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1. MSAViewer: interactive JavaScript visualization of multiple sequence alignments

Author

Yachdav, Guy, Wilzbach, Sebastian, Rauscher, Benedikt, Sheridan, Robert, Sillitoe, Ian, Procter, James, Lewis, Suzanna E, Rost, Burkhard, and Goldberg, Tatyana

Subjects
Information and Computing Sciences, Biological Sciences, Bioinformatics and Computational Biology, Applied Computing, Bioengineering, Programming Languages, Sequence Alignment, Software, Web Browser, Mathematical Sciences, Bioinformatics, Biological sciences, Information and computing sciences, Mathematical sciences
Abstract

The MSAViewer is a quick and easy visualization and analysis JavaScript component for Multiple Sequence Alignment data of any size. Core features include interactive navigation through the alignment, application of popular color schemes, sorting, selecting and filtering. The MSAViewer is 'web ready': written entirely in JavaScript, compatible with modern web browsers and does not require any specialized software. The MSAViewer is part of the BioJS collection of components.Availability and implementationThe MSAViewer is released as open source software under the Boost Software License 1.0. Documentation, source code and the viewer are available at http://msa.biojs.net/Supplementary information: Supplementary data are available at Bioinformatics online.Contactmsa@bio.sh.

Published
2016

3. PredictProtein - Predicting Protein Structure and Function for 29 Years

Author

Bernhofer, Michael, Dallago, Christian, Karl, Tim, Satagopam, Venkata, Heinzinger, Michael, Littmann, Maria, Olenyi, Tobias, Qiu, Jiajun, Schütze, Konstantin, Yachdav, Guy, Ashkenazy, Haim, Ben-Tal, Nir, Bromberg, Yana, Goldberg, Tatyana, Kajan, Laszlo, O’Donoghue, Sean, Sander, Chris, Schafferhans, Andrea, Schlessinger, Avner, Vriend, Gerrit, Mirdita, Milot, Gawron, Piotr, Gu, Wei, Jarosz, Yohan, Trefois, Christophe, Steinegger, Martin, Schneider, Reinhard, Rost, Burkhard, and Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center]

Subjects
Binding Sites, Bioinformatics, Protein Conformation, AcademicSubjects/SCI00010, Proteins, RNA-Binding Proteins, Multidisciplinary, general & others [F99] [Life sciences], Phosphoproteins, Protein Structure, Secondary, DNA-Binding Proteins, Multidisciplinaire, généralités & autres [F99] [Sciences du vivant], ComputingMethodologies_PATTERNRECOGNITION, Sequence Analysis, Protein, Web Server Issue, Coronavirus Nucleocapsid Proteins, Nanomedicine Radboud Institute for Molecular Life Sciences [Radboudumc 19], Sequence Alignment, Software
Abstract

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings., Graphical Abstract Graphical AbstractQueried by protein sequences, PredictProtein generates multiple sequence alignments and predicts aspects of protein function and structure through database lookups, homology-based inference, machine learning and artificial intelligence.

Published
2021
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4. PredictProtein—an open resource for online prediction of protein structural and functional features

Author

Yachdav, Guy, Kloppmann, Edda, Kajan, Laszlo, Hecht, Maximilian, Goldberg, Tatyana, Hamp, Tobias, Hönigschmid, Peter, Schafferhans, Andrea, Roos, Manfred, Bernhofer, Michael, Richter, Lothar, Ashkenazy, Haim, Punta, Marco, Schlessinger, Avner, Bromberg, Yana, Schneider, Reinhard, Vriend, Gerrit, Sander, Chris, Ben-Tal, Nir, and Rost, Burkhard

Published
2014
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5. LocTree3 prediction of localization

Author

Goldberg, Tatyana, Hecht, Maximilian, Hamp, Tobias, Karl, Timothy, Yachdav, Guy, Ahmed, Nadeem, Altermann, Uwe, Angerer, Philipp, Ansorge, Sonja, Balasz, Kinga, Bernhofer, Michael, Betz, Alexander, Cizmadija, Laura, Trinh Do, Kieu, Gerke, Julia, Greil, Robert, Joerdens, Vadim, Hastreiter, Maximilian, Hembach, Katharina, Herzog, Max, Kalemanov, Maria, Kluge, Michael, Meier, Alice, Nasir, Hassan, Neumaier, Ulrich, Prade, Verena, Reeb, Jonas, Sorokoumov, Aleksandr, Troshani, Ilira, Vorberg, Susann, Waldraff, Sonja, Zierer, Jonas, Nielsen, Henrik, and Rost, Burkhard

Published
2014
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6. PredictProtein - Predicting Protein Structure and Function for 29 Years

Author

Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Bernhofer, Michael, Dallago, Christian, Karl, Tim, Satagopam, Venkata, Heinzinger, Michael, Littmann, Maria, Olenyi, Tobias, Qiu, Jiajun, Schütze, Konstantin, Yachdav, Guy, Ashkenazy, Haim, Ben-Tal, Nir, Bromberg, Yana, Goldberg, Tatyana, Kajan, Laszlo, O’Donoghue, Sean, Sander, Chris, Schafferhans, Andrea, Schlessinger, Avner, Vriend, Gerrit, Mirdita, Milot, Gawron, Piotr, Gu, Wei, Jarosz, Yohan, Trefois, Christophe, Steinegger, Martin, Schneider, Reinhard, Rost, Burkhard, Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Bernhofer, Michael, Dallago, Christian, Karl, Tim, Satagopam, Venkata, Heinzinger, Michael, Littmann, Maria, Olenyi, Tobias, Qiu, Jiajun, Schütze, Konstantin, Yachdav, Guy, Ashkenazy, Haim, Ben-Tal, Nir, Bromberg, Yana, Goldberg, Tatyana, Kajan, Laszlo, O’Donoghue, Sean, Sander, Chris, Schafferhans, Andrea, Schlessinger, Avner, Vriend, Gerrit, Mirdita, Milot, Gawron, Piotr, Gu, Wei, Jarosz, Yohan, Trefois, Christophe, Steinegger, Martin, Schneider, Reinhard, and Rost, Burkhard

Abstract

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.

