MbPA

MbPA curates annotated metalloprotein records, metal-binding sites, quantitative proteomics data, and mutation annotations to support analysis of protein–metal interactions across species.

Key Features:

• Extensive Database: Contains 106,373 entries and 440,187 sites associated with 54 different metals across 8,169 species.
• Species-Specific Data: Provides listings of metal-binding proteins and site annotations specific to individual species for comparative analyses.
• Metal-Proteomics Data: Integrates quantitative proteomics data describing expression levels of metalloproteins in various tissues and organs.
• Amino Acid Binding Analysis: Reports residue preferences at metal-binding sites, noting that ~80% of metal ions preferentially bind to cysteine, aspartic acid, glutamic acid, and histidine.
• Diversity Measure Application: Applies a Diversity Measure to evaluate metal-binding specificity across the periodic table, including analysis of binding modes of 19 transition metals with 20 amino acids.
• Pathogenic Mutation Data: Includes annotations for 6,855 potential pathogenic mutations related to metalloproteins.

Scientific Applications:

• Protein–Metal Interaction Studies: Enables characterization of principles governing metal coordination and residue preferences in metalloproteins.
• Expression and Proteomics Analyses: Supports investigation of tissue- and organ-specific abundance of metalloproteins using integrated quantitative proteomics data.
• Mutation Impact and Disease Research: Facilitates study of potential pathogenic mutations in metalloproteins to explore disease mechanisms.
• Comparative and Evolutionary Biology: Allows comparative studies of metalloprotein diversity across 8,169 species and investigation of evolutionary patterns.
• Drug Discovery and Toxicology: Provides data relevant to targeting metalloproteins and assessing metal-related toxicological effects.
• Bioinformatics and Molecular Biology Research: Serves as a resource for computational and experimental analyses in bioinformatics, molecular biology, and biomedicine.

Methodology:

Integration of curated data sources and quantitative proteomics combined with computational analysis using a Diversity Measure and related analytical techniques.