Macromolecular Modeling Blog ™

NAR just published the new special “Database” issue for 2010. We collected 23 papers that might be of interest to structural biologists, modelers and other protein people.

3D-footprint: a database for the structural analysis of protein–DNA complexes

Bruno Contreras-Moreira^1,2,3,*

¹Estación Experimental de Aula Dei, Consejo Superior de Investigaciones Científicas, ²Fundación ARAID, Paseo María Agustín 36, Zaragoza, Spain and ³Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, Mexico

*To whom correspondence should be addressed. Tel: +34 976716089; Email: bcontreras@eead.csic.es

Received August 14, 2009. Revised September 2, 2009. Accepted September 3, 2009.

3D-footprint is a living database, updated and curated on a weekly basis, which provides estimates of binding specificity for all protein–DNA complexes available at the Protein Data Bank. The web interface allows the user to: (i) browse DNA-binding proteins by keyword; (ii) find proteins that recognize a similar DNA motif and (iii) BLAST similar DNA-binding proteins, highlighting interface residues in the resulting alignments. Each complex in the database is dissected to draw interface graphs and footprint logos, and two complementary algorithms are employed to characterize binding specificity. Moreover, oligonucleotide sequences extracted from literature abstracts are reported in order to show the range of variant sites bound by each protein and other related proteins. Benchmark experiments, including comparisons with expert-curated databases RegulonDB and TRANSFAC, support the quality of structure-based estimates of specificity. The relevant content of the database is available for download as flat files and it is also possible to use the 3D-footprint pipeline to analyze protein coordinates input by the user. 3D-footprint is available at http://floresta.eead.csic.es/3dfootprint with demo buttons and a comprehensive tutorial that illustrates the main uses of this resource.

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PROSITE, a protein domain database for functional characterization and annotation

Christian J. A. Sigrist^1,*, Lorenzo Cerutti¹, Edouard de Castro¹, Petra S. Langendijk-Genevaux¹, Virginie Bulliard¹, Amos Bairoch^1,2 and Nicolas Hulo¹

¹Swiss Institute of Bioinformatics (SIB), Centre Médical Universitaire and ²Structural Biology and Bioinformatics Department, University of Geneva, 1 rue Michel Servet, CH-1211 Geneva 4, Switzerland

*To whom correspondence should be addressed. Tel: +41 22 379 58 68; Fax: +41 22 379 58 58; Email: christian.sigrist@isb-sib.ch

Received September 3, 2009. Revised October 2, 2009. Accepted October 2, 2009.

PROSITE consists of documentation entries describing protein domains, families and functional sites, as well as associated patterns and profiles to identify them. It is complemented by ProRule, a collection of rules based on profiles and patterns, which increases the discriminatory power of these profiles and patterns by providing additional information about functionally and/or structurally critical amino acids. PROSITE is largely used for the annotation of domain features of UniProtKB/Swiss-Prot entries. Among the 983 (DNA-binding) domains, repeats and zinc fingers present in Swiss-Prot (release 57.8 of 22 September 2009), 696 (70%) are annotated with PROSITE descriptors using information from ProRule. In order to allow better functional characterization of domains, PROSITE developments focus on subfamily specific profiles and a new profile building method giving more weight to functionally important residues. Here, we describe AMSA, an annotated multiple sequence alignment format used to build a new generation of generalized profiles, the migration of ScanProsite to Vital-IT, a cluster of 633 CPUs, and the adoption of the Distributed Annotation System (DAS) to facilitate PROSITE data integration and interchange with other sources. The latest version of PROSITE (release 20.54, of 22 September 2009) contains 1308 patterns, 863 profiles and 869 ProRules. PROSITE is accessible at: http://www.expasy.org/prosite/.

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ELM: the status of the 2010 eukaryotic linear motif resource

Cathryn M. Gould¹, Francesca Diella¹, Allegra Via², Pål Puntervoll³, Christine Gemünd¹, Sophie Chabanis-Davidson¹, Sushama Michael¹, Ahmed Sayadi², Jan Christian Bryne^3,4, Claudia Chica¹, Markus Seiler¹, Norman E. Davey¹, Niall Haslam¹, Robert J. Weatheritt¹, Aidan Budd¹, Tim Hughes⁵, Jakub Pa⁶, Leszek Rychlewski⁶, Gilles Travé⁷, Rein Aasland⁵, Manuela Helmer-Citterich⁸, Rune Linding⁹ and Toby J. Gibson^1,*

