Dataset of Proteins Involved in Metal Resistance

Dataset of Proteins Involved in Metal Resistance General Information This repository contains a curated collection of protein datasets related to Metal resistance in bacteria, fungi, and plants. The datasets have been compiled from multiple publicly available sources and filtered for relevance. Datasets Included1. Bacterial Metal Resistance Proteins Description: Proteins associated with metal resistance in bacteria. Sources and Licensing BacMet – CC BY 4.0 UniProt – CC BY 4.0 Files Metal_resistance_Bacteria_Annotation_v1.0.csv Tabular annotation file listing all proteins included in the dataset, together with source-derived metadata and curated descriptive information. Metals_resistance_Bacteria_v1.0.fasta Reference FASTA file containing the amino acid sequences of proteins associated with bacterial metal resistance and tolerance. 2. Fungal Metal Resistance Proteins Description: Proteins associated with metal resistance in fungi, extracted from UniProt. Sources and Licensing UniProt – CC BY 4.0 Files Metal_resistance_Fungi_FastaDescriptors_v1.0.csv Description of protein dataset sequences and cross-references to annotation identifiers included in Metal_resistance_Fungi_Annotation_v1.0.csv. Metal_resistance_Fungi_Annotation_v1.0.csv Tabular annotation file listing all proteins included in the dataset, together with source-derived metadata and curated descriptive information. Metal_resistance_Fungi_v1.0.fasta Reference FASTA file containing the amino acid sequences of proteins associated with fungal metal resistance and tolerance. 3. Plant Metal Resistance Proteins Description: Proteins associated with metal resistance in plants, obtained from UniProt and PlantPres. Sources and Licensing UniProt – CC BY 4.0 PlantPres – No licensing information available. Files Metal_resistance_plants_annotation_v2.0.csv Tabular annotation file listing all proteins included in the dataset, together with source-derived metadata and curated descriptive information. Metal_resistance_plants_v2.0.fasta Reference FASTA file containing the amino acid sequences of proteins associated with plant metal resistance and tolerance. Usage and Attribution This dataset is freely available under CC BY 4.0, respecting the original data sources' licensing terms. If you use this dataset, please cite: UniProt- The UniProt Consortium (2024). Nucleic Acids Research, 52(D1), D1–D10. [DOI: 10.1093/nar/gky1049](https://doi.org/10.1093/nar/gky1049) MEGARes-Lakin, S. M., et al. (2017). Nucleic Acids Research, 45(D1), D574–D580. [DOI: 10.1093/nar/gkw1009](https://doi.org/10.1093/nar/gkw1009) BacMet- Pal, C., et al. (2014). Nucleic Acids Research, 42(D1), D737–D743. [DOI: 10.1093/nar/gkt1252](https://doi.org/10.1093/nar/gkt1252) PlantPres- Mousavi et al. (2016), Journal of Proteomics,143 , D69-D72. [DOI: 10.1016/j.jprot.2016.03.009](https://doi.org/10.1016/j.jprot.2016.03.009) BIOSYSMOdb was developed as part of the BIOSYSMO project, which has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101060211. Acknowledgments BacMet, UniProt, and PlantPres – For providing essential data that supported the curation of BIOSYSMOdb. BIOSYSMO consortium – For their contributions to the database’s design and development. We extend our gratitude to the Horizon Europe programme and the European Union for their support in advancing research on bioremediation and biodegradation. Version and updates: Last updated: 20/06/2026 - Current Version: 2.0 (Includes the three dataset sections (Bacteria, Fungi and Plants). In this version, the Plant dataset was manually reviewed and curated, and some entries were removed when the available evidence was not considered strong enough to support their association with metal resistance or tolerance.) - Previous Versions: 1.0 (Initial release including the complete dataset collection (Bacteria, Fungi and Plants) in their original compiled form.) Contact Information For inquiries, please contact: 📧 Project Coordinator: Dr. Sara Gil Guerrero, Main Researcher: Marta Franco de Benito, MsC 🏛️ IDENER.ai ✉️ [e-mail] , [e-mail]