Published
2021

7. PredictProtein – Predicting Protein Structure and Function for 29 Years

Author

Bernhofer, Michael, primary, Dallago, Christian, additional, Karl, Tim, additional, Satagopam, Venkata, additional, Heinzinger, Michael, additional, Littmann, Maria, additional, Olenyi, Tobias, additional, Qiu, Jiajun, additional, Schütze, Konstantin, additional, Yachdav, Guy, additional, Ashkenazy, Haim, additional, Ben-Tal, Nir, additional, Bromberg, Yana, additional, Goldberg, Tatyana, additional, Kajan, Laszlo, additional, O’Donoghue, Sean, additional, Sander, Chris, additional, Schafferhans, Andrea, additional, Schlessinger, Avner, additional, Vriend, Gerrit, additional, Mirdita, Milot, additional, Gawron, Piotr, additional, Gu, Wei, additional, Jarosz, Yohan, additional, Trefois, Christophe, additional, Steinegger, Martin, additional, Schneider, Reinhard, additional, and Rost, Burkhard, additional

Published
2021
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13. Frameworks for Large Scale Annotation of Proteins, Proteomes and Meta-proteomes

Author

Yachdav, Guy, Rost, Burkhard (Prof. Dr.), and Bromberg, Yana (Prof. Dr.)

Subjects
Biowissenschaften, Biologie, ddc:570, ddc:000, Informatik, Wissen, Systeme
Abstract

A recent survey of UniProt reveals that only about 0.6% of those sequences contain experimental annotation. With the continuous growth in the number of new sequences, the annotation gap keeps on increasing. In this PhD project I will create publicly available, open system that helps bridge the annotation gap by integrating a battery of bioinformatics methods into a single unified platform. The goal of the work is to enable rapid and comprehensive analysis of complete proteomes and meta-proteomes in a systematic way. The system will be leveraged to investigate the metabolome in the gut of a mouse. Eine aktuelle Analyse von UniProt zeigt, dass nur etwa 0,6% der Sequenzen experimentell annotiert sind. Da die Zahl neuer Sequenzen weiterhin wächst, vergrössert sich auch die Annotations-Lücke. Im Zuge meiner Doktorarbeit werde ich ein öffentliches, offenens System erstellen, das dabei helfen wird, diese Lücke zu verkleinern, indem eine Vielzahl bioinformatischer Methoden in einer einzigen Plattform integriert wird. Das Ziel der Arbeit ist es eine schnelle, umfassende und systematische Analyse von kompletten Genomen und Meta-Proteomen zu ermöglichen. Das System wird zunächst genutzt, um das Darm-Metabolom einer Maus zu untersuchen.

Published
2016

14. Cutting edge: Anatomy of BioJS, an open source community for the life sciences

Author

Yachdav, Guy, Goldberg, Tatyana, Wilzbach, Sebastian, Dao, David, Shih, Iris, Choudhary, Saket, Crouch, Steve, Franz, Max, García, Alexander, García, Leyla J., Grüning, Björn A., Inupakutika, Devasena, Sillitoe, Ian, Thanki, Anil S., Vieira, Bruno, Villaveces, José M., Schneider, Maria V., Lewis, Suzanna, Pettifer, Steve, Rost, Burkhard, and Corpas, Manuel

Abstract

BioJS is an open source software project that develops visualization tools for different types of biological data. Here we report on the factors that influenced the growth of the BioJS user and developer community, and outline our strategy for building on this growth. The lessons we have learned on BioJS may also be relevant to other open source software projects., eLife, 4, ISSN:2050-084X

Published
2015

15. Frameworks for Large Scale Annotation of Proteins, Proteomes and Meta-proteomes

Author

Rost, Burkhard (Prof. Dr.), Bromberg, Yana (Prof. Dr.), Yachdav, Guy, Rost, Burkhard (Prof. Dr.), Bromberg, Yana (Prof. Dr.), and Yachdav, Guy

Abstract

A recent survey of UniProt reveals that only about 0.6% of those sequences contain experimental annotation. With the continuous growth in the number of new sequences, the annotation gap keeps on increasing. In this PhD project I will create publicly available, open system that helps bridge the annotation gap by integrating a battery of bioinformatics methods into a single unified platform. The goal of the work is to enable rapid and comprehensive analysis of complete proteomes and meta-proteomes in a systematic way. The system will be leveraged to investigate the metabolome in the gut of a mouse., Eine aktuelle Analyse von UniProt zeigt, dass nur etwa 0,6% der Sequenzen experimentell annotiert sind. Da die Zahl neuer Sequenzen weiterhin wächst, vergrössert sich auch die Annotations-Lücke. Im Zuge meiner Doktorarbeit werde ich ein öffentliches, offenens System erstellen, das dabei helfen wird, diese Lücke zu verkleinern, indem eine Vielzahl bioinformatischer Methoden in einer einzigen Plattform integriert wird. Das Ziel der Arbeit ist es eine schnelle, umfassende und systematische Analyse von kompletten Genomen und Meta-Proteomen zu ermöglichen. Das System wird zunächst genutzt, um das Darm-Metabolom einer Maus zu untersuchen.