¹Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany, ²Biocomputing Group, Department of Biochemical Sciences, ‘A. Rossi-Fanelli’, Sapienza Universita’ di Roma, P.le Aldo Moro, 5, 00185 Rome, Italy, ³Computational Biology Unit, Bergen Centre for Computational Science, Høyteknologisenteret, Thormøhlensgate 55, ⁴Sars Centre for Marine Molecular Biology, University of Bergen, 5008 Bergen, ⁵Department of Molecular Biology, University of Bergen, HIB, Thormøhlensgt. 55, 5020 Bergen, Norway, ⁶BioInfoBank Institute, Limanowskiego 24A16 60-744, Pozna, Poland, ⁷ESBS, 1, Bld Sébastien Brandt, BP10413, 67412 Illkirch, France, ⁸Centre for Molecular Bioinformatics, Department of Biology, University of Rome ‘Tor Vergata’, Via della Ricerca Scientifica, 00133 Rome, Italy and ⁹Cellular & Molecular Logic Team, The Institute of Cancer Research (ICR), Section of Cell and Molecular Biology, SW3 6JB London, UK

*To whom correspondence should be addressed. Tel: +49 6221 387398; Fax: +49 6221 387517; Email: gibson@embl-heidelberg.de

Received September 14, 2009. Revised October 16, 2009. Accepted October 19, 2009.

Linear motifs are short segments of multidomain proteins that provide regulatory functions independently of protein tertiary structure. Much of intracellular signalling passes through protein modifications at linear motifs. Many thousands of linear motif instances, most notably phosphorylation sites, have now been reported. Although clearly very abundant, linear motifs are difficult to predict de novo in protein sequences due to the difficulty of obtaining robust statistical assessments. The ELM resource at http://elm.eu.org/ provides an expanding knowledge base, currently covering 146 known motifs, with annotation that includes >1300 experimentally reported instances. ELM is also an exploratory tool for suggesting new candidates of known linear motifs in proteins of interest. Information about protein domains, protein structure and native disorder, cellular and taxonomic contexts is used to reduce or deprecate false positive matches. Results are graphically displayed in a ‘Bar Code’ format, which also displays known instances from homologous proteins through a novel ‘Instance Mapper’ protocol based on PHI-BLAST. ELM server output provides links to the ELM annotation as well as to a number of remote resources. Using the links, researchers can explore the motifs, proteins, complex structures and associated literature to evaluate whether candidate motifs might be worth experimental investigation.

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The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.

Present address: Christine Gemünd, Cellzome AG, Meyerhofstrasse 1, 69117 Heidelberg, Germany.

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MeMotif: a database of linear motifs in -helical transmembrane proteins

Annalisa Marsico^*, Kerstin Scheubert, Anne Tuukkanen, Andreas Henschel, Christof Winter, Rainer Winnenburg and Michael Schroeder

Bioinformatics Department, Biotechnology Center, TU Dresden, Tatzberg 47/49, 01307 Dresden, Germany

* To whom correspondence should be addressed. Tel: +49 (0) 35146340067; Email: annalisa@biotec.tu-dresden.de

Received September 1, 2009. Revised October 22, 2009. Accepted October 23, 2009.

Membrane proteins are important for many processes in the cell and used as main drug targets. The increasing number of high-resolution structures available makes for the first time a characterization of local structural and functional motifs in -helical transmembrane proteins possible. MeMotif (http://projects.biotec.tu-dresden.de/memotif) is a database and wiki which collects more than 2000 known and novel computationally predicted linear motifs in -helical transmembrane proteins. Motifs are fully described in terms of several structural and functional features and editable. Motifs contained in MeMotif can be used in different biological applications, from the identification of biochemically important functional residues which are candidates for mutagenesis experiments to the improvement of tools for transmembrane protein modeling.

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The Pfam protein families database

Robert D. Finn^1,*, Jaina Mistry¹, John Tate¹, Penny Coggill¹, Andreas Heger², Joanne E. Pollington¹, O. Luke Gavin¹, Prasad Gunasekaran¹, Goran Ceric³, Kristoffer Forslund⁴, Liisa Holm⁵, Erik L. L. Sonnhammer⁴, Sean R. Eddy³ and Alex Bateman¹

¹Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, ²Department of Physiology, Anatomy and Genetics, MRC Functional Genomics Unit, University of Oxford, Oxford, UK, ³Janelia Farm Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA, ⁴Stockholm Bioinformatics Center, Albanova, Stockholm University, SE-10691 Stockholm, Sweden and ⁵Institute of Biotechnology and Department of Biological and Environmental Sciences, University of Helsinki, PO Box 56 (Viikinkaari 5), 00014 Helsinki, Finland

*To whom correspondence should be addressed. Tel: +44 1223 495330; Fax: +44 1223 494919; Email: rdf@sanger.ac.uk

Received October 12, 2009. Accepted October 15, 2009.

Pfam is a widely used database of protein families and domains. This article describes a set of major updates that we have implemented in the latest release (version 24.0). The most important change is that we now use HMMER3, the latest version of the popular profile hidden Markov model package. This software is 100 times faster than HMMER2 and is more sensitive due to the routine use of the forward algorithm. The move to HMMER3 has necessitated numerous changes to Pfam that are described in detail. Pfam release 24.0 contains 11 912 families, of which a large number have been significantly updated during the past two years. Pfam is available via servers in the UK (http://pfam.sanger.ac.uk/), the USA (http://pfam.janelia.org/) and Sweden (http://pfam.sbc.su.se/).

