Bioinformatics Advance Access originally published online on August 25, 2007
Bioinformatics 2007 23(22):3110-3112; doi:10.1093/bioinformatics/btm395
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MeTaDoR: a comprehensive resource for membrane targeting domains and their host proteins
1Bioinformatics Program, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, 2Department of Biochemistry and Molecular Biology, Indiana University School of Medicine-South Bend and the Department of Chemistry and Biochemistry and the Walther Center for Cancer Research, University of Notre Dame South Bend, IN 46617 and 3Department of Chemistry, University of Illinois at Chicago, Chicago, IL 60607, USA
*To whom correspondence should be addressed.
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ABSTRACT |
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 TOP ABSTRACT 1 INTRODUCTION2 RESOURCE CONTENT3 CONCLUSIONACKNOWLEDGEMENTSREFERENCES |
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Motivation: Protein-lipid interactions play a central role in cellular signaling and membrane trafficking and at the core of these interactions are domains specialized in lipid binding and membrane targeting. Considering the importance of these domains, we have created MeTaDoR, a comprehensive resource dedicated to membrane targeting domains (MTDs).
Result: MeTaDoR begins with a brief introduction about all the important MTDs including their subcellular localization and structural features. Sequences of all known MTDs are then provided in two formats: standard Prosite format and a parsed tab-delimited format that provides a manually curated classification into binding or non-binding. Structures of all MTDs and host proteins known so far are provided with links to PDB and Pfam databases. Membrane-binding orientation of these proteins, whether experimentally determined or proposed, is also provided with links to the appropriate literature. To facilitate molecular dynamics studies of these proteins, the force-field parameters for many non-standard lipids that commonly interact with these proteins are also provided. Finally, an online server for predicting membrane-binding proteins and a search function with various search fields are included. The resource is publicly available and will be updated on a regular basis.
Availability: http://proteomics.bioengr.uic.edu/metador
Contact: huilu{at}uic.edu
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1 INTRODUCTION |
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TOPABSTRACT1 INTRODUCTION2 RESOURCE CONTENT3 CONCLUSIONACKNOWLEDGEMENTSREFERENCES |
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Cellular responses to external stimuli are mediated by diverse signal transduction pathways. Because dysfunctional or unregulated cell signaling pathways are known to cause a wide range of human diseases, including cancer, cell signaling pathways offer many attractive drug targets, as witnessed by the remarkable success of a signaling kinase inhibitor, Gleevec, against chronic myelogenous leukemia (Sawyers, 2004). Regulation of cell signaling involves a myriad of molecular interactions, including protein–protein and protein–lipid interactions. A large number of cytosolic proteins, collectively known as peripheral proteins, are recruited to different cellular membranes to form these macromolecular interactions (Cho and Stahelin, 2005; Hurley and Meyer, 2001; Teruel and Meyer, 2000). These peripheral proteins host one or more modular domains specialized in lipid binding called lipid binding domains or membrane targeting domains (MTDs). MTDs play key roles in not only cell signaling mechanisms but also other processes, such as membrane trafficking and cytoskeletal dynamics.
Due to their critical involvement in diverse cellular processes, MTDs have received much attention in the past decade. 3D structures of many MTDs and host proteins have been solved at high resolution. Biophysical studies by EPR, X-ray reflectivity, and fluorescence techniques have elucidated the diverse membrane-binding mechanism of these domains, while cellular translocation studies have illustrated their distinct subcellular localization patterns. Currently, however, there is no dedicated resource that provides comprehensive information about these important domains, apart from OPM database that lists the predicted binding orientation of proteins in membranes (Lomize et al., 2006). This is in contrast to abundant resources dedicated for proteins involved in different interactions such as protein–protein (Mewes et al., 2006; Salwinski et al., 2004), protein–DNA (Kumar et al., 2006) and protein–ligand interactions (Puvanendrampillai and Mitchell, 2003). With ever increasing data available for the lipid-binding and MTDs, the time is ripe for a high-quality organized resource dedicated for these domains.
Here, we develop an exclusive online resource for MTDs and the proteins hosting these domains, called MeTaDoR (Membrane Targeting Domains Resource). MeTaDoR integrates all the essential information about MTDs at a publicly available platform including sequences, structures, membrane-binding modes of MTDs, their manually curated classification into membrane-binding/non-binding, a prediction server, molecular dynamics resources and a search function.
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2 RESOURCE CONTENT |
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TOPABSTRACT1 INTRODUCTION2 RESOURCE CONTENT3 CONCLUSIONACKNOWLEDGEMENTSREFERENCES |
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The MeTaDoR menu begins with the listing of all known MTDs with links to more detailed descriptions about individual domains (Figure 1(a)). The description begins with a brief introduction of the function and occurrence of a domain and its host proteins. It is followed by the 3D structure of a prototype domain, preferentially as a complex with a lipid molecule (or metal ion). Also listed is the subcellular localization of the domain under various physiological conditions. A list of relevant literature, with a review article, is also provided.
