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45 results on '"Trellet, Mikael"'

1. PDB-tools web: A user-friendly interface for the manipulation of PDB files

Author

Jiménez-García, Brian, Teixeira, João M C, Trellet, Mikael, Rodrigues, João P G L M, Bonvin, Alexandre M J J, Jiménez-García, Brian, Teixeira, João M C, Trellet, Mikael, Rodrigues, João P G L M, and Bonvin, Alexandre M J J

Abstract

The Protein Data Bank (PDB) file format remains a popular format used and supported by many software to represent coordinates of macromolecular structures. It however suffers from drawbacks such as error-prone manual editing. Because of that, various software toolkits have been developed to facilitate its editing and manipulation, but, to date, there is no online tool available for this purpose. Here we present PDB-Tools Web, a flexible online service for manipulating PDB files. It offers a rich and user-friendly graphical user interface that allows users to mix-and-match more than 40 individual tools from the pdb-tools suite. Those can be combined in a few clicks to perform complex pipelines, which can be saved and uploaded. The resulting processed PDB files can be visualized online and downloaded. The web server is freely available at https://wenmr.science.uu.nl/pdbtools.

Published
2021

2. PDB-tools web: A user-friendly interface for the manipulation of PDB files

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Jiménez-García, Brian, Teixeira, João M C, Trellet, Mikael, Rodrigues, João P G L M, Bonvin, Alexandre M J J, NMR Spectroscopy, Sub NMR Spectroscopy, Jiménez-García, Brian, Teixeira, João M C, Trellet, Mikael, Rodrigues, João P G L M, and Bonvin, Alexandre M J J

Published
2021

3. PDB-tools web: A user-friendly interface for the manipulation of PDB files

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Jiménez-García, Brian, Teixeira, João M C, Trellet, Mikael, Rodrigues, João P G L M, Bonvin, Alexandre M J J, NMR Spectroscopy, Sub NMR Spectroscopy, Jiménez-García, Brian, Teixeira, João M C, Trellet, Mikael, Rodrigues, João P G L M, and Bonvin, Alexandre M J J

Published
2021

4. PDB-tools web: A user-friendly interface for the manipulation of PDB files

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Jiménez-García, Brian, Teixeira, João M C, Trellet, Mikael, Rodrigues, João P G L M, Bonvin, Alexandre M J J, NMR Spectroscopy, Sub NMR Spectroscopy, Jiménez-García, Brian, Teixeira, João M C, Trellet, Mikael, Rodrigues, João P G L M, and Bonvin, Alexandre M J J

Published
2021

5. Inhibition of the integrated stress response by viral proteins that block p-eIF2-eIF2B association

Author

Rabouw, Huib H, Visser, Linda J, Passchier, Tim C, Langereis, Martijn A, Liu, Fan, Giansanti, Piero, van Vliet, Arno L W, Dekker, José G, van der Grein, Susanne G, Saucedo, Jesús G, Anand, Aditya A, Trellet, Mikael E, Bonvin, Alexandre M J J, Walter, Peter, Heck, Albert J R, de Groot, Raoul J, van Kuppeveld, Frank J M, Rabouw, Huib H, Visser, Linda J, Passchier, Tim C, Langereis, Martijn A, Liu, Fan, Giansanti, Piero, van Vliet, Arno L W, Dekker, José G, van der Grein, Susanne G, Saucedo, Jesús G, Anand, Aditya A, Trellet, Mikael E, Bonvin, Alexandre M J J, Walter, Peter, Heck, Albert J R, de Groot, Raoul J, and van Kuppeveld, Frank J M