Published
2016

16. Tools and data services registry: a community effort to document bioinformatics resources

Author

Ison, Jon, Rapacki, Kristoffer, Ménager, Hervé, Kalaš, Matúš, Rydza, Emil, Chmura, Piotr, Anthon, Christian, Beard, Niall, Berka, Karel, Bolser, Dan, Booth, Tim, Bretaudeau, Anthony, Brezovsky, Jan, Casadio, Rita, Cesareni, Gianni, Coppens, Frederik, Cornell, Michael, Cuccuru, Gianmauro, Davidsen, Kristian, Vedova, Gianluca Della, Dogan, Tunca, Doppelt-Azeroual, Olivia, Emery, Laura, Gasteiger, Elisabeth, Gatter, Thomas, Goldberg, Tatyana, Grosjean, Marie, Grüning, Björn, Helmer-Citterich, Manuela, Ienasescu, Hans, Ioannidis, Vassilios, Jespersen, Martin Closter, Jimenez, Rafael, Juty, Nick, Juvan, Peter, Koch, Maximilian, Laibe, Camille, Li, Jing-Woei, Licata, Luana, Mareuil, Fabien, Mičetić, Ivan, Friborg, Rune Møllegaard, Moretti, Sebastien, Morris, Chris, Möller, Steffen, Nenadic, Aleksandra, Peterson, Hedi, Profiti, Giuseppe, Rice, Peter, Romano, Paolo, Roncaglia, Paola, Saidi, Rabie, Schafferhans, Andrea, Schwämmle, Veit, Smith, Callum, Sperotto, Maria Maddalena, Stockinger, Heinz, Vařeková, Radka Svobodová, Tosatto, Silvio C.E., de la Torre, Victor, Uva, Paolo, Via, Allegra, Yachdav, Guy, Zambelli, Federico, Vriend, Gert, Rost, Burkhard, Parkinson, Helen, Løngreen, Peter, Brunak, Søren, Ison, Jon, Rapacki, Kristoffer, Ménager, Hervé, Kalaš, Matúš, Rydza, Emil, Chmura, Piotr, Anthon, Christian, Beard, Niall, Berka, Karel, Bolser, Dan, Booth, Tim, Bretaudeau, Anthony, Brezovsky, Jan, Casadio, Rita, Cesareni, Gianni, Coppens, Frederik, Cornell, Michael, Cuccuru, Gianmauro, Davidsen, Kristian, Vedova, Gianluca Della, Dogan, Tunca, Doppelt-Azeroual, Olivia, Emery, Laura, Gasteiger, Elisabeth, Gatter, Thomas, Goldberg, Tatyana, Grosjean, Marie, Grüning, Björn, Helmer-Citterich, Manuela, Ienasescu, Hans, Ioannidis, Vassilios, Jespersen, Martin Closter, Jimenez, Rafael, Juty, Nick, Juvan, Peter, Koch, Maximilian, Laibe, Camille, Li, Jing-Woei, Licata, Luana, Mareuil, Fabien, Mičetić, Ivan, Friborg, Rune Møllegaard, Moretti, Sebastien, Morris, Chris, Möller, Steffen, Nenadic, Aleksandra, Peterson, Hedi, Profiti, Giuseppe, Rice, Peter, Romano, Paolo, Roncaglia, Paola, Saidi, Rabie, Schafferhans, Andrea, Schwämmle, Veit, Smith, Callum, Sperotto, Maria Maddalena, Stockinger, Heinz, Vařeková, Radka Svobodová, Tosatto, Silvio C.E., de la Torre, Victor, Uva, Paolo, Via, Allegra, Yachdav, Guy, Zambelli, Federico, Vriend, Gert, Rost, Burkhard, Parkinson, Helen, Løngreen, Peter, and Brunak, Søren

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.

