Anders, G.

Paszkowski-Rogacz, M.

Pisabarro, M. T.

Teyra, J.

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John von Neumann Institute for ComputingAnalysis and Classification of theStructural InteractomeJ. Teyra, M. Paszkowski-Rogacz, G. Anders,M. T. Pisabarropublished inFrom Computational Biophysics to Systems Biology (CBSB08),Proceedings of the NIC Workshop 2008,Ulrich H. E. Hansmann, Jan H. Meinke, Sandipan Mohanty,Walter Nadler, Olav Zimmermann (Editors),John von Neumann Institute for Computing, Ju¨lich,NIC Series, Vol. 40, ISBN 978-3-9810843-6-8, pp. 397-400, 2008.c© 2008 by John von Neumann Institute for ComputingPermission to make digital or hard copies of portions of this work forpersonal or classroom use is granted provided that the copies are notmade or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwiserequires prior specific permission by the publisher mentioned above.http://www.fz-juelich.de/nic-series/volume40Analysis and Classification of the Structural InteractomeJoan Teyra, Maciej Paszkowski-Rogacz, Gerd Anders, and M. Teresa PisabarroBIOTEC TU Dresden, Tatzberg 47/49, 01307 Dresden, GermanyE-mail: joan.teyra@biotec.tu-dresden.de1 IntroductionProtein interactions are essential for intra-cellular communication in biological processes.Proteins are composed of small units or domains that can physically interact together form-ing multi-domain protein complexes. A single protein can have several binding regions,and each region can engage distinct ligands, either simultaneously or at successive stagesof signalling. Detailed information about protein interactions is critical for our understand-ing of the principles governing protein recognition mechanisms. The structures of manyproteins have been experimentally determined in complex with different ligands boundeither in the same or different binding regions. Thus, the structural interactome requiresthe development of tools to classify protein binding regions. A proper classification mayprovide a general view of the regions that a protein uses to bind others and also facilitatea detailed comparative analysis of the interacting information for specific protein bindingregions at atomic level. Such classification might be of potential use for deciphering pro-tein interaction networks, understanding protein function, rational engineering and design.We present the SCOWLP database and web-interface [1, 2], a framework to study proteininterfaces and for comparative analysis of protein family binding regions (PBRs).2 MethodologySCOWLP was developed following several steps:2.1 Extraction of Interfaces and Contacting DomainsAn accurate definition of the interacting residues is crucial to have a proper clustering ofa family PBR. Our database includes all protein-interacting components of the PDB in-cluding peptides and solvent, which until now have been excluded from systematic proteininterface analysis and databases. The inclusion of water enriches the definition of proteininterfaces by considering residues interacting exclusively by water, defined as wet spots[3]. In our database all interface interactions are described at atom, residue and domainlevel by using interacting rules based on atomic physicochemical criteria [1]. The defini-tion of a domain was extracted from the SCOP database [4]. We consider ”interface” alldomain-domain interactions ; that means those belonging to the same protein and also todifferent proteins. SCOWLP contains 79,803 interfaces contained in 2,561 SCOP fami-lies. We grouped the domains participating in each interface by SCOP families, obtainingfor each family a list of contacting domains with the residues forming part of the bindingregion.3972.2 Pair-Wise Structural Alignments (PSAs)A reliable alignment is indispensable to calculate the similarities among binding regions.For this purpose we used MAMMOTH, which has shown proven accuracy to structurallyalign protein families [5]. We performed all-against-all PSAs of the contacting domainsfor each family to be able to measure the similarity among binding regions. SCOWLPcontains about 160,000 contacting domains uneven distributed by families. This represents276 million PSAs performed in a cluster of five Pentium IV 2.6 GHz. The alignmentswere performed taking the C atoms into account and using a gap penalty function foropening and extension [6]. The root-mean-squared deviation (RMSD) was not consideredfor measuring the similarity between two interfaces, as the superimposed members of thesame family share a common structure.2.3 Similarity Index (Si)The residues described in SCOWLP forming and interface were mapped onto the domain-pair