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3DNALandscapes: a database for exploring the conformational features of DNA

Guohui Zheng¹, Andrew V. Colasanti¹, Xiang-Jun Lu^1,2 and Wilma K. Olson^1,*

¹Department of Chemistry & Chemical Biology, BioMaPS Institute for Quantitative Biology, Rutgers, the State University of New Jersey, Wright-Rieman Laboratories, 610 Taylor Road, Piscataway, NJ 08854 and ²Department of Biological Sciences, Center for Computational Biology and Bioinformatics, Columbia University, New York, NY 10027, USA

*To whom correspondence should be addressed. Tel: +1 732 445 3993; Fax: +1 732 445 5958; Email: wilma.olson@rutgers.edu

Received August 15, 2009. Revised October 10, 2009. Accepted October 13, 2009.

3DNALandscapes, located at: http://3DNAscapes.rutgers.edu, is a new database for exploring the conformational features of DNA. In contrast to most structural databases, which archive the Cartesian coordinates and/or derived parameters and images for individual structures, 3DNALandscapes enables searches of conformational information across multiple structures. The database contains a wide variety of structural parameters and molecular images, computed with the 3DNA software package and known to be useful for characterizing and understanding the sequence-dependent spatial arrangements of the DNA sugar-phosphate backbone, sugar-base side groups, base pairs, base-pair steps, groove structure, etc. The data comprise all DNA-containing structures—both free and bound to proteins, drugs and other ligands—currently available in the Protein Data Bank. The web interface allows the user to link, report, plot and analyze this information from numerous perspectives and thereby gain insight into DNA conformation, deformability and interactions in different sequence and structural contexts. The data accumulated from known, well-resolved DNA structures can serve as useful benchmarks for the analysis and simulation of new structures. The collective data can also help to understand how DNA deforms in response to proteins and other molecules and undergoes conformational rearrangements.

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NNDB: the nearest neighbor parameter database for predicting stability of nucleic acid secondary structure

Douglas H. Turner¹ and David H. Mathews^2,*

¹Department of Chemistry and Center for RNA Biology, Box 0216, University of Rochester, Rochester, NY 14627-0216 and ²Department of Biochemistry and Biophysics and Center for RNA Biology, Box 712, University of Rochester Medical Center, Rochester, NY 14642, USA

*To whom correspondence should be addressed. Tel: +1 585 275 1734; Fax: +1 585 275 6007; Email: david_mathews@urmc.rochester.edu

Received August 17, 2009. Revised October 4, 2009. Accepted October 6, 2009.

The Nearest Neighbor Database (NNDB, http://rna.urmc.rochester.edu/NNDB) is a web-based resource for disseminating parameter sets for predicting nucleic acid secondary structure stabilities. For each set of parameters, the database includes the set of rules with descriptive text, sequence-dependent parameters in plain text and html, literature references to experiments and usage tutorials. The initial release covers parameters for predicting RNA folding free energy and enthalpy changes.

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ComSin: database of protein structures in bound (complex) and unbound (single) states in relation to their intrinsic disorder

Michail Yu. Lobanov¹, Benjamin A. Shoemaker², Sergiy O. Garbuzynskiy¹, Jessica H. Fong², Anna R. Panchenko² and Oxana V. Galzitskaya^1,*

¹Institute of Protein Research, Russian Academy of Sciences, Pushchino, Moscow Region, Russia and ²National Center for Biotechnology Information, NIH, Bethesda, MD, USA

*To whom correspondence should be addressed. Tel/Fax: +7-495-6327871; Email: ogalzit@vega.protres.ru

Received August 14, 2009. Revised October 9, 2009. Accepted October 13, 2009.

Most of the proteins in a cell assemble into complexes to carry out their function. In this work, we have created a new database (named ComSin) of protein structures in bound (complex) and unbound (single) states to provide a researcher with exhaustive information on structures of the same or homologous proteins in bound and unbound states. From the complete Protein Data Bank (PDB), we selected 24 910 pairs of protein structures in bound and unbound states, and identified regions of intrinsic disorder. For 2448 pairs, the proteins in bound and unbound states are identical, while 7129 pairs have sequence identity 90% or larger. The developed server enables one to search for proteins in bound and unbound states with several options including sequence similarity between the corresponding proteins in bound and unbound states, and validation of interaction interfaces of protein complexes. Besides that, through our web server, one can obtain necessary information for studying disorder-to-order and order-to-disorder transitions upon complex formation, and analyze structural differences between proteins in bound and unbound states. The database is available at http://antares.protres.ru/comsin/.