Published
2016

17. Tools and data services registry:a community effort to document bioinformatics resources

Author

Ison, Jon, Rapacki, Kristoffer, Ménager, Hervé, Kalaš, Matúš, Rydza, Emil, Chmura, Piotr, Anthon, Christian, Beard, Niall, Berka, Karel, Bolser, Dan, Booth, Tim, Bretaudeau, Anthony, Brezovsky, Jan, Casadio, Rita, Cesareni, Gianni, Coppens, Frederik, Cornell, Michael, Cuccuru, Gianmauro, Davidsen, Kristian, Vedova, Gianluca Della, Dogan, Tunca, Doppelt-Azeroual, Olivia, Emery, Laura, Gasteiger, Elisabeth, Gatter, Thomas, Goldberg, Tatyana, Grosjean, Marie, Grüning, Björn, Helmer-Citterich, Manuela, Ienasescu, Hans-Ioan, Ioannidis, Vassilios, Jespersen, Martin Closter, Jimenez, Rafael, Juty, Nick, Juvan, Peter, Koch, Maximilian, Laibe, Camille, Li, Jing-Woei, Licata, Luana, Mareuil, Fabien, Mičetić, Ivan, Friborg, Rune Møllegaard, Moretti, Sebastien, Morris, Chris, Möller, Steffen, Nenadic, Aleksandra, Peterson, Hedi, Profiti, Giuseppe, Rice, Peter, Romano, Paolo, Roncaglia, Paola, Saidi, Rabie, Schafferhans, Andrea, Schwämmle, Veit, Smith, Callum, Sperotto, Maria Maddalena, Stockinger, Heinz, Vařeková, Radka Svobodová, Tosatto, Silvio C. E., de la Torre, Victor, Uva, Paolo, Via, Allegra, Yachdav, Guy, Zambelli, Federico, Vriend, Gert, Rost, Burkhard, Parkinson, Helen, Løngreen, Peter, Brunak, Søren, Ison, Jon, Rapacki, Kristoffer, Ménager, Hervé, Kalaš, Matúš, Rydza, Emil, Chmura, Piotr, Anthon, Christian, Beard, Niall, Berka, Karel, Bolser, Dan, Booth, Tim, Bretaudeau, Anthony, Brezovsky, Jan, Casadio, Rita, Cesareni, Gianni, Coppens, Frederik, Cornell, Michael, Cuccuru, Gianmauro, Davidsen, Kristian, Vedova, Gianluca Della, Dogan, Tunca, Doppelt-Azeroual, Olivia, Emery, Laura, Gasteiger, Elisabeth, Gatter, Thomas, Goldberg, Tatyana, Grosjean, Marie, Grüning, Björn, Helmer-Citterich, Manuela, Ienasescu, Hans-Ioan, Ioannidis, Vassilios, Jespersen, Martin Closter, Jimenez, Rafael, Juty, Nick, Juvan, Peter, Koch, Maximilian, Laibe, Camille, Li, Jing-Woei, Licata, Luana, Mareuil, Fabien, Mičetić, Ivan, Friborg, Rune Møllegaard, Moretti, Sebastien, Morris, Chris, Möller, Steffen, Nenadic, Aleksandra, Peterson, Hedi, Profiti, Giuseppe, Rice, Peter, Romano, Paolo, Roncaglia, Paola, Saidi, Rabie, Schafferhans, Andrea, Schwämmle, Veit, Smith, Callum, Sperotto, Maria Maddalena, Stockinger, Heinz, Vařeková, Radka Svobodová, Tosatto, Silvio C. E., de la Torre, Victor, Uva, Paolo, Via, Allegra, Yachdav, Guy, Zambelli, Federico, Vriend, Gert, Rost, Burkhard, Parkinson, Helen, Løngreen, Peter, and Brunak, Søren

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.

Published
2016

18. LocTree3 prediction of localization

Author

Goldberg, Tatyana, Hecht, Maximilian, Hamp, Tobias, Karl, Timothy, Yachdav, Guy, Ahmed, Nadeem, Altermann, Uwe, Angerer, Philipp, Ansorge, Sonja, Balasz, Kinga, Bernhofer, Michael, Betz, Alexander, Cizmadija, Laura, Do, Kieu Trinh, Gerke, Julia, Greil, Robert, Joerdens, Vadim, Hastreiter, Maximilian, Hembach, Katharina, Herzog, Max, Kalemanov, Maria, Kluge, Michael, Meier, Alice, Nasir, Hassan, Neumaier, Ulrich, Prade, Verena, Reeb, Jonas, Sorokoumov, Aleksandr, Troshani, Ilira, Vorberg, Susann, Waldraff, Sonja, Zierer, Jonas, Nielsen, Henrik, and Rost, Burkhard

Subjects
ddc
Published
2013

19. Tools and data services registry: a community effort to document bioinformatics resources

Author

Ison, Jon, primary, Rapacki, Kristoffer, additional, Ménager, Hervé, additional, Kalaš, Matúš, additional, Rydza, Emil, additional, Chmura, Piotr, additional, Anthon, Christian, additional, Beard, Niall, additional, Berka, Karel, additional, Bolser, Dan, additional, Booth, Tim, additional, Bretaudeau, Anthony, additional, Brezovsky, Jan, additional, Casadio, Rita, additional, Cesareni, Gianni, additional, Coppens, Frederik, additional, Cornell, Michael, additional, Cuccuru, Gianmauro, additional, Davidsen, Kristian, additional, Vedova, Gianluca Della, additional, Dogan, Tunca, additional, Doppelt-Azeroual, Olivia, additional, Emery, Laura, additional, Gasteiger, Elisabeth, additional, Gatter, Thomas, additional, Goldberg, Tatyana, additional, Grosjean, Marie, additional, Grüning, Björn, additional, Helmer-Citterich, Manuela, additional, Ienasescu, Hans, additional, Ioannidis, Vassilios, additional, Jespersen, Martin Closter, additional, Jimenez, Rafael, additional, Juty, Nick, additional, Juvan, Peter, additional, Koch, Maximilian, additional, Laibe, Camille, additional, Li, Jing-Woei, additional, Licata, Luana, additional, Mareuil, Fabien, additional, Mičetić, Ivan, additional, Friborg, Rune Møllegaard, additional, Moretti, Sebastien, additional, Morris, Chris, additional, Möller, Steffen, additional, Nenadic, Aleksandra, additional, Peterson, Hedi, additional, Profiti, Giuseppe, additional, Rice, Peter, additional, Romano, Paolo, additional, Roncaglia, Paola, additional, Saidi, Rabie, additional, Schafferhans, Andrea, additional, Schwämmle, Veit, additional, Smith, Callum, additional, Sperotto, Maria Maddalena, additional, Stockinger, Heinz, additional, Vařeková, Radka Svobodová, additional, Tosatto, Silvio C.E., additional, de la Torre, Victor, additional, Uva, Paolo, additional, Via, Allegra, additional, Yachdav, Guy, additional, Zambelli, Federico, additional, Vriend, Gert, additional, Rost, Burkhard, additional, Parkinson, Helen, additional, Løngreen, Peter, additional, and Brunak, Søren, additional