structural alignment. We calculated a similarity index (Si) based on the number ofinteracting residues that overlap and the length of both interacting regions by:Si(a, b) =2IRoverlap(a,b)IRlength(a)−IRgaps(a)+IRlength(b)−IRgaps(b) , where a and b represent the twodomain structures aligned. The number of interacting residues that match in the PSA isrepresented by IRoverlap(a, b). This value is divided by the average number of the inter-acting residues in both domains excluding the interacting residues located in gap regionsin the structural alignment (IRgaps).2.4 Clustering Binding RegionsBased on the calculated Si, we clustered the binding regions of each SCOP family using theagglomerative hierarchical algorithm following several steps: 1) Define as a cluster eachcontacting domain. 2) Find the closest pair of clusters and merge them into a single cluster.3) Re-compute the distances between the new cluster and each of the remaining clusters.4) Repeat steps 2 and 3 until all contacting domains are clustered into a single cluster.To re-compute the distances we used the complete-linkage method, which considers thedistance between two clusters to be equal to the minimum similarityof the two members.2.5 Binding Region Definition by Si Cut-OffsThe result of the clustering can be represented in an intuitive tree or dendrogram, whichshows how the individual contacting domains are successively merged at greater distancesinto larger and fewer clusters. The final PBRs depend on the Si cut-off that is set up.Based on our observations of a representative group of families we set up an empiricalmaximum similarity cut-off value of 0.4. We pre-calculated the results for Si cut-offs at 0,0.1, 0.2, 0.3 and 0.4 to offer a range of values that allow flexibility in the final analysis ofPBRs. The SCOWLP web application offers the possibility to display the classification atany of these cut-off values. Our classification clustered 160,000 contacting domains from2,561 families in 9,334 binding regions. About 65% of the families contain more than onebinding region. These values are obtained for similarity zero and may vary depending onthe similarity cut-off applied.3982.6 Interface DefinitionsIn order to differentiate binding regions having single-interfaces from multi-interfaces, weidentified in each binding region the partner for each contacting domain. Each bindingregion was divided in sub-clusters when there were different domain families interactingin the same binding region. This resulted in a total of 10,300 interfaces. The classificationshows a 78% of the binding regions having a single-interface and the rest having mainly 2or 3 interfaces per region. These numbers have to be carefully interpreted by taking intoaccount the limitation of the structural information contained in the PDB (i.e. 1,715 bind-ing regions contain a unique member in the PDB and therefore only one known interfaceper binding region).3 ConclusionsSCOWLP database contains detailed interacting information of protein interfaces and thehierarchical classification of PBRs. It represents a framework to study protein interfacesand for comparative analysis of protein family binding regions. This comparison can beperformed at atomic level and allows the user to study interactome conservation and vari-ability. The new SCOWLP classification may be of great utility for reconstruction of pro-tein complexes, understanding protein networks and ligand design. The web application isavailable at http://www.scowlp.org.References1. J. Teyra, A. Doms, M. Schroeder, M. T. Pisabarro, SCOWLP: a web-based databasefor detailed characterization and visualization of protein interfaces, BMC Bioinfor-matics. 7, 1004, 2006.2. J. Teyra, M. Paszkowski-Rogacz, G. Anders, M. T. Pisabarro, SCOWLP classification:structural comparison and analysis of protein binding regions, BMC Bioinformatics.9, 9, 2008.3. J. Teyra, M. T. Pisabarro, Characterization of interfacial solvent in protein complexesand contribution of wet spots to the interface description, Proteins 67, 2577–2587,2007.4. http://scop.mrc-lmb.cam.ac.uk.5. D. Lupyan D, A. Leo-Macias A, A. R. Ortiz, A new progressive-iterative algorithmfor multiple structure alignment, Bioinformatics 21, 3255–3263, 2005.6. A. R. Ortiz, C. E. Strauss, O. C. Olmea, MAMMOTH (matching molecular modelsobtained from theory): an automated method for model comparison, Protein Sci 11,2606–2621, 2002.399

Analysis and Classification of the Structural Interactome

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Analysis and Classification of the Structural Interactome

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