Abstract

Eukaryotic cells, when exposed to environmental or internal stress, activate the integrated stress response (ISR) to restore homeostasis and promote cell survival. Specific stress stimuli prompt dedicated stress kinases to phosphorylate eukaryotic initiation factor 2 (eIF2). Phosphorylated eIF2 (p-eIF2) in turn sequesters the eIF2-specific guanine exchange factor eIF2B to block eIF2 recycling, thereby halting translation initiation and reducing global protein synthesis. To circumvent stress-induced translational shutdown, viruses encode ISR antagonists. Those identified so far prevent or reverse eIF2 phosphorylation. We now describe two viral proteins-one from a coronavirus and the other from a picornavirus-that have independently acquired the ability to counteract the ISR at its very core by acting as a competitive inhibitor of p-eIF2-eIF2B interaction. This allows continued formation of the eIF2-GTP-Met-tRNAi ternary complex and unabated global translation at high p-eIF2 levels that would otherwise cause translational arrest. We conclude that eIF2 and p-eIF2 differ in their interaction with eIF2B to such effect that p-eIF2-eIF2B association can be selectively inhibited.

Published
2020

6. Protein–Protein Modeling Using Cryo-EM Restraints

Author

Trellet, Mikael, van Zundert, Gydo, Bonvin, Alexandre M.J.J., Trellet, Mikael, van Zundert, Gydo, and Bonvin, Alexandre M.J.J.

Abstract

Recent improvements in cryo-electron microscopy (cryo-EM) in the past few years are now allowing to observe molecular complexes at atomic resolution. As a consequence, numerous structures derived from cryo-EM are now available in the Protein Data Bank. However, if for some complexes atomic resolution is reached, this is not true for all. This is also the case in cryo-electron tomography where the achievable resolution is still limited. Furthermore the resolution in a cryo-EM map is not a constant, with often outer regions being of lower resolution, possibly linked to conformational variability. Although those low- to medium-resolution EM maps (or regions thereof) cannot directly provide atomic structure of large molecular complexes, they provide valuable information to model the individual components and their assembly into them. Most approaches for this kind of modeling are performing rigid fitting of the individual components into the EM density map. While this would appear an obvious option, they ignore key aspects of molecular recognition, the energetics and flexibility of the interfaces. Moreover, this often restricts the modeling to a unique source of data, the EM density map. In this chapter, we describe a protocol where an EM map is used as restraint in HADDOCK to guide the modeling process. In the first step, rigid-body fitting is performed with PowerFit in order to identify the most likely locations of the molecules into the map. These are then used as centroids to which distance restraints are defined from the center of mass of the components of the complex for the initial rigid-body docking. The EM density is then directly used as an additional restraint energy term, which can be combined with all the other types of data supported by HADDOCK. This protocol relies on the new version 2.4 of both the HADDOCK webserver and software. Preparation steps consisting of cropping the EM map and rigid-body fitting of the atomic structure are explained. Then, the

Published
2020

7. An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45

Author

Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jorg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li C., Honorato, Rodrigo V., Moreira, Irina, Kurkcuoglu, Zeynep, Vangone, Anna, Bonvin, Alexandre M. J. J., Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jorg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li C., Honorato, Rodrigo V., Moreira, Irina, Kurkcuoglu, Zeynep, Vangone, Anna, and Bonvin, Alexandre M. J. J.

Abstract

Our information‐driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38‐45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template‐based refinement/CA‐CA restraint‐guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse‐grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher‐quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models

Published
2020

8. Inhibition of the integrated stress response by viral proteins that block p-eIF2-eIF2B association

Author

LS Virologie, dI&I I&I-1, Sub Biomol.Mass Spectrometry & Proteom., Virologie, dB&C I&I, Sub NMR Spectroscopy, Afd Biomol.Mass Spect. and Proteomics, Biomolecular Mass Spectrometry and Proteomics, Rabouw, Huib H, Visser, Linda J, Passchier, Tim C, Langereis, Martijn A, Liu, Fan, Giansanti, Piero, van Vliet, Arno L W, Dekker, José G, van der Grein, Susanne G, Saucedo, Jesús G, Anand, Aditya A, Trellet, Mikael E, Bonvin, Alexandre M J J, Walter, Peter, Heck, Albert J R, de Groot, Raoul J, van Kuppeveld, Frank J M, LS Virologie, dI&I I&I-1, Sub Biomol.Mass Spectrometry & Proteom., Virologie, dB&C I&I, Sub NMR Spectroscopy, Afd Biomol.Mass Spect. and Proteomics, Biomolecular Mass Spectrometry and Proteomics, Rabouw, Huib H, Visser, Linda J, Passchier, Tim C, Langereis, Martijn A, Liu, Fan, Giansanti, Piero, van Vliet, Arno L W, Dekker, José G, van der Grein, Susanne G, Saucedo, Jesús G, Anand, Aditya A, Trellet, Mikael E, Bonvin, Alexandre M J J, Walter, Peter, Heck, Albert J R, de Groot, Raoul J, and van Kuppeveld, Frank J M

