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. 2010 Jan;38(Database issue):D545-51.
doi: 10.1093/nar/gkp893. Epub 2009 Oct 30.

PepX: a structural database of non-redundant protein-peptide complexes

Affiliations

PepX: a structural database of non-redundant protein-peptide complexes

Peter Vanhee et al. Nucleic Acids Res. 2010 Jan.

Abstract

Although protein-peptide interactions are estimated to constitute up to 40% of all protein interactions, relatively little information is available for the structural details of these interactions. Peptide-mediated interactions are a prime target for drug design because they are predominantly present in signaling and regulatory networks. A reliable data set of nonredundant protein-peptide complexes is indispensable as a basis for modeling and design, but current data sets for protein-peptide interactions are often biased towards specific types of interactions or are limited to interactions with small ligands. In PepX (http://pepx.switchlab.org), we have designed an unbiased and exhaustive data set of all protein-peptide complexes available in the Protein Data Bank with peptide lengths up to 35 residues. In addition, these complexes have been clustered based on their binding interfaces rather than sequence homology, providing a set of structurally diverse protein-peptide interactions. The final data set contains 505 unique protein-peptide interface clusters from 1431 complexes. Thorough annotation of each complex with both biological and structural information facilitates searching for and browsing through individual complexes and clusters. Moreover, we provide an additional source of data for peptide design by annotating peptides with naturally occurring backbone variations using fragment clusters from the BriX database.

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Figures

Figure 1.
Figure 1.
Contents of the protein–peptide dataset. From the PDB, 1431 protein–peptide interactions are extracted and clustered using the architecture of the binding site to remove redundancy. Of all the protein–peptide complexes, 47% are classified in 10 classes with more than five members, while the remaining 53% contain less frequent structural binding modes (A). Clusters with (B) class I MHC bound to peptide (169 structures), (C) estrogen receptor α-ligand binding domain bound to peptide (111 structures), (D) thrombin inhibitor complex (89 structures) and (E) SH3 domain–peptide interaction (7 structures) are shown.
Figure 2.
Figure 2.
Search options in the PepX database. (A) A simple, Google-like search on the contents of the database is implemented. The search is nonrestrictive and accepts everything from keywords to PDB identifiers. (B) Guided search uses structural classifications of SCOP and CATH and keywords from PDB and Pfam. (C) Tag clouds are generated from the various annotations of the protein–peptide complexes.
Figure 3.
Figure 3.
Overview of the information displayed for a thrombin complexed with an inhibitor. Searching for the keywords ‘thrombin’ and ‘inhibitor’ provides a list of hits. For the selected entry 1BTH various types of information are shown, as well as a listing of the clusters the complex belongs to. General properties of the PDB entry are accompanied by 3D views of the full complex and detailed views of the peptide-binding site generated by Yasara. The binding energy between protein and peptide as calculated by the FoldX force field is shown together with details for the hydrogen bond interactions. Various statistics regarding the secondary structure content and flexibility parameters for the binding site are listed, as well as direct links to relevant databases. The peptides are annotated with naturally occurring backbone variations using fragment clusters from the BriX database.

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