1
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Dhanda SK, Mahajan S, Paul S, Yan Z, Kim H, Jespersen MC, Jurtz V, Andreatta M, Greenbaum JA, Marcatili P, Sette A, Nielsen M, Peters B. IEDB-AR: immune epitope database-analysis resource in 2019. Nucleic Acids Res 2020; 47:W502-W506. [PMID: 31114900 PMCID: PMC6602498 DOI: 10.1093/nar/gkz452] [Citation(s) in RCA: 202] [Impact Index Per Article: 50.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 05/01/2019] [Accepted: 05/10/2019] [Indexed: 11/13/2022] Open
Abstract
The Immune Epitope Database Analysis Resource (IEDB-AR, http://tools.iedb.org/) is a companion website to the IEDB that provides computational tools focused on the prediction and analysis of B and T cell epitopes. All of the tools are freely available through the public website and many are also available through a REST API and/or a downloadable command-line tool. A virtual machine image of the entire site is also freely available for non-commercial use and contains most of the tools on the public site. Here, we describe the tools and functionalities that are available in the IEDB-AR, focusing on the 10 new tools that have been added since the last report in the 2012 NAR webserver edition. In addition, many of the tools that were already hosted on the site in 2012 have received updates to newest versions, including NetMHC, NetMHCpan, BepiPred and DiscoTope. Overall, this IEDB-AR update provides a substantial set of updated and novel features for epitope prediction and analysis.
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Affiliation(s)
- Sandeep Kumar Dhanda
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Swapnil Mahajan
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Sinu Paul
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Zhen Yan
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Haeuk Kim
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | | | - Vanessa Jurtz
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Massimo Andreatta
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Argentina
| | - Jason A Greenbaum
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Paolo Marcatili
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Alessandro Sette
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.,Department of Medicine, University of California, San Diego, CA 92122, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Argentina
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.,Department of Medicine, University of California, San Diego, CA 92122, USA
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2
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Jensen KK, Rantos V, Jappe EC, Olsen TH, Jespersen MC, Jurtz V, Jessen LE, Lanzarotti E, Mahajan S, Peters B, Nielsen M, Marcatili P. TCRpMHCmodels: Structural modelling of TCR-pMHC class I complexes. Sci Rep 2019; 9:14530. [PMID: 31601838 PMCID: PMC6787230 DOI: 10.1038/s41598-019-50932-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 09/09/2019] [Indexed: 01/30/2023] Open
Abstract
The interaction between the class I major histocompatibility complex (MHC), the peptide presented by the MHC and the T-cell receptor (TCR) is a key determinant of the cellular immune response. Here, we present TCRpMHCmodels, a method for accurate structural modelling of the TCR-peptide-MHC (TCR-pMHC) complex. This TCR-pMHC modelling pipeline takes as input the amino acid sequence and generates models of the TCR-pMHC complex, with a median Cα RMSD of 2.31 Å. TCRpMHCmodels significantly outperforms TCRFlexDock, a specialised method for docking pMHC and TCR structures. TCRpMHCmodels is simple to use and the modelling pipeline takes, on average, only two minutes. Thanks to its ease of use and high modelling accuracy, we expect TCRpMHCmodels to provide insights into the underlying mechanisms of TCR and pMHC interactions and aid in the development of advanced T-cell-based immunotherapies and rational design of vaccines. The TCRpMHCmodels tool is available at http://www.cbs.dtu.dk/services/TCRpMHCmodels/.
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Affiliation(s)
| | - Vasileios Rantos
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.,Centre for Structural Systems Biology (CSSB), DESY and European Molecular Biology Laboratory, Notkestrasse 85, 22607, Hamburg, Germany
| | - Emma Christine Jappe
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.,Evaxion Biotech, Bredgade 34E, 1260, Copenhagen, Denmark
| | - Tobias Hegelund Olsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark
| | | | - Vanessa Jurtz
- Department of Bioinformatics and Data Mining, Novo Nordisk A/S, 2760, Måløv, Denmark
| | - Leon Eyrich Jessen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Esteban Lanzarotti
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Swapnil Mahajan
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.,University of California San Diego, Department of Medicine, La Jolla, CA 92037, USA
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Paolo Marcatili
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.
