly generated sequence exploration, offers a biologically grounded search space derived from organisms shaped by ecological and evolutionary pressures. Here, we evaluate this principle using Malassezia furfur, a skin-associated yeast that coexists with bacterial colonizers such as S. aureus, as a genomic source for AI-prioritized antimicrobial candidates. Candidate fragments were generated from two M. furfur genomes, filtered by physicochemical properties, prioritized with deep-learning AMP predictors, synthesized, and experimentally characterized. Selected peptides underwent cross-kingdom antimicrobial screening against S. aureus, combining kinetic growth and ultrastructural assays, complemented by in silico structural prediction, lipid-membrane interaction analysis, and human keratinocyte cytotoxicity evaluation. AI-guided genomic mining enriched biologically motivated sequence space for peptides with measurable antimicrobial activity, while revealing biases and generalizability limits of AI-based AMP inference. Closing the loop between genome-derived candidate generation, AI-based inference, synthesis, and functional characterization, this study provides an experimental assessment of model-guided AMP discovery and a reproducible route from computational prediction to validated antimicrobial candidates.

bioengineering↗

bioRxiv2020

PharmaNet: Pharmaceutical discovery with deep recurrent neural networks.

The discovery and development of novel pharmaceuticals is an area of active research mainly due to the large investments required and long payback times. As of 2016, the development of a novel drug candidate required up to $ USD 2.6 billion in investment for only 10% rate of approval by the FDA. To help decreasing the costs associated with the process, a number of in silico approaches have been developed with relatively low success due to limited predicting performance. Here, we introduced a machine learning-based algorithm as an alternative for a more accurate search of new pharmacological candidates, which takes advantage of Recurrent Neural Networks (RNN) for active molecule prediction within large databases. Our approach, termed PharmaNet was implemented here to search for ligands against specific cell receptors within 102 targets of the DUD-E database, which contains 22886 active molecules. PharmaNet comprises three main phases. First, a SMILES representation of the molecule is converted into a raw molecular image. Second, a convolutional encoder processes the data to obtain a fingerprint molecular image that is finally analyzed by a Recurrent Neural Network (RNN). This approach enables precise predictions of the molecules target on the basis of the feature extraction, the sequence analysis and the relevant information filtered out throughout the process. Molecule Target prediction is a highly unbalanced detection problem and therefore, we propose that an adequate evaluation metric of performance is the area under the Normalized Average Precision (NAP) curve. PharmaNet largely surpasses the previous state-of-the-art method with 97.7% in the Receiver Operating Characteristic curve (ROC-AUC) and 65.5% in the NAP curve. We obtained a perfect performance for human farnesyl pyrophosphate synthase (FPPS), which is a potential target for antimicrobial and anticancer treatments. We decided to test PharmaNet for activity prediction against FPPS by searching in the CHEMBL data set. We obtained three (3) potential inhibitors that were further validated through both molecular docking and in silico toxicity prediction. Most importantly, one of this candidates, CHEMBL2007613, was predicted as a potential antiviral due to its involvement on the PCDH17 pathway, which has been reported to be related to viral infections.

bioinformatics↗

bioRxiv2019

New peptides with immunomodulatory activity in macrophages and antibacterial activity against multiresistant Staphylococcus aureus

Staphylococcus aureus infections are a common concern world-wide due to the increasing number of bacterial strains with multiresistant properties to existing antibiotics, incrementing the need for novel molecules and therapy approaches for their treatment. This study evaluated the antibacterial and immunomodulatory activity of eight new peptides (AA, KS, NS, RN, AT, GF, KV and LK) as the basis for the search of new antibacterial and therapeutic agents for topic prevention and treatment against S. aureus infections. Here, there are characterized in silico eight new antimicrobial peptides. Their antibacterial activity against S. aureus and cytotoxic activity in mammalian cell lines were evaluated in vitro with the peptides individually and combined. Three of the peptides (GF, AT and AA) immunomodulatory activity was assessed in macrophages and under three scenarios: non-stimulation, Escherichia coli LPS stimulation and S. aureus lysate stimulation. Results showed that three peptides individually showed the best antibacterial activity against the S. aureus bacteria evaluated. The peptides presented immunomodulatory activity in THP-1 macrophages by displaying different profiles, increasing or decreasing four cytokines (IL-1{beta}, TNF-, IL-8 and CCL2 (MCP1)). This activity depended on the peptide concentration and the stimulation in which the macrophages were exposed to. Taken together, these results demonstrate the potential of these peptides to be used in further studies as novel antimicrobial molecules for the prevention and treatment of S. aureus infections.

microbiology↗