A Computational Framework for Proteome-Wide Pursuit and Prediction of Metalloproteins using ICP-MS and MS/MS Data

Background: Metal-containing proteins comprise a diverse and sizable category within the proteomes of organisms, ranging from proteins that use metals to catalyze reactions to proteins in which metals play key structural roles. Unfortunately, reliably predicting that a protein will contain a specific metal from its amino acid sequence is not currently possible. We recently developed a generally-applicable experimental technique for finding metalloproteins on a genome-wide scale. Applying this metal-directed protein purification approach (ICPMS and MS/MS based) to the prototypical microbe Pyrococcus furiosus conclusively demonstrated the extent and diversity of the uncharacterized portion of microbial metalloproteomes since a majority of the observed metal peaks could not be assigned to known or predicted metalloproteins. However, even using this technique, it is not technically feasible to purify to homogeneity all metalloproteins in an organism. In order to address these limitations ...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: W. Andrew Lancaster, Jeremy L. Praissman, Farris L. Poole II, Aleksandar Cvetkovic, Angeli L. Menon, Joseph W. Scott, Francis E. Jenney Jr., Michael P. Thorgersen, Ewa Kalisiak, Junefredo V. Apon, Sunia A. Trauger, Gary Siuzdak, John A. Tainer, Michael W. W. Adams
• Keywords: Artificial Intelligence, BMCBI 2011, Metal, Metalloprotein, Proteins