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3
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Mahajan S, Yan Z, Jespersen MC, Jensen KK, Marcatili P, Nielsen M, Sette A, Peters B. Benchmark datasets of immune receptor-epitope structural complexes. BMC Bioinformatics 2019; 20:490. [PMID: 31601176 PMCID: PMC6785892 DOI: 10.1186/s12859-019-3109-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 09/20/2019] [Indexed: 02/05/2023] Open
Abstract
Background The development of accurate epitope prediction tools is important in facilitating disease diagnostics, treatment and vaccine development. The advent of new approaches making use of antibody and TCR sequence information to predict receptor-specific epitopes have the potential to transform the epitope prediction field. Development and validation of these new generation of epitope prediction methods would benefit from regularly updated high-quality receptor-antigen complex datasets. Results To address the need for high-quality datasets to benchmark performance of these new generation of receptor-specific epitope prediction tools, a webserver called SCEptRe (Structural Complexes of Epitope-Receptor) was created. SCEptRe extracts weekly updated 3D complexes of antibody-antigen, TCR-pMHC and MHC-ligand from the Immune Epitope Database and clusters them based on antigen, receptor and epitope features to generate benchmark datasets. SCEptRe also provides annotated information such as CDR sequences and VDJ genes on the receptors. Users can generate custom datasets based by selecting thresholds for structural quality and clustering parameters (e.g. resolution, R-free factor, antigen or epitope sequence identity) based on their need. Conclusions SCEptRe provides weekly updated, user-customized comprehensive benchmark datasets of immune receptor-epitope structural complexes. These datasets can be used to develop and benchmark performance of receptor-specific epitope prediction tools in the future. SCEptRe is freely accessible at http://tools.iedb.org/sceptre.
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Affiliation(s)
- Swapnil Mahajan
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, 92037, USA.
| | - Zhen Yan
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, 92037, USA
| | | | | | - Paolo Marcatili
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Alessandro Sette
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, 92037, USA.,Department of Medicine, University of California, San Diego, CA, 92093, USA
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, 92037, USA.,Department of Medicine, University of California, San Diego, CA, 92093, USA
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4
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Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res 2019; 45:W24-W29. [PMID: 28472356 PMCID: PMC5570230 DOI: 10.1093/nar/gkx346] [Citation(s) in RCA: 815] [Impact Index Per Article: 163.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Accepted: 04/20/2017] [Indexed: 02/07/2023] Open
Abstract
Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community.
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Affiliation(s)
- Martin Closter Jespersen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby 2800, Denmark
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby 2800, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Paolo Marcatili
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby 2800, Denmark
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5
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Klausen MS, Jespersen MC, Nielsen H, Jensen KK, Jurtz VI, Sønderby CK, Sommer MOA, Winther O, Nielsen M, Petersen B, Marcatili P. NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning. Proteins 2019; 87:520-527. [PMID: 30785653 DOI: 10.1002/prot.25674] [Citation(s) in RCA: 285] [Impact Index Per Article: 57.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 02/01/2019] [Accepted: 02/17/2019] [Indexed: 12/26/2022]
Abstract
The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed. Here, we present NetSurfP-2.0, a novel tool that can predict the most important local structural features with unprecedented accuracy and runtime. NetSurfP-2.0 is sequence-based and uses an architecture composed of convolutional and long short-term memory neural networks trained on solved protein structures. Using a single integrated model, NetSurfP-2.0 predicts solvent accessibility, secondary structure, structural disorder, and backbone dihedral angles for each residue of the input sequences. We assessed the accuracy of NetSurfP-2.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features. We observe a correlation of 80% between predictions and experimental data for solvent accessibility, and a precision of 85% on secondary structure 3-class predictions. In addition to improved accuracy, the processing time has been optimized to allow predicting more than 1000 proteins in less than 2 hours, and complete proteomes in less than 1 day.