Published
2015
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20. Anatomy of BioJS, an open source community for the life sciences

Author

Yachdav, Guy, primary, Goldberg, Tatyana, additional, Wilzbach, Sebastian, additional, Dao, David, additional, Shih, Iris, additional, Choudhary, Saket, additional, Crouch, Steve, additional, Franz, Max, additional, García, Alexander, additional, García, Leyla J, additional, Grüning, Björn A, additional, Inupakutika, Devasena, additional, Sillitoe, Ian, additional, Thanki, Anil S, additional, Vieira, Bruno, additional, Villaveces, José M, additional, Schneider, Maria V, additional, Lewis, Suzanna, additional, Pettifer, Steve, additional, Rost, Burkhard, additional, and Corpas, Manuel, additional

Published
2015
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21. Structural genomics reveals EVE as a new ASCH/PUA-related domain

Author

Bertonati, Claudia, Punta, Marco, Fischer, Markus, Yachdav, Guy, Forouhar, Farhad, Zhou, Weihong, Kuzin, Alexander P., Seetharaman, Jayaraman, Abashidze, Mariam, Ramelot, Theresa A., Kennedy, Michael A., Cort, John R., Belachew, Adam, Hunt, John F., Tong, Liang, Montelione, Gaetano T., and Rost, Burkhard

Subjects
Models, Molecular, Binding Sites, Sequence Homology, Amino Acid, Molecular Sequence Data, Proteins, RNA-Binding Proteins, Genomics, Ligands, Article, Protein Structure, Tertiary, Structure-Activity Relationship, Eukaryotic Cells, Prokaryotic Cells, Amino Acid Sequence, Databases, Protein, Conserved Sequence, Phylogeny
Abstract

We report on several proteins recently solved by structural genomics consortia, in particular by the Northeast Structural Genomics consortium (NESG). The proteins considered in this study differ substantially in their sequences but they share a similar structural core, characterized by a pseudobarrel five-stranded beta sheet. This core corresponds to the PUA domain-like architecture in the SCOP database. By connecting sequence information with structural knowledge, we characterize a new subgroup of these proteins that we propose to be distinctly different from previously described PUA domain-like domains such as PUA proper or ASCH. We refer to these newly defined domains as EVE. Although EVE may have retained the ability of PUA domains to bind RNA, the available experimental and computational data suggests that both the details of its molecular function and its cellular function differ from those of other PUA domain-like domains. This study of EVE and its relatives illustrates how the combination of structure and genomics creates new insights by connecting a cornucopia of structures that map to the same evolutionary potential. Primary sequence information alone would have not been sufficient to reveal these evolutionary links.

Published
2009

22. New in protein structure and function annotation: Hotspots, single nucleotide polymorphisms and the 'Deep Web'

Author

Bromberg, Yana, Yachdav, Guy, Ofran, Yanay, Schneider, Reinhard, Rost, Burkhard, and Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center]

Subjects
Biochemistry, biophysics & molecular biology [F05] [Life sciences], Biochimie, biophysique & biologie moléculaire [F05] [Sciences du vivant]
Abstract

The rapidly increasing quantity of protein sequence data continues to widen the gap between available sequences and annotations. Comparative modeling suggests some aspects of the 3D structures of approximately half of all known proteins; homology- and network-based inferences annotate some aspect of function for a similar fraction of the proteome. For most known protein sequences, however, there is detailed knowledge about neither their function nor their structure. Comprehensive efforts towards the expert curation of sequence annotations have failed to meet the demand of the rapidly increasing number of available sequences. Only the automated prediction of protein function in the absence of homology can close the gap between available sequences and annotations in the foreseeable future. This review focuses on two novel methods for automated annotation, and briefly presents an outlook on how modern web software may revolutionize the field of protein sequence annotation. First, predictions of protein binding sites and functional hotspots, and the evolution of these into the most successful type of prediction of protein function from sequence will be discussed. Second, a new tool, comprehensive in silico mutagenesis, which contributes important novel predictions of function and at the same time prepares for the onset of the next sequencing revolution, will be described. While these two new sub-fields of protein prediction represent the breakthroughs that have been achieved methodologically, it will then be argued that a different development might further change the way biomedical researchers benefit from annotations: modern web software can connect the worldwide web in any browser with the 'Deep Web' (ie, proprietary data resources). The availability of this direct connection, and the resulting access to a wealth of data, may impact drug discovery and development more than any existing method that contributes to protein annotation.