Published
2020

9. Protein–Protein Modeling Using Cryo-EM Restraints

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Trellet, Mikael, van Zundert, Gydo, Bonvin, Alexandre M.J.J., Gáspári, Zoltán, Sub NMR Spectroscopy, NMR Spectroscopy, Trellet, Mikael, van Zundert, Gydo, Bonvin, Alexandre M.J.J., and Gáspári, Zoltán

Published
2020

10. An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jorg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li C., Honorato, Rodrigo V., Moreira, Irina, Kurkcuoglu, Zeynep, Vangone, Anna, Bonvin, Alexandre M. J. J., NMR Spectroscopy, Sub NMR Spectroscopy, Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jorg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li C., Honorato, Rodrigo V., Moreira, Irina, Kurkcuoglu, Zeynep, Vangone, Anna, and Bonvin, Alexandre M. J. J.

Published
2020

11. Inhibition of the integrated stress response by viral proteins that block p-eIF2-eIF2B association

Author

LS Virologie, dI&I I&I-1, Sub Biomol.Mass Spectrometry & Proteom., Virologie, dB&C I&I, Sub NMR Spectroscopy, Afd Biomol.Mass Spect. and Proteomics, Biomolecular Mass Spectrometry and Proteomics, Rabouw, Huib H, Visser, Linda J, Passchier, Tim C, Langereis, Martijn A, Liu, Fan, Giansanti, Piero, van Vliet, Arno L W, Dekker, José G, van der Grein, Susanne G, Saucedo, Jesús G, Anand, Aditya A, Trellet, Mikael E, Bonvin, Alexandre M J J, Walter, Peter, Heck, Albert J R, de Groot, Raoul J, van Kuppeveld, Frank J M, LS Virologie, dI&I I&I-1, Sub Biomol.Mass Spectrometry & Proteom., Virologie, dB&C I&I, Sub NMR Spectroscopy, Afd Biomol.Mass Spect. and Proteomics, Biomolecular Mass Spectrometry and Proteomics, Rabouw, Huib H, Visser, Linda J, Passchier, Tim C, Langereis, Martijn A, Liu, Fan, Giansanti, Piero, van Vliet, Arno L W, Dekker, José G, van der Grein, Susanne G, Saucedo, Jesús G, Anand, Aditya A, Trellet, Mikael E, Bonvin, Alexandre M J J, Walter, Peter, Heck, Albert J R, de Groot, Raoul J, and van Kuppeveld, Frank J M

Published
2020

12. Protein–Protein Modeling Using Cryo-EM Restraints

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Trellet, Mikael, van Zundert, Gydo, Bonvin, Alexandre M.J.J., Gáspári, Zoltán, Sub NMR Spectroscopy, NMR Spectroscopy, Trellet, Mikael, van Zundert, Gydo, Bonvin, Alexandre M.J.J., and Gáspári, Zoltán

Published
2020

13. An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jorg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li C., Honorato, Rodrigo V., Moreira, Irina, Kurkcuoglu, Zeynep, Vangone, Anna, Bonvin, Alexandre M. J. J., NMR Spectroscopy, Sub NMR Spectroscopy, Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jorg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li C., Honorato, Rodrigo V., Moreira, Irina, Kurkcuoglu, Zeynep, Vangone, Anna, and Bonvin, Alexandre M. J. J.

Published
2020

14. Inhibition of the integrated stress response by viral proteins that block p-eIF2-eIF2B association

Author

LS Virologie, dI&I I&I-1, Sub Biomol.Mass Spectrometry & Proteom., Virologie, dB