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fPOP: footprinting functional pockets of proteins by comparative spatial patterns

Yan Yuan Tseng^1,*, Z. Jeffrey Chen² and Wen-Hsiung Li^1,3

¹Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, ²Center for Computational Biology and Bioinformatics, University of Texas at Austin, One University Station, C4500, Austin, TX 78712, USA and ³Biodiversity Research Center, Academia Sinica, Taipei 115, Taiwan

*To whom correspondence should be addressed. Tel: +1 773 834 3965; Fax: +1 773 702 9740; Email: ytseng3@uchicago.edu

Received August 11, 2009. Revised September 21, 2009. Accepted October 6, 2009.

fPOP (footprinting Pockets Of Proteins, http://pocket.uchicago.edu/fpop/) is a relational database of the protein functional surfaces identified by analyzing the shapes of binding sites in 42 700 structures, including both holo and apo forms. We previously used a purely geometric method to extract the spatial patterns of functional surfaces (split pockets) in 19 000 bound structures and constructed a database, SplitPocket (http://pocket.uchicago.edu/). These functional surfaces are now used as spatial templates to predict the binding surfaces of unbound structures. To conduct a shape comparison, we use the Smith–Waterman algorithm to footprint an unbound pocket fragment with those of the functional surfaces in SplitPocket. The pairwise alignment of the unbound and bound pocket fragments is used to evaluate the local structural similarity via geometric matching. The final results of our large-scale computation, including 90 000 identified or predicted functional surfaces, are stored in fPOP. This database provides an easily accessible resource for studying functional surfaces, assessing conformational changes between bound and unbound forms and analyzing functional divergence. Moreover, it may facilitate the exploration of the physicochemical textures of molecules and the inference of protein function. Finally, our approach provides a framework for classification of proteins into families on the basis of their functional surfaces.

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IMGT/3Dstructure-DB and IMGT/DomainGapAlign: a database and a tool for immunoglobulins or antibodies, T cell receptors, MHC, IgSF and MhcSF

François Ehrenmann¹, Quentin Kaas¹ and Marie-Paule Lefranc^1,2,*

¹IMGT^®, the international imMunoGeneTics information system^®, Université Montpellier 2, Laboratoire d’I;mmunoGénétique Moléculaire LIGM, Institut de Génétique Humaine IGH, UPR CNRS 1142, 141 rue de la Cardonille, 34396 Montpellier cedex 5 and ²Institut Universitaire de France, 103 Bd St Michel, 75005 Paris, France

*To whom correspondence should be addressed. Tel: +33 4 99 61 99 65; Fax: +33 4 99 61 99 01; Email: marie-paule.lefranc@igh.cnrs.fr

Received September 11, 2009. Revised October 9, 2009. Accepted October 12, 2009.

IMGT/3Dstructure-DB is the three-dimensional (3D) structure database of IMGT^®, the international ImMunoGenetics information system^® that is acknowledged as the global reference in immunogenetics and immunoinformatics. IMGT/3Dstructure-DB contains 3D structures of immunoglobulins (IG) or antibodies, T cell receptors (TR), major histocompatibility complex (MHC) proteins, antigen receptor/antigen complexes (IG/Ag, TR/peptide/MHC) of vertebrates; 3D structures of related proteins of the immune system (RPI) of vertebrates and invertebrates, belonging to the immunoglobulin and MHC superfamilies (IgSF and MhcSF, respectively) and found in complexes with IG, TR or MHC. IMGT/3Dstructure-DB data are annotated according to the IMGT criteria, using IMGT/DomainGapAlign, and based on the IMGT-ONTOLOGY concepts and axioms. IMGT/3Dstructure-DB provides IMGT gene and allele identification (CLASSIFICATION), region and domain delimitations (DESCRIPTION), amino acid positions according to the IMGT unique numbering (NUMEROTATION) that are used in IMGT/3Dstructure-DB cards, results of contact analysis and renumbered flat files. In its Web version, the IMGT/DomainGapAlign tool analyses amino acid sequences, per domain. Coupled to the IMGT/Collier-de-Perles tool, it provides an invaluable help for antibody engineering and humanization design based on complementarity determining region (CDR) grafting as it precisely defines the standardized framework regions (FR-IMGT) and CDR-IMGT. IMGT/3Dstructure-DB and IMGT/DomainGapAlign are freely available at http://www.imgt.org.

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Present address: Quentin Kaas, The University of Queensland, Institute for Molecular Bioscience, Brisbane QLD 4072, Australia.

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PDBe: Protein Data Bank in Europe

S. Velankar^*, C. Best, B. Beuth, C. H. Boutselakis, N. Cobley, A. W. Sousa Da Silva, D. Dimitropoulos, A. Golovin, M. Hirshberg, M. John, E. B. Krissinel, R. Newman, T. Oldfield, A. Pajon, C. J. Penkett, J. Pineda-Castillo, G. Sahni, S. Sen, R. Slowley, A. Suarez-Uruena, J. Swaminathan, G. van Ginkel, W. F. Vranken, K. Henrick and G. J. Kleywegt^*

Protein Databank in Europe, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK

*To whom correspondence should be addressed. Tel: +44 1223 494646; Fax: +44 1223 494468; Email: sameer@ebi.ac.uk

Correspondence may also be addressed to G. J. Kleywegt. Tel: +44 1223 492663; Fax: +44 1223 494468; Email: gerard@ebi.ac.uk

Received September 15, 2009. Accepted October 7, 2009.