Published
2009

23. PredictProtein--an open resource for online prediction of protein structural and functional features

Author

Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Yachdav, Guy, Kloppman, Kajan, Hecht, Goldberg, Hamp, Honigschmid, Schafferhans, Roos, Manfred, Bernhofer, Richter, Ashkenazy, Punta, Schlessinger, Bromberg, Schneider, Reinhard, Vriend, Sander, Ben-Tal, Rost, Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Yachdav, Guy, Kloppman, Kajan, Hecht, Goldberg, Hamp, Honigschmid, Schafferhans, Roos, Manfred, Bernhofer, Richter, Ashkenazy, Punta, Schlessinger, Bromberg, Schneider, Reinhard, Vriend, Sander, Ben-Tal, and Rost

Abstract

PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein–protein binding sites (ISIS2), protein–polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org.

Published
2014

27. BioJS: an open source standard for biological visualisation – its status in 2014

Author

Corpas, Manuel, primary, Jimenez, Rafael, additional, Carbon, Seth J, additional, García, Alex, additional, Garcia, Leyla, additional, Goldberg, Tatyana, additional, Gomez, John, additional, Kalderimis, Alexis, additional, Lewis, Suzanna E, additional, Mulvany, Ian, additional, Pawlik, Aleksandra, additional, Rowland, Francis, additional, Salazar, Gustavo, additional, Schreiber, Fabian, additional, Sillitoe, Ian, additional, Spooner, William H, additional, Thanki, Anil S., additional, Villaveces, José M, additional, Yachdav, Guy, additional, and Hermjakob, Henning, additional

Published
2014
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28. New in protein structure and function annotation: Hotspots, single nucleotide polymorphisms and the 'Deep Web'

Author

Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Bromberg, Yana, Yachdav, Guy, Ofran, Yanay, Schneider, Reinhard, Rost, Burkhard, Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Bromberg, Yana, Yachdav, Guy, Ofran, Yanay, Schneider, Reinhard, and Rost, Burkhard

Abstract

The rapidly increasing quantity of protein sequence data continues to widen the gap between available sequences and annotations. Comparative modeling suggests some aspects of the 3D structures of approximately half of all known proteins; homology- and network-based inferences annotate some aspect of function for a similar fraction of the proteome. For most known protein sequences, however, there is detailed knowledge about neither their function nor their structure. Comprehensive efforts towards the expert curation of sequence annotations have failed to meet the demand of the rapidly increasing number of available sequences. Only the automated prediction of protein function in the absence of homology can close the gap between available sequences and annotations in the foreseeable future. This review focuses on two novel methods for automated annotation, and briefly presents an outlook on how modern web software may revolutionize the field of protein sequence annotation. First, predictions of protein binding sites and functional hotspots, and the evolution of these into the most successful type of prediction of protein function from sequence will be discussed. Second, a new tool, comprehensive in silico mutagenesis, which contributes important novel predictions of function and at the same time prepares for the onset of the next sequencing revolution, will be described. While these two new sub-fields of protein prediction represent the breakthroughs that have been achieved methodologically, it will then be argued that a different development might further change the way biomedical researchers benefit from annotations: modern web software can connect the worldwide web in any browser with the 'Deep Web' (ie, proprietary data resources). The availability of this direct connection, and the resulting access to a wealth of data, may impact drug discovery and development more than any existing method that contributes to protein annotation.

Published
2009

29. Cloud Prediction of Protein Structure and Function with PredictProtein for Debian

Author

Kaján, László, primary, Yachdav, Guy, additional, Vicedo, Esmeralda, additional, Steinegger, Martin, additional, Mirdita, Milot, additional, Angermüller, Christof, additional, Böhm, Ariane, additional, Domke, Simon, additional, Ertl, Julia, additional, Mertes, Christian, additional, Reisinger, Eva, additional, Staniewski, Cedric, additional, and Rost, Burkhard, additional

Published
2013
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32. Structural genomics reveals EVE as a new ASCH/PUA-related domain

Author

Bertonati, Claudia, primary, Punta, Marco, additional, Fischer, Markus, additional, Yachdav, Guy, additional, Forouhar, Farhad, additional, Zhou, Weihong, additional, Kuzin, Alexander P., additional, Seetharaman, Jayaraman, additional, Abashidze, Mariam, additional, Ramelot, Theresa A., additional, Kennedy, Michael A., additional, Cort, John R., additional, Belachew, Adam, additional, Hunt, John F., additional, Tong, Liang, additional, Montelione, Gaetano T., additional, and Rost, Burkhard, additional

Published
2008
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33. Tools and data services registry: a community effort to document bioinformatics resources

