Characterization of metalloproteins by high-throughput X-ray absorption spectroscopy
- Wuxian Shi1,7,
- Marco Punta2,
- Jen Bohon1,
- J. Michael Sauder3,
- Rhijuta D'Mello1,
- Mike Sullivan1,
- John Toomey1,
- Don Abel1,
- Marco Lippi4,
- Andrea Passerini5,
- Paolo Frasconi4,
- Stephen K. Burley3,
- Burkhard Rost2,6 and
- Mark R. Chance1
- 1New York SGX Research Center for Structural Genomics (NYSGXRC), Case Western Reserve University, Center for Proteomics and Bioinformatics, Case Center for Synchrotron Biosciences, Upton, New York 11973, USA;
- 2TU Munich, Informatik, Bioinformatik, Institute for Advanced Studies, 85748 Garching, Germany;
- 3New York SGX Research Center for Structural Genomics (NYSGXRC), Eli Lilly and Company, Lilly Biotechnology Center, San Diego, California 92121, USA;
- 4Dipartimento di Sistemi e Informatica, Università degli Studi di Firenze, 50139 Firenze, Italy;
- 5Dipartimento di Ingegneria e Scienza dell'Informazione, Università degli Studi di Trento, 38123 Povo, Italy;
- 6New York Consortium on Membrane Protein Structure, New York Structural Biology Center, New York, New York 10027, USA
Abstract
High-throughput X-ray absorption spectroscopy was used to measure transition metal content based on quantitative detection of X-ray fluorescence signals for 3879 purified proteins from several hundred different protein families generated by the New York SGX Research Center for Structural Genomics. Approximately 9% of the proteins analyzed showed the presence of transition metal atoms (Zn, Cu, Ni, Co, Fe, or Mn) in stoichiometric amounts. The method is highly automated and highly reliable based on comparison of the results to crystal structure data derived from the same protein set. To leverage the experimental metalloprotein annotations, we used a sequence-based de novo prediction method, MetalDetector, to identify Cys and His residues that bind to transition metals for the redundancy reduced subset of 2411 sequences sharing <70% sequence identity and having at least one His or Cys. As the HT-XAS identifies metal type and protein binding, while the bioinformatics analysis identifies metal- binding residues, the results were combined to identify putative metal-binding sites in the proteins and their associated families. We explored the combination of this data with homology models to generate detailed structure models of metal-binding sites for representative proteins. Finally, we used extended X-ray absorption fine structure data from two of the purified Zn metalloproteins to validate predicted metalloprotein binding site structures. This combination of experimental and bioinformatics approaches provides comprehensive active site analysis on the genome scale for metalloproteins as a class, revealing new insights into metalloprotein structure and function.
Footnotes
-
↵7 Corresponding author.
E-mail wushi{at}bnl.gov.
-
[Supplemental material is available for this article.]
-
Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.115097.110.
- Received September 9, 2010.
- Accepted March 4, 2011.
- Copyright © 2011 by Cold Spring Harbor Laboratory Press











