Biology subjects

Santos-Junior, C. D.

Publications and source records attributed to Santos-Junior, C. D..

3 recordsLinked to original sources

bioRxiv2025

A global deep-sea small protein atlas reveals a reservoir of noncanonical antimicrobial peptides

Small proteins encoded by small open reading frames (smORFs; [≤] 100 aa) represent a largely unexplored dimension of microbial diversity, especially in the deep sea. By analyzing 708 metagenomes from five major deep-sea biomes (hadal trenches, cold seeps, hydrothermal vents, abyssal plains, and seamounts), we constructed the Deep-Sea Small Protein Atlas, comprising 88.7 million smORFs with exceptional novelty and strong habitat specificity. Deep-learning predictions identified 5.47 million candidate antimicrobial peptides (c_AMPs), revealing a peptide space far larger and structurally distinct from known AMPs. Deep-sea c_AMPs are longer, enriched in nonpolar and acidic residues, and exhibit low charge and high intrinsic disorder, suggesting non-membranolytic modes of action. We synthesized 131 representative peptides, of which 87% were antimicrobial, with MICs as low as 1.25 M, broad-spectrum antibacterial and even antifungal efficacy, and minimal mammalian cytotoxicity. Transcriptomics, TEM imaging, and peptide-protein modeling showed that representative peptides preserve membrane integrity while disrupting intracellular processes such as translation and metabolism, supporting intracellular, non-lytic mechanisms. This work uncovers a vast reservoir of previously unrecognized deep-sea small proteins and structurally unconventional AMPs, providing a foundational resource for discovering next-generation peptide therapeutics.

microbiology↗

bioRxiv2019

FACS: antimicrobial peptide screening in genomes and metagenomes

MotivationAntimicrobial peptides (AMPs) have the potential to tackle multidrug-resistant pathogens in both clinical and non-clinical contexts. The recent growth in the availability of genomes and metagenomes provides an opportunity for in silico prediction of novel AMP molecules. However, due to the small size of these peptides, standard gene prospection methods cannot be applied in this domain and alternative approaches are necessary. In particular, standard gene prediction methods have low precision for short peptides, and functional classification by homology results in low recall. ResultsHere, we present Macrel (for metagenomic AMP classification and retrieval), which is an end-to-end pipeline for the prospection of high-quality AMP candidates from (meta)genomes. For this, we introduce a novel set of 22 peptide features. These were used to build classifiers which perform similarly to the state-of-the-art in the prediction of both antimicrobial and hemolytic activity of peptides, but with enhanced precision (using standard benchmarks as well as a stricter testing regime). We demonstrate that Macrel recovers high-quality AMP candidates using realistic simulations and real data. AvailabilityMacrel is implemented in Python 3. It is available as open source at https://github.com/BigDataBiology/macrel and through bioconda. Classification of peptides or prediction of AMPs in contigs can also be performed on the webserver: http://big-data-biology.org/software/macrel.

bioinformatics↗

bioRxiv2019

Uncovering the gene machinery of the Amazon River microbiome to degrade rainforest organic matter

The Amazon River receives, from the surrounding rainforest, huge amounts of terrestrial organic matter (TeOM), which is typically resistant to microbial degradation. However, only a small fraction of the TeOM ends up in the ocean, indicating that most of it is degraded in the river. So far, the nature of the genes involved in TeOM degradation and their spatial distributions are barely known. Here, we examined the Amazon River microbiome gene repertoire and found that it contains a substantial gene-novelty, compared to other environments (rivers and rainforest soil). We predicted ~3.7 million non-redundant genes, affiliating mostly to bacteria. The gene-functions involved in TeOM degradation revealed that lignin degradation correlated to tricarboxylates and hemicellulose processing, pointing to higher lignin degradation rates under consumption of labile compounds. We describe the biochemical machinery that could be speeding up the decomposition of recalcitrant compounds in Amazonian waters, previously reported only in incubation experiments.

biochemistry↗