Author

Callum Smith, Paolo Uva, Thomas Gatter, Peter Løngreen, Peter Juvan, Hans Ienasescu, Giuseppe Profiti, Aleksandra Nenadic, Kristoffer Rapacki, Chris Morris, Paola Roncaglia, Steffen Möller, Laura Emery, Søren Brunak, Maria Maddalena Sperotto, Heinz Stockinger, Kristian Davidsen, Federico Zambelli, Helen Parkinson, Olivia Doppelt-Azeroual, Luana Licata, Tatyana Goldberg, Andrea Schafferhans, Elisabeth Gasteiger, Emil Karol Rydza, Camille Laibe, Victor De La Torre, Marie Grosjean, Manuela Helmer-Citterich, Hervé Ménager, Radka Svobodová Vařeková, Rafael C. Jimenez, Martin Closter Jespersen, Anthony Bretaudeau, Jan Brezovsky, Tunca Doğan, Matúš Kalaš, Peter M. Rice, Ivan Mičetić, Rune Møllegaard Friborg, Maximilian Koch, Silvio C. E. Tosatto, Nick Juty, Björn Grüning, Gianmauro Cuccuru, Frederik Coppens, Gianni Cesareni, Jon Ison, Rabie Saidi, Sébastien Moretti, Rita Casadio, Gert Vriend, Guy Yachdav, Niall Beard, Timothy F. Booth, Michael Cornell, Piotr Jaroslaw Chmura, Veit Schwämmle, Karel Berka, Dan Bolser, Vassilios Ioannidis, Jing-Woei Li, Burkhard Rost, Gianluca Della Vedova, Fabien Mareuil, Hedi Peterson, Allegra Via, Paolo Romano, Christian Anthon, Technical University of Denmark [Lyngby] (DTU), Institut Pasteur de Madagascar, Réseau International des Instituts Pasteur (RIIP), University of Bergen (UIB), University of Copenhagen = Københavns Universitet (KU), University of Manchester, Palacky University, European Bioinformatics Institute, NEBC Wallingford, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Masaryk University, University of Bologna, Università degli Studi di Roma Tor Vergata [Roma], Ghent University [Belgium] (UGENT), Flanders Institute for Biotechnology, CRS4 Bioinformat, Università degli studi di Milano-Bicocca, Swiss Institute of Bioinformatics, Universität Bielefeld = Bielefeld University, Tumor Biology Center, Centre National de la Recherche Scientifique (CNRS), University of Freiburg, University of Ljubljana, The Chinese University of Hong Kong [Hong Kong], Universita degli Studi di Padova, Bioinformatics Research Centre, Université de Lausanne, CCLRC Daresbury Laboratory, Universität zu Lübeck [Lübeck] - University of Lübeck [Lübeck], Universität Rostock, University of Tartu, Imperial College London, IRCCS Azienda Ospedaliera Universitaria Integrata San Martino (IRCCS AOU San Martino), University of Southern Denmark (SDU), WTCHG, Central European Institute of Technology [Brno] (CEITEC), Instituto Nacional de Bioinformática, Sapienza University of Rome (DIAG), Consiglio Nazionale delle Ricerche, University of Milan, Radboud University Nijmegen, 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, DELLA VEDOVA, G, 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, M, Jimenez, R, Juty, N, Juvan, P, Koch, M, Laibe, C, Li, J, Licata, L, Mareuil, F, Mičetić, I, Friborg, R, 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, M, Stockinger, H, Vařeková, R, Tosatto, S, de la Torre, V, Uva, P, Via, A, Yachdav, G, Zambelli, F, Vriend, G, Rost, B, Parkinson, H, Løngreen, P, Brunak, S, University of Bergen (UiB), Palacky University Olomouc, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Masaryk University [Brno] (MUNI), Universiteit Gent = Ghent University [Belgium] (UGENT), Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne (UNIL), Universität zu Lübeck [Lübeck], Central European Institute of Technology [Brno] (CEITEC MU), Brno University of Technology [Brno] (BUT), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Danmarks Tekniske Universitet = Technical University of Denmark (DTU), University of Copenhagen = Københavns Universitet (UCPH), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, University of Bologna/Università di Bologna, Universiteit Gent = Ghent University (UGENT), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), Université de Lausanne = University of Lausanne (UNIL), Università degli Studi di Padova = University of Padua (Unipd), Universität zu Lübeck = University of Lübeck [Lübeck], Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), Università degli Studi di Milano = University of Milan (UNIMI), Ison, Jon, Rapacki, Kristoffer, Ménager, Hervé, Kalaš, Matúš, Rydza, Emil, Chmura, Piotr, Anthon, Christian, Beard, Niall, Berka, Karel, Bolser, Dan, Booth, Tim, Bretaudeau, Anthony, Brezovsky, Jan, Casadio, Rita, Cesareni, Gianni, Coppens, Frederik, Cornell, Michael, Cuccuru, Gianmauro, Davidsen, Kristian, Vedova, Gianluca Della, Dogan, Tunca, Doppelt-Azeroual, Olivia, Emery, Laura, Gasteiger, Elisabeth, Gatter, Thoma, Goldberg, Tatyana, Grosjean, Marie, Grüning, Björn, Helmer-Citterich, Manuela, Ienasescu, Han, Ioannidis, Vassilio, Jespersen, Martin Closter, Jimenez, Rafael, Juty, Nick, Juvan, Peter, Koch, Maximilian, Laibe, Camille, Li, Jing-Woei, Licata, Luana, Mareuil, Fabien, Mičetić, Ivan, Friborg, Rune Møllegaard, Moretti, Sebastien, Morris, Chri, Möller, Steffen, Nenadic, Aleksandra, Peterson, Hedi, Profiti, Giuseppe, Rice, Peter, Romano, Paolo, Roncaglia, Paola, Saidi, Rabie, Schafferhans, Andrea, Schwämmle, Veit, Smith, Callum, Sperotto, Maria Maddalena, Stockinger, Heinz, Vařeková, Radka Svobodová, Tosatto, Silvio C E, de la Torre, Victor, Uva, Paolo, Via, Allegra, Yachdav, Guy, Zambelli, Federico, Vriend, Gert, Rost, Burkhard, Parkinson, Helen, Løngreen, Peter, and Brunak, Søren