The Protein Data Bank in Europe (PDBe) (http://www.ebi.ac.uk/pdbe/) is actively working with its Worldwide Protein Data Bank partners to enhance the quality and consistency of the international archive of bio-macromolecular structure data, the Protein Data Bank (PDB). PDBe also works closely with its collaborators at the European Bioinformatics Institute and the scientific community around the world to enhance its databases and services by adding curated and actively maintained derived data to the existing structural data in the PDB. We have developed a new database infrastructure based on the remediated PDB archive data and a specially designed database for storing information on interactions between proteins and bound molecules. The group has developed new services that allow users to carry out simple textual queries or more complex 3D structure-based queries. The newly designed ‘PDBeView Atlas pages’ provide an overview of an individual PDB entry in a user-friendly layout and serve as a starting point to further explore the information available in the PDBe database. PDBe’s active involvement with the X-ray crystallography, Nuclear Magnetic Resonance spectroscopy and cryo-Electron Microscopy communities have resulted in improved tools for structure deposition and analysis.

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Present address: R. Newman, A. Pajon, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1HH, UK

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PDBselect 1992–2009 and PDBfilter-select

Sven Griep and Uwe Hobohm^*

University of Applied Sciences Giessen, Bioinformatics, D-35390 Giessen, Germany

*To whom correspondence should be addressed. Tel: 0641 3092580; Fax: 0641 3092549; Email: uwe.hobohm@tg.fh-giessen.de

Received August 1, 2009. Accepted September 4, 2009.

PDBselect (http://bioinfo.tg.fh-giessen.de/pdbselect/) is a list of representative protein chains with low mutual sequence identity selected from the protein data bank (PDB) to enable unbiased statistics. The list increased from 155 chains in 1992 to more than 4500 chains in 2009. PDBfilter-select is an online service to generate user-defined selections.

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Protein Geometry Database: a flexible engine to explore backbone conformations and their relationships to covalent geometry

Donald S. Berkholz¹, Peter B. Krenesky², John R. Davidson² and P. Andrew Karplus^1,*

¹Department of Biochemistry and Biophysics, Oregon State University, 2011 ALS and ²Open Source Lab, Oregon State University, B211 Kerr Admin, Corvallis OR 97331, USA

*To whom correspondence should be addressed. Tel: +1 541 737 3200; Fax: +1 541 737 0481; Email: karplusp@science.oregonstate.edu

Received August 14, 2009. Revised October 14, 2009. Accepted October 19, 2009.

The backbone bond lengths, bond angles, and planarity of a protein are influenced by the backbone conformation (,), but no tool exists to explore these relationships, leaving this area as a reservoir of untapped information about protein structure and function. The Protein Geometry Database (PGD) enables biologists to easily and flexibly query information about the conformation alone, the backbone geometry alone, and the relationships between them. The capabilities the PGD provides are valuable for assessing the uniqueness of observed conformational or geometric features in protein structure as well as discovering novel features and principles of protein structure. The PGD server is available at http://pgd.science.oregonstate.edu/ and the data and code underlying it are freely available to use and extend.

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PTGL: a database for secondary structure-based protein topologies

Patrick May^1,*, Annika Kreuchwig², Thomas Steinke³ and Ina Koch^4,5,*

¹Max Planck Institute for Molecular Plant Physiology, Bioinformatics, Am Muehlenberg 1, 14476 Potsdam-Golm, ²Leibniz-Institut fuer Molekulare Pharmakologie, Structural Bioinformatics, Robert-Roessle-Strasse 10, 13125 Berlin, ³Zuse Institute Berlin, Computer Science Research, Takustrasse 7, 14195 Berlin, ⁴Beuth University for Technology Berlin, FB VI, Bioinformatics, Seestrasse 64, 13347 Berlin and ⁵Max Planck Institute for Molecular Genetics, Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin, Germany

*To whom correspondence should be addressed. Fax: +49 (0)331 5678136; Email: may@mpimp-golm.mpg.de

Correspondence may also be addressed to Ina Koch. Tel: +49 (0)30/8413-1168; Fax: +49 (0)30/8413-1152; Email: koch_i@molgen.mpg.de

Received August 15, 2009. Revised October 14, 2009. Accepted October 15, 2009.

