Evidence map›Paper›PMID 18182098›Full record

ArticleBMC bioinformatics2008

SCOWLP classification: structural comparison and analysis of protein binding regions.

Joan Teyra, Maciej Paszkowski-Rogacz, Gerd Anders, M Teresa Pisabarro

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Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

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16citing papers in PubMed
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3 · Its place in the literature

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16 citing papers in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Joan TeyraStructural Bioinformatics, BIOTEC TU-Dresden, Tatzberg 47-51, 01307 Dresden, Germany. joan.teyra@biotec.tu-dresden.de
Maciej Paszkowski-Rogacz
Gerd Anders
M Teresa Pisabarro

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDetailed information about protein interactions is critical for our understanding of the principles governing protein recognition mechanisms. The structures of many proteins have been experimentally determined in complex with different ligands bound either in the same or different binding regions. Thus, the structural interactome requires the development of tools to classify protein binding regions. A proper classification may provide a general view of the regions that a protein uses to bind others and also facilitate a detailed comparative analysis of the interacting information for specific protein binding regions at atomic level. Such classification might be of potential use for deciphering protein interaction networks, understanding protein function, rational engineering and design. DESCRIPTION: Protein binding regions (PBRs) might be ideally described as well-defined separated regions that share no interacting residues one another. However, PBRs are often irregular, discontinuous and can share a wide range of interacting residues among them. The criteria to define an individual binding region can be often arbitrary and may differ from other binding regions within a protein family. Therefore, the rational behind protein interface classification should aim to fulfil the requirements of the analysis to be performed.We extract detailed interaction information of protein domains, peptides and interfacial solvent from the SCOWLP database and we classify the PBRs of each domain family. For this purpose, we define a similarity index based on the overlapping of interacting residues mapped in pair-wise structural alignments. We perform our classification with agglomerative hierarchical clustering using the complete-linkage method. Our classification is calculated at different similarity cut-offs to allow flexibility in the analysis of PBRs, feature especially interesting for those protein families with conflictive binding regions. The hierarchical classification of PBRs is implemented into the SCOWLP database and extends the SCOP classification with three additional family sub-levels: Binding Region, Interface and Contacting Domains. SCOWLP contains 9,334 binding regions distributed within 2,561 families. In 65% of the cases we observe families containing more than one binding region. Besides, 22% of the regions are forming complex with more than one different protein family.

conclusionThe current SCOWLP classification and its web application represent a framework for the study of protein interfaces and comparative analysis of protein family binding regions. This comparison can be performed at atomic level and allows the user to study interactome conservation and variability. The new SCOWLP classification may be of great utility for reconstruction of protein complexes, understanding protein networks and ligand design. SCOWLP will be updated with every SCOP release. The web application is available at http://www.scowlp.org.

Indexed as

Databases, ProteinInternetClassificationComputational BiologyProtein Interaction Domains and MotifsProteomics

Identifiers

PMID18182098
PMCPMC2259299

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.