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The next menu, called ‘sequences’, lists the sequences of all the MTDs in both Prosite format (Hulo et al., 2006) and a more condensed tab-delimited format that simplifies the retrieval of the sequences and other information such as function and subcellular location. These sequences were compiled after searching the Prosite database with the name of each domain as the keyword and including all well-annotated hits. Not all MTDs are directly involved in membrane binding; some are implicated in protein–protein interactions and others may play a structural role. We also provide manually curated classification as membrane-binding or non-binding for each protein.
Under the ‘structures’ menu, the PDB (Berman et al., 2002) IDs of all the domains whose structures have been solved are listed for each MTD. These IDs have been linked to the corresponding PDB and Pfam (Bateman et al., 2004) entry. MeTaDoR also provides the binding orientation of MTDs, both experimental and proposed, relative to the membrane plane in the ‘Orientation’ menu (Figure 1(b)). Apart from the corresponding PDB ID, a brief ‘Remarks’ section is included that describes the important residues and mechanism and nature of this interaction. Wherever applicable, MeTaDor has also been linked to Orientation of Protein in Membranes (OPM) database that provides spatial arrangements of membrane proteins.
A large number of MTDs specifically interact with membrane lipids. The interactions of MTDs with membrane containing these specific lipids have been the subject of many molecular dynamics studies and the force-field parameters of these lipids are undergoing constant refinement. Despite the wealth of experimental data on lipids, force-field parameters are not available for many lipids, including phosphatidylinositol-4,5-bisphosphate, diacylglycerol and phosphatidylserine, which are actively involved in membrane recruitment of many cellular proteins. To facilitate the molecular dynamics study of MTDs, MeTaDoR provides the force-field parameters of these lipids (under ‘MD Resources’ menu) along with links to appropriate tutorials describing the usage of these parameters to start the simulations. These parameters were developed and refined carefully after testing for various properties during the simulation of the bilayers containing these lipids. PDBs of some pre-equilibrated protein-lipid systems with different compositions of the lipids at different temperatures are also provided.
We also link MeTaDoR to the membrane-binding protein prediction server (‘Prediction Server’ menu) that has been in service since 2006 and has received numerous submissions from users around the world. The prediction strategy is based on a machine-learning protocol we developed earlier (Bhardwaj et al., 2006). To the best of our knowledge, this is currently the only public-domain prediction algorithm to identify membrane-binding proteins.
A search menu has also been provided to search the database for specific proteins/domains. Search fields include domain name, species, class, Swiss-Prot ID, host-protein name and keywords in function and location description. Four different search fields can be used in a single run for a refined search in addition to a homology search for a sequence using BLAST (Altschul et al., 1990).
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3 CONCLUSION |
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TOPABSTRACT1 INTRODUCTION2 RESOURCE CONTENT3 CONCLUSIONACKNOWLEDGEMENTSREFERENCES |
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MeTaDoR represents the first dedicated resource for MTDs providing all the essential information about these domains and their host proteins. It provides the sequences of these domains in an easy-to-use format. It lists all the known structures and membrane-binding modes of these domains with appropriate links. A search function has also been provided. To facilitate their molecular dynamics studies, the force-field parameters for some commonly interacting lipids are also provided. MeTaDoR is also linked to an online server for prediction of MTDs. Finally, MeTaDoR will be maintained and updated on a regular basis adding new sequential, structural and functional information about MTDs as it becomes available. Overall, integration and ready availability of such essential information about these important domains in a user-friendly format makes MeTaDoR a useful and powerful resource to the research community.
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ACKNOWLEDGEMENTS |
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TOPABSTRACT1 INTRODUCTION2 RESOURCE CONTENT3 CONCLUSIONACKNOWLEDGEMENTSREFERENCES |
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This work was supported by NIH grants P01AI060915 (H.L.), GM52598 (W.C.) and GM68849 (W.C.) and Indiana University Biomedical Research Grant (R.V.S.). N.B. gratefully acknowledges the support from FMC Technologies, Inc., Fellowship.
Conflict of Interest: none declared.
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FOOTNOTES |
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Associate Editor: Alfonso Valencia
Received on April 15, 2007; revised on July 6, 2007; accepted on July 30, 2007
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REFERENCES |
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TOPABSTRACT1 INTRODUCTION2 RESOURCE CONTENT3 CONCLUSIONACKNOWLEDGEMENTSREFERENCES |
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Bhardwaj N, et al. Structural bioinformatics prediction of membrane-binding proteins. J. Mol. Biol. (2006) 359:486–495.[CrossRef][Web of Science][Medline]
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Kumar MD, et al. ProTherm and ProNIT: thermodynamic databases for proteins and protein-nucleic acid interactions. Nucleic Acids Res. (2006) 34:D204–D206.
Lomize MA, et al. OPM: orien-tations of proteins in membranes database. Bioinformatics (2006) 22:623–625.
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Puvanendrampillai D, Mitchell JB. L/D Protein Ligand Database (PLD): additional understanding of the nature and specificity of protein-ligand complexes. Bioinformatics (2003) 19:1856–1857.
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Teruel MN, Meyer T. Translocation and reversible localization of signal-ing proteins: a dynamic future for signal transduction. Cell (2000) 103:181–184.[CrossRef][Web of Science][Medline]
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