With growing amount of experimental data, the number of known protein structures also increases continuously. Classification of protein structures helps to understand relationships between protein structure and function. The main classification methods based on secondary structures are SCOP, CATH and TOPS, which all classify under different aspects, and therefore can lead to different results. We developed a mathematically unique representation of protein structure topologies at a higher abstraction level providing new aspects of classification and enabling for a fast search through the data. Protein Topology Graph Library (PTGL; http://ptgl.zib.de) aims at providing a database on protein secondary structure topologies, including search facilities, the visualization as intuitive topology diagrams as well as in the 3D structure, and additional information. Secondary structure-based protein topologies are represented uniquely as undirected labeled graphs in four different ways allowing for exploration under different aspects. The linear notations, and the 2D and 3D diagrams of each notation facilitate a deeper understanding of protein topologies. Several search functions for topologies and sub-topologies, BLAST search possibility, and links to SCOP, CATH and PDBsum support individual and large-scale investigation of protein structures. Currently, PTGL comprises topologies of 54 859 protein structures. Main structural patterns for common structural motifs like TIM-barrel or Jelly Roll are pre-implemented, and can easily be searched.

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CORUM: the comprehensive resource of mammalian protein complexes—2009

Andreas Ruepp^1,*, Brigitte Waegele^1,2, Martin Lechner¹, Barbara Brauner¹, Irmtraud Dunger-Kaltenbach¹, Gisela Fobo¹, Goar Frishman¹, Corinna Montrone¹ and H.-Werner Mewes^1,2

¹Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München—German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg and ²Technische Universität München, Chair of Genome Oriented Bioinformatics, Center of Life and Food Science, D-85350 Freising-Weihenstephan, Germany

*To whom correspondence should be addressed. Tel: +49 3187 3189; Fax: +49 3187 3585; Email: andreas.ruepp@helmholtz-muenchen.de

Received September 10, 2009. Accepted October 7, 2009.

CORUM is a database that provides a manually curated repository of experimentally characterized protein complexes from mammalian organisms, mainly human (64%), mouse (16%) and rat (12%). Protein complexes are key molecular entities that integrate multiple gene products to perform cellular functions. The new CORUM 2.0 release encompasses 2837 protein complexes offering the largest and most comprehensive publicly available dataset of mammalian protein complexes. The CORUM dataset is built from 3198 different genes, representing 16% of the protein coding genes in humans. Each protein complex is described by a protein complex name, subunit composition, function as well as the literature reference that characterizes the respective protein complex. Recent developments include mapping of functional annotation to Gene Ontology terms as well as cross-references to Entrez Gene identifiers. In addition, a ‘Phylogenetic Conservation’ analysis tool was implemented that analyses the potential occurrence of orthologous protein complex subunits in mammals and other selected groups of organisms. This allows one to predict the occurrence of protein complexes in different phylogenetic groups. CORUM is freely accessible at (http://mips.helmholtz-muenchen.de/genre/proj/corum/index.html).

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GWIDD: Genome-wide protein docking database

Petras J. Kundrotas¹, Zhengwei Zhu¹ and Ilya A. Vakser^1,2,*

¹Center for Bioinformatics and ²Department of Molecular Biosciences, 2030 Becker Drive, The University of Kansas, Lawrence, KS 66047, USA

*To whom correspondence should be addressed. Tel: +1 785 584 1057; Fax: +1 785 864 5558; Email: vakser@ku.edu

Received August 10, 2009. Revised September 27, 2009. Accepted October 12, 2009.

Structural information on interacting proteins is important for understanding life processes at the molecular level. Genome-wide docking database is an integrated resource for structural studies of protein–protein interactions on the genome scale, which combines the available experimental data with models obtained by docking techniques. Current database version (August 2009) contains 25 559 experimental and modeled 3D structures for 771 organisms spanned over the entire universe of life from viruses to humans. Data are organized in a relational database with user-friendly search interface allowing exploration of the database content by a number of parameters. Search results can be interactively previewed and downloaded as PDB-formatted files, along with the information relevant to the specified interactions. The resource is freely available at http://gwidd.bioinformatics.ku.edu.

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Inferred Biomolecular Interaction Server—a web server to analyze and predict protein interacting partners and binding sites

Benjamin A. Shoemaker, Dachuan Zhang, Ratna R. Thangudu, Manoj Tyagi, Jessica H. Fong, Aron Marchler-Bauer, Stephen H. Bryant, Thomas Madej^* and Anna R. Panchenko^*

National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA

*To whom correspondence should be addressed. Tel: +1 301 435 5891; Fax: +1 301 480 4637; Email: panch@ncbi.nlm.nih.gov Correspondence may also be addressed to Thomas Madej. Tel: +1 301 435 5998; Fax: +1 301 480 4637; Email: madej@ncbi.nlm.nih.gov

Received August 15, 2009. Revised September 16, 2009. Accepted September 21, 2009.

