Biology subjects

Ramos, J. L.

Publications and source records attributed to Ramos, J. L..

2 recordsLinked to original sources

bioRxiv2025

Development of Advanced Bioinformatic Profiles to Improve the Detection and Functional Understanding of Fungal Acid Phosphatases

We have retrieved approximately 9,000 protein sequences annotated as fungal acid phosphatase or phytase from the UniProtKB database. After stringent quality filtering, a curated dataset comprising 3,058 high-confidence sequences was assembled. Phylogenetic analysis revealed that these enzymes segregate into eight distinct clades, representing distinct groups of fungal acid phosphatases, purple acid phosphatases, phytases and groups containing both phytases and acid phosphatases annotations. Based on this classification, we have developed three representative protein profiles referred to as Pfr-A-Fungal_phos, Pfr-B-Fungal_phos and Pfr-C-Fungal_phos, each designed to capture the phylogenetic and functional of the enzyme families. The specificity and breadth of these profiles were validated through comprehensive heat-map analyses. When deployed on public protein and metagenomic databases, these profiles demonstrated high sensitivity and specificity, enabling the identification of hundreds of previously uncharacterized fungal proteins. These proteins spanned a broad taxonomic distribution, with notable prevalence in the Ascomycota and Basidiomycota phyla. Collectively, these findings highlight the utility of the newly developed profiles to uncover a novel and taxonomically widespread family of fungal enzymes. This work provided valuable insights into the evolutionary diversity and ecological significance of fungal enzymes involved in phosphorous cycling.

microbiology↗

bioRxiv2017

Expanding The Computable Reactome In Pseudomonas putida Reveals Metabolic Cycles Providing Robustness

Genome-scale network reconstructions are organism-specific representations of metabolism and powerful tools for analyzing systemic metabolic properties. The use of reconstructions is limited by the lack of coverage of the metabolic reactome. We present an exhaustive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440, greatly expanding its computable metabolic states. The reconstruction, iJN1411, represents a significant expansion over other reconstructed bacterial metabolic networks. Computations based on the reconstruction exhibit high accuracy in predicting nutrient sources, growth rates, carbon flux distributions, and gene essentiality, thus providing a deep understanding of Pseudomonas metabolism. iJN1411 was used for: i) the assessment of the metabolic capabilities of P. putida as a species through multi-strain modeling, ii) deciphering the molecular mechanisms underlying metabolic robustness, and iii) identification of metabolic \"capacitors\" based on ATP-fueled metabolic cycles. This study represents the most complete and comprehensive bacterial metabolic reconstruction built to date, while providing computational and experimental evidence about how bacteria increase metabolic robustness, paving the way for engineering more robust biocatalysts and searching for drug targets in robust pathogens.

systems biology↗