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Affiliation(s)
- Michael Schantz Klausen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Martin Closter Jespersen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Henrik Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Vanessa Isabell Jurtz
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Casper Kaae Sønderby
- The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | | | - Ole Winther
- The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark.,Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Bent Petersen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.,Faculty of Applied Sciences, Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Kedah, Malaysia
| | - Paolo Marcatili
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
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6
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Jespersen MC, Mahajan S, Peters B, Nielsen M, Marcatili P. Antibody Specific B-Cell Epitope Predictions: Leveraging Information From Antibody-Antigen Protein Complexes. Front Immunol 2019; 10:298. [PMID: 30863406 PMCID: PMC6399414 DOI: 10.3389/fimmu.2019.00298] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 02/05/2019] [Indexed: 11/13/2022] Open
Abstract
B-cells can neutralize pathogenic molecules by targeting them with extreme specificity using receptors secreted or expressed on their surface (antibodies). This is achieved via molecular interactions between the paratope (i.e., the antibody residues involved in the binding) and the interacting region (epitope) of its target molecule (antigen). Discerning the rules that define this specificity would have profound implications for our understanding of humoral immunogenicity and its applications. The aim of this work is to produce improved, antibody-specific epitope predictions by exploiting features derived from the antigens and their cognate antibodies structures, and combining them using statistical and machine learning algorithms. We have identified several geometric and physicochemical features that are correlated in interacting paratopes and epitopes, used them to develop a Monte Carlo algorithm to generate putative epitopes-paratope pairs, and train a machine-learning model to score them. We show that, by including the structural and physicochemical properties of the paratope, we improve the prediction of the target of a given B-cell receptor. Moreover, we demonstrate a gain in predictive power both in terms of identifying the cognate antigen target for a given antibody and the antibody target for a given antigen, exceeding the results of other available tools.
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Affiliation(s)
- Martin Closter Jespersen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Swapnil Mahajan
- La Jolla Institute for Allergy and Immunology, Center for Infectious Disease, Allergy and Asthma Research, La Jolla, CA, United States
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, Center for Infectious Disease, Allergy and Asthma Research, La Jolla, CA, United States
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Paolo Marcatili
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
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7
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Mahajan S, Vita R, Shackelford D, Lane J, Schulten V, Zarebski L, Jespersen MC, Marcatili P, Nielsen M, Sette A, Peters B. Epitope Specific Antibodies and T Cell Receptors in the Immune Epitope Database. Front Immunol 2018; 9:2688. [PMID: 30515166 PMCID: PMC6255941 DOI: 10.3389/fimmu.2018.02688] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 10/31/2018] [Indexed: 11/13/2022] Open
Abstract
The Immune Epitope Database (IEDB) is a free public resource which catalogs experiments characterizing immune epitopes. To accommodate data from next generation repertoire sequencing experiments, we recently updated how we capture and query epitope specific antibodies and T cell receptors. Specifically, we are now storing partial receptor sequences sufficient to determine CDRs and VDJ gene usage which are commonly identified by repertoire sequencing. For previously captured full length receptor sequencing data, we have calculated the corresponding CDR sequences and gene usage information using IMGT numbering and VDJ gene nomenclature format. To integrate information from receptors defined at different levels of resolution, we grouped receptors based on their host species, receptor type and CDR3 sequence. As of August 2018, we have cataloged sequence information for more than 22,510 receptors in 18,292 receptor groups, shown to bind to more than 2,241 distinct epitopes. These data are accessible as full exports and through a new dedicated query interface. The later combines the new ability to search by receptor characteristics with previously existing capability to search by epitope characteristics such as the infectious agent the epitope is derived from, or the kind of immune response involved in its recognition. We expect that this comprehensive capture of epitope specific immune receptor information will provide new insights into receptor-epitope interactions, and facilitate the development of novel tools that help in the analysis of receptor repertoire data.
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Affiliation(s)
- Swapnil Mahajan
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Randi Vita
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Deborah Shackelford
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Jerome Lane
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Veronique Schulten
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Laura Zarebski
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Martin Closter Jespersen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Paolo Marcatili
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Alessandro Sette
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States.,University of California San Diego, La Jolla, CA, United States
| | - Bjoern Peters
- Center for Infectious Disease, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States.,University of California San Diego, La Jolla, CA, United States
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8
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Ison J, Rapacki K, Ménager H, Kalaš M, Rydza E, Chmura P, Anthon C, Beard N, Berka K, Bolser D, Booth T, Bretaudeau A, Brezovsky J, Casadio R, Cesareni G, Coppens F, Cornell M, Cuccuru G, Davidsen K, Vedova GD, Dogan T, Doppelt-Azeroual O, Emery L, Gasteiger E, Gatter T, Goldberg T, Grosjean M, Grüning B, Helmer-Citterich M, Ienasescu H, Ioannidis V, Jespersen MC, Jimenez R, Juty N, Juvan P, Koch M, Laibe C, Li JW, Licata L, Mareuil F, Mičetić I, Friborg RM, Moretti S, Morris C, Möller S, Nenadic A, Peterson H, Profiti G, Rice P, Romano P, Roncaglia P, Saidi R, Schafferhans A, Schwämmle V, Smith C, Sperotto MM, Stockinger H, Vařeková RS, Tosatto SCE, de la Torre V, Uva P, Via A, Yachdav G, Zambelli F, Vriend G, Rost B, Parkinson H, Løngreen P, Brunak S. Tools and data services registry: a community effort to document bioinformatics resources. Nucleic Acids Res 2015; 44:D38-47. [PMID: 26538599 PMCID: PMC4702812 DOI: 10.1093/nar/gkv1116] [Citation(s) in RCA: 86] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Accepted: 10/13/2015] [Indexed: 01/24/2023] Open
Abstract
Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand. Here we present a community-driven curation effort, supported by ELIXIR—the European infrastructure for biological information—that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners. As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools.
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Affiliation(s)
- Jon Ison
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | - Kristoffer Rapacki
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | - Hervé Ménager
- Centre d'Informatique pour la Biologie, C3BI, Institut Pasteur, France
| | - Matúš Kalaš
- Computational Biology Unit, Department of Informatics, University of Bergen, Norway
| | - Emil Rydza
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | - Piotr Chmura
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | - Christian Anthon
- Department of Veterinary Clinical and Animal Sciences, Faculty for Health and Medical Sciences, University of Copenhagen, Denmark
| | - Niall Beard
- School of Computer Science, University of Manchester, UK
| | - Karel Berka
- Department of Physical Chemistry, RCPTM, Faculty of Science, Palacky University, Czech Republic
| | - Dan Bolser
- The European Bioinformatics Institute (EMBL-EBI), UK
| | - Tim Booth
- NEBC Wallingford, Centre for Ecology and Hydrology, UK
| | - Anthony Bretaudeau
- INRA, UMR Institut de Génétique, Environnement et Protection des Plantes (IGEPP), BioInformatics Platform for Agroecosystems Arthropods (BIPAA), France INRIA, IRISA, GenOuest Core Facility, France
| | - Jan Brezovsky
- Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Masaryk University, Czech Republic
| | - Rita Casadio
- Bologna Biocomputing Group, University of Bologna, Italy
| | | | - Frederik Coppens
- Department of Plant Systems Biology, VIB, Belgium Department of Plant Biotechnology and Bioinformatics, Ghent University, Belgium
| | | | | | - Kristian Davidsen
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | | | - Tunca Dogan
- UniProt, European Bioinformatics Institute (EMBL-EBI), UK
| | | | - Laura Emery
- The European Bioinformatics Institute (EMBL-EBI), UK
| | | | - Thomas Gatter
- Faculty of Technology and Center for Biotechnology, Universität Bielefeld, Germany
| | | | - Marie Grosjean
- Institut Français de Bioinformatique (French Institute of Bioinformatics), CNRS, UMS3601, France
| | - Björn Grüning
- Albert-Ludwigs-Universität Freiburg, Fahnenbergplatz, 79085 Freiburg
| | | | - Hans Ienasescu
- Bioinformatics Centre, Department of Biology, University of Copenhagen, Denmark
| | | | - Martin Closter Jespersen
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | | | - Nick Juty
- The European Bioinformatics Institute (EMBL-EBI), UK
| | - Peter Juvan
- Centre for Functional Genomics and Biochips, Faculty of Medicine, University of Ljubljana, Slovenia
| | | | - Camille Laibe
- The European Bioinformatics Institute (EMBL-EBI), UK
| | - Jing-Woei Li
- Faculty of Medicine, The Chinese University of Hong Kong, China Hong Kong Bioinformatics Centre, School of Life Sciences,The Chinese University of Hong Kong, China
| | - Luana Licata
- Dept. of Biology, University of Rome Tor Vergata, Italy
| | - Fabien Mareuil
- Centre d'Informatique pour la Biologie, C3BI, Institut Pasteur, France
| | - Ivan Mičetić
- Department of Biomedical Sciences, University of Padua, Italy
| | | | - Sebastien Moretti
- SIB Swiss Institute of Bioinformatics, Switzerland Department of Ecology and Evolution, Biophore, Evolutionary Bioinformatics group, University of Lausanne, Switzerland
| | | | - Steffen Möller
- Department of Dermatology, University of Lübeck, Germany Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Germany
| | | | - Hedi Peterson
- Institute of Computer Science, University of Tartu, Estonia
| | | | - Peter Rice
- Department of Computing, William Penney Laboratory, Imperial College London, UK
| | | | | | - Rabie Saidi
- UniProt, European Bioinformatics Institute (EMBL-EBI), UK
| | | | - Veit Schwämmle
- Protein Research Group, Department for Biochemistry and Molecular Biology, University of Southern Denmark, Denmark
| | | | - Maria Maddalena Sperotto
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | | | | | | | - Victor de la Torre
- National Bioinformatics Institute Unit (INB), Fundacion Centro Nacional de Investigaciones Oncologicas, Spain
| | | | - Allegra Via
- Dept. of Physics, Sapienza University, Italy
| | - Guy Yachdav
- Department of Informatics, Bioinformatics-I12, TUM, Germany
| | - Federico Zambelli
- Institute of Biomembranes and Bioenergetics, National Research Council (CNR), and Dept. of Biosciences, University of Milano, Italy
| | - Gert Vriend
- Radboud University Medical Centre, CMBI, Netherlands
| | - Burkhard Rost
- Department of Informatics, Bioinformatics-I12, TUM, Germany
| | | | - Peter Løngreen
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | - Søren Brunak
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
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Klausen MS, Anderson MV, Jespersen MC, Nielsen M, Marcatili P. LYRA, a webserver for lymphocyte receptor structural modeling. Nucleic Acids Res 2015; 43:W349-55. [PMID: 26007650 PMCID: PMC4489227 DOI: 10.1093/nar/gkv535] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Accepted: 05/09/2015] [Indexed: 01/22/2023] Open
Abstract
The accurate structural modeling of B- and T-cell receptors is fundamental to gain a detailed insight in the mechanisms underlying immunity and in developing new drugs and therapies. The LYRA (LYmphocyte Receptor Automated modeling) web server (http://www.cbs.dtu.dk/services/LYRA/) implements a complete and automated method for building of B- and T-cell receptor structural models starting from their amino acid sequence alone. The webserver is freely available and easy to use for non-specialists. Upon submission, LYRA automatically generates alignments using ad hoc profiles, predicts the structural class of each hypervariable loop, selects the best templates in an automatic fashion, and provides within minutes a complete 3D model that can be downloaded or inspected online. Experienced users can manually select or exclude template structures according to case specific information. LYRA is based on the canonical structure method, that in the last 30 years has been successfully used to generate antibody models of high accuracy, and in our benchmarks this approach proves to achieve similarly good results on TCR modeling, with a benchmarked average RMSD accuracy of 1.29 and 1.48 Å for B- and T-cell receptors, respectively. To the best of our knowledge, LYRA is the first automated server for the prediction of TCR structure.
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Affiliation(s)
- Michael Schantz Klausen
- Center for Biological Sequence Analysis, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Mads Valdemar Anderson
- Center for Biological Sequence Analysis, Technical University of Denmark, Kgs. Lyngby, Denmark
| | | | - Morten Nielsen
- Center for Biological Sequence Analysis, Technical University of Denmark, Kgs. Lyngby, Denmark Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Paolo Marcatili
- Center for Biological Sequence Analysis, Technical University of Denmark, Kgs. Lyngby, Denmark
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