IBIS is the NCBI Inferred Biomolecular Interaction Server. This server organizes, analyzes and predicts interaction partners and locations of binding sites in proteins. IBIS provides annotations for different types of binding partners (protein, chemical, nucleic acid and peptides), and facilitates the mapping of a comprehensive biomolecular interaction network for a given protein query. IBIS reports interactions observed in experimentally determined structural complexes of a given protein, and at the same time IBIS infers binding sites/interacting partners by inspecting protein complexes formed by homologous proteins. Similar binding sites are clustered together based on their sequence and structure conservation. To emphasize biologically relevant binding sites, several algorithms are used for verification in terms of evolutionary conservation, biological importance of binding partners, size and stability of interfaces, as well as evidence from the published literature. IBIS is updated regularly and is freely accessible via http://www.ncbi.nlm.nih.gov/Structure/ibis/ibis.html.

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The IntAct molecular interaction database in 2010

B. Aranda¹, P. Achuthan¹, Y. Alam-Faruque¹, I. Armean, A. Bridge², C. Derow¹, M. Feuermann², A. T. Ghanbarian¹, S. Kerrien¹, J. Khadake¹, J. Kerssemakers¹, C. Leroy¹, M. Menden¹, M. Michaut¹, L. Montecchi-Palazzi¹, S. N. Neuhauser¹, S. Orchard¹, V. Perreau³, B. Roechert², K. van Eijk¹ and H. Hermjakob^1,*

¹EMBL Outstation, European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus Hinxton, Cambridge, CB10 1SD, UK, ²Swiss Institute of Bioinformatics Geneva, Switzerland and ³Centre for Neuroscience, University of Melbourne, Australia

*To whom correspondence should be addressed. Tel: +44 1223 49 4671; Fax: +44 1223 49 4468; Email: hhe@ebi.ac.uk

Received September 11, 2009. Revised October 1, 2009. Accepted October 2, 2009.

IntAct is an open-source, open data molecular interaction database and toolkit. Data is abstracted from the literature or from direct data depositions by expert curators following a deep annotation model providing a high level of detail. As of September 2009, IntAct contains over 200.000 curated binary interaction evidences. In response to the growing data volume and user requests, IntAct now provides a two-tiered view of the interaction data. The search interface allows the user to iteratively develop complex queries, exploiting the detailed annotation with hierarchical controlled vocabularies. Results are provided at any stage in a simplified, tabular view. Specialized views then allows ‘zooming in’ on the full annotation of interactions, interactors and their properties. IntAct source code and data are freely available at http://www.ebi.ac.uk/intact.

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MINT, the molecular interaction database: 2009 update

Arnaud Ceol^1,*, Andrew Chatr Aryamontri¹, Luana Licata¹, Daniele Peluso², Leonardo Briganti¹, Livia Perfetto¹, Luisa Castagnoli¹ and Gianni Cesareni^1,2

¹Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 and ²IRCCS Fondazione Santa Lucia, 00143 Rome

*To whom correspondence should be addressed. Tel: +39 067 2594315; Fax: +39 062 023500; Email: arnaud.ceol@uniroma2.it

Received September 11, 2009. Revised October 13, 2009. Accepted October 15, 2009.

MINT (http://mint.bio.uniroma2.it/mint) is a public repository for molecular interactions reported in peer-reviewed journals. Since its last report, MINT has grown considerably in size and evolved in scope to meet the requirements of its users. The main changes include a more precise definition of the curation policy and the development of an enhanced and user-friendly interface to facilitate the analysis of the ever-growing interaction dataset. MINT has adopted the PSI-MI standards for the annotation and for the representation of molecular interactions and is a member of the IMEx consortium.

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The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.

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The Negatome database: a reference set of non-interacting protein pairs

Pawel Smialowski^1,2, Philipp Pagel^1,2, Philip Wong¹, Barbara Brauner¹, Irmtraud Dunger¹, Gisela Fobo¹, Goar Frishman¹, Corinna Montrone¹, Thomas Rattei², Dmitrij Frishman^1,2,* and Andreas Ruepp¹

¹Institute for Bioinformatics and Systems Biology/MIPS, HMGU—German Research Center for Environmental Health Ingolstaedter Landstrasse 1, 85764 Neuherberg and ²Germany Department of Genome Oriented Bioinformatics, Technische Universitaet Muenchen Wissenschaftszentrum Weihenstephan, 85350 Freising, Germany

*To whom correspondence should be addressed. Tel: +49 8161 712134; Fax: +49 8161 712186; Email: d.frishman@wzw.tum.de

Received October 14, 2009. Revised October 19, 2009. Accepted October 20, 2009.

The Negatome is a collection of protein and domain pairs that are unlikely to be engaged in direct physical interactions. The database currently contains experimentally supported non-interacting protein pairs derived from two distinct sources: by manual curation of literature and by analyzing protein complexes with known 3D structure. More stringent lists of non-interacting pairs were derived from these two datasets by excluding interactions detected by high-throughput approaches. Additionally, non-interacting protein domains have been derived from the stringent manual and structural data, respectively. The Negatome is much less biased toward functionally dissimilar proteins than the negative data derived by randomly selecting proteins from different cellular locations. It can be used to evaluate protein and domain interactions from new experiments and improve the training of interaction prediction algorithms. The Negatome database is available at http://mips.helmholtz-muenchen.de/proj/ppi/negatome.

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The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.

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

Peter Vanhee^1,2, Joke Reumers^1,2, Francois Stricher³, Lies Baeten^1,2, Luis Serrano³, Joost Schymkowitz^1,2,* and Frederic Rousseau^1,2,*

¹VIB SWITCH Laboratory, ²Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium, ³EMBL-CRG Systems Biology Unit, CRG-Centre de Regulacio Genomica, Dr Aiguader 88, 08003 Barcelona and ⁴ICREA. Institucio Catala de Recerca i Estudis Avancats. Passeig Lluís Companys, 23 08010 Barcelona, Spain

*To whom correspondence should be addressed. Tel: +32 2 629 14 25; Fax: +32 2 629 19 63; Email: joost.schymkowitz@switch.vib-vub.be

Correspondence may also be addressed to Frederic Rousseau. Email: frederic.rousseau@switch.vib-vub.be

Received August 15, 2009. Revised October 1, 2009. Accepted October 6, 2009.

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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Protein Structure Initiative Material Repository: an open shared public resource of structural genomics plasmids for the biological community

Catherine Y. Cormier^1,2,3, Stephanie E. Mohr^3,4, Dongmei Zuo^2,3, Yanhui Hu^2,3, Andreas Rolfs^2,3, Jason Kramer^1,2,3, Elena Taycher^2,3, Fontina Kelley^2,3, Michael Fiacco^1,2, Greggory Turnbull¹ and Joshua LaBaer^1,2,3,*

¹Arizona State University, Biodesign Institute, Virginia G. Piper Center for Personalized Diagnostics, 1001 S. McAllister Dr. Tempe, AZ 85287-6401, ²Protein Structure Initiative Material Repository, Arizona State University, 1001 S. McAllister Dr. Tempe, AZ 85287-6401, ³Harvard Medical School, 240 Longwood Avenue, Boston, ⁴Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA

*To whom correspondence should be addressed. Tel: +1 480 965 2805; Fax: +1 480 965 3051; Email: jlabaer@asu.edu

Received September 4, 2009. Revised October 15, 2009. Accepted October 16, 2009.

The Protein Structure Initiative Material Repository (PSI-MR; http://psimr.asu.edu) provides centralized storage and distribution for the protein expression plasmids created by PSI researchers. These plasmids are a resource that allows the research community to dissect the biological function of proteins whose structures have been identified by the PSI. The plasmid annotation, which includes the full length sequence, vector information and associated publications, is stored in a freely available, searchable database called DNASU (http://dnasu.asu.edu). Each PSI plasmid is also linked to a variety of additional resources, which facilitates cross-referencing of a particular plasmid to protein annotations and experimental data. Plasmid samples can be requested directly through the website. We have also developed a novel strategy to avoid the most common concern encountered when distributing plasmids namely, the complexity of material transfer agreement (MTA) processing and the resulting delays this causes. The Expedited Process MTA, in which we created a network of institutions that agree to the terms of transfer in advance of a material request, eliminates these delays. Our hope is that by creating a repository of expression-ready plasmids and expediting the process for receiving these plasmids, we will help accelerate the accessibility and pace of scientific discovery.

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BioNumbers—the database of key numbers in molecular and cell biology

Ron Milo^1,*, Paul Jorgensen^2,3, Uri Moran¹, Griffin Weber⁴ and Michael Springer²

¹Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel, ²Department of Systems Biology, Harvard Medical School, Boston, MA 02445, USA, ³Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada M5S 3E1 and ⁴Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA

*To whom correspondence should be addressed. Tel: +97289344466; Fax: +97289344181; Email: ron.milo@weizmann.ac.il

Received July 16, 2009. Accepted October 2, 2009.

BioNumbers (http://www.bionumbers.hms.harvard.edu) is a database of key numbers in molecular and cell biology—the quantitative properties of biological systems of interest to computational, systems and molecular cell biologists. Contents of the database range from cell sizes to metabolite concentrations, from reaction rates to generation times, from genome sizes to the number of mitochondria in a cell. While always of importance to biologists, having numbers in hand is becoming increasingly critical for experimenting, modeling, and analyzing biological systems. BioNumbers was motivated by an appreciation of how long it can take to find even the simplest number in the vast biological literature. All numbers are taken directly from a literature source and that reference is provided with the number. BioNumbers is designed to be highly searchable and queries can be performed by keywords or browsed by menus. BioNumbers is a collaborative community platform where registered users can add content and make comments on existing data. All new entries and commentary are curated to maintain high quality. Here we describe the database characteristics and implementation, demonstrate its use, and discuss future directions for its development.

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