Subjects
0301 basic medicine, [SDV]Life Sciences [q-bio], registry, Bioinformatics, computer.software_genre, Matematikk og naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Systemutvikling og -arbeid: 426 [VDP], Task (project management), Documentation, Data and Information, Database Issue, Registries, bioinformatique, Data Curation, base de données, Settore BIO/11, gestion de données, tool, SOFTWARE-DEVELOPMENT, bioinformatics, ddc, outil informatique, Tools and data services registry, SEQANSWERS, Web service, MOLECULAR-BIOLOGY, Biology, Ecology and Environment, 03 medical and health sciences, SDG 3 - Good Health and Well-being, Genetics, Implementation, Dissemination, Bioinformatikk / Bioinformatics, Data curation, bioinformatic, business.industry, Computational Biology, Software, Software development, bioinformatics, tools, registry, elixir, Biology and Life Sciences, Mathematics and natural scienses: 400::Information and communication science: 420::System development and design: 426 [VDP], FRAMEWORK, ELIXIR, Settore BIO/18 - Genetica, 030104 developmental biology, tools, Data as a service, COMPILATION, business, COLLECTION, Nanomedicine Radboud Institute for Molecular Life Sciences [Radboudumc 19], computer, WEB SERVICES, LIFE SCIENCES
Abstract

Contains fulltext : 171819.pdf (Publisher’s version ) (Open Access) 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.

Full Text
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34. Tools and data services registry: a community effort to document bioinformatics resources.

Author

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 SC, de la Torre V, Uva P, Via A, Yachdav G, Zambelli F, Vriend G, Rost B, Parkinson H, Løngreen P, and Brunak S

Subjects
Data Curation, Software, Computational Biology, Registries
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., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Published
2016
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35. Structural genomics reveals EVE as a new ASCH/PUA-related domain.

Author

Bertonati C, Punta M, Fischer M, Yachdav G, Forouhar F, Zhou W, Kuzin AP, Seetharaman J, Abashidze M, Ramelot TA, Kennedy MA, Cort JR, Belachew A, Hunt JF, Tong L, Montelione GT, and Rost B

Subjects
Amino Acid Sequence, Binding Sites genetics, Conserved Sequence genetics, Databases, Protein, Eukaryotic Cells chemistry, Eukaryotic Cells metabolism, Ligands, Models, Molecular, Molecular Sequence Data, Phylogeny, Prokaryotic Cells chemistry, Prokaryotic Cells metabolism, Proteins classification, Proteins genetics, RNA-Binding Proteins chemistry, RNA-Binding Proteins genetics, Sequence Homology, Amino Acid, Structure-Activity Relationship, Genomics methods, Protein Structure, Tertiary, Proteins chemistry
Abstract

We report on several proteins recently solved by structural genomics consortia, in particular by the Northeast Structural Genomics consortium (NESG). The proteins considered in this study differ substantially in their sequences but they share a similar structural core, characterized by a pseudobarrel five-stranded beta sheet. This core corresponds to the PUA domain-like architecture in the SCOP database. By connecting sequence information with structural knowledge, we characterize a new subgroup of these proteins that we propose to be distinctly different from previously described PUA domain-like domains such as PUA proper or ASCH. We refer to these newly defined domains as EVE. Although EVE may have retained the ability of PUA domains to bind RNA, the available experimental and computational data suggests that both the details of its molecular function and its cellular function differ from those of other PUA domain-like domains. This study of EVE and its relatives illustrates how the combination of structure and genomics creates new insights by connecting a cornucopia of structures that map to the same evolutionary potential. Primary sequence information alone would have not been sufficient to reveal these evolutionary links.

Published
2009
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36. New in protein structure and function annotation: hotspots, single nucleotide polymorphisms and the 'Deep Web'.

Author

Bromberg Y, Yachdav G, Ofran Y, Schneider R, and Rost B

Subjects
Databases as Topic, Models, Molecular, Mutagenesis physiology, Protein Binding, Protein Interaction Domains and Motifs drug effects, Software, Databases, Protein classification, Drug Discovery methods, Internet trends
Abstract

The rapidly increasing quantity of protein sequence data continues to widen the gap between available sequences and annotations. Comparative modeling suggests some aspects of the 3D structures of approximately half of all known proteins; homology- and network-based inferences annotate some aspect of function for a similar fraction of the proteome. For most known protein sequences, however, there is detailed knowledge about neither their function nor their structure. Comprehensive efforts towards the expert curation of sequence annotations have failed to meet the demand of the rapidly increasing number of available sequences. Only the automated prediction of protein function in the absence of homology can close the gap between available sequences and annotations in the foreseeable future. This review focuses on two novel methods for automated annotation, and briefly presents an outlook on how modern web software may revolutionize the field of protein sequence annotation. First, predictions of protein binding sites and functional hotspots, and the evolution of these into the most successful type of prediction of protein function from sequence will be discussed. Second, a new tool, comprehensive in silico mutagenesis, which contributes important novel predictions of function and at the same time prepares for the onset of the next sequencing revolution, will be described. While these two new sub-fields of protein prediction represent the breakthroughs that have been achieved methodologically, it will then be argued that a different development might further change the way biomedical researchers benefit from annotations: modern web software can connect the worldwide web in any browser with the 'Deep Web' (ie, proprietary data resources). The availability of this direct connection, and the resulting access to a wealth of data, may impact drug discovery and development more than any existing method that contributes to protein annotation.

Published
2009

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