April 2, 2012
As interest in membrane protein targets by both academic laboratories and large pharmacological companies grows, becoming versed in the topic of membrane proteins is more important than ever. After working with soluble proteins, the world of membrane protein structure can be intimidating to the novice structural biologist just entering the field. With over 80,000 structures present in the Protein Data Bank, searching through this vast number of entries for membrane protein structures can be akin to searching for a needle in a haystack. In this post I will highlight and discuss the features of three repositories of membrane protein structures: Membrane Proteins of Known 3D Structure, Membrane Protein Databank, and Protein Databank of Transmembrane Proteins.
The Membrane Proteins of Known 3D Structure database is curated by the lab of Dr. Stephen White at the University of California Irvine. The database is updated often, well-maintained, and contains structures of membrane proteins determined by both X-ray and electron diffraction methods (although you’ll find a few NMR structures in there as well). Membrane protein structures are divided into three classes on the site: (1) monotopic transmembrane proteins, (2) multi-pass beta-barrel transmembrane proteins, and (3) multi-pass alpha-helical transmembrane proteins. Each class of protein is the further sub-classified into functional groups via a drop-down menu. So if I wanted to look at available structures for autotransporters (my personal favorite), I would open the “Transmembrane Proteins: Beta-Barrel” drop-down menu and then select “Outer Membrane Autotransporters”. Proteins that have multiple entries (ie: different ligands bound) are then grouped together so you don’t have to go searching through a long list to find them all. The database is also accessible using text-based searches. At the time of writing of this post there were 956 entries in this database, 322 of those being unique.
The Membrane Protein Databank was started by the lab of Dr. Martin Caffrey, currently at the University of Limerick in Ireland. It is updated weekly and consists of membrane protein structures determined by X-ray diffraction, electron diffraction, NMR, and cryoelectron miscroscopy. The database is searchable by a number of different criteria including but not limited to: expression system, function, journal of publication, ligand, pH, resolution, alpha-helical vs. beta-sheet, crystallization method, and temperature. In addition, the statistics function makes doing searches like “How many structures have been solved using bicelles?” a breeze to answer. Simply select Crystallization Method for “Statistics on Membrane Protein Versus”, All Experimental Techniques for “As Appropriate, Limit Analysis by Experimental Technique”, and Bicelle for “As Approriate, Limit Analysis by Crystallization Method” and voila! At the time of the writing of this post the MPDB database held 1096 entries.
The Protein Data Bank of Transmembrane Proteins is maintained by the Institute of Enzymology in Hungary. The database is updated by an automated algorithm called TMDET that scans the entire PDB every week. This database is searchable by PDB code, PDB keyword, alpha-helical vs. beta sheet, and number of transmembrane segments. For example, if I wanted to ask the question “How many beta-barrel proteins structures exist that have 22 strands present?”, I would simply pick beta-barrels as the search type and then 22 as the number of transmembrane segments. This will return a list of 22-stranded beta-barrels with links to download the PDB text file for each entry. At the time of the writing of this post the TMDET algorithm has identified what it believes to be 1568 membrane proteins, 1348 of those alpha-helical and 219 of them beta-barrels.
- Raman, P., Cherezov, V., & Caffrey, M. (2005). The Membrane Protein Data Bank Cellular and Molecular Life Sciences, 63 (1), 36-51 DOI: 10.1007/s00018-005-5350-6
- Tusnády GE, Dosztányi Z, & Simon I (2004). Transmembrane proteins in the Protein Data Bank: identification and classification. Bioinformatics (Oxford, England), 20 (17), 2964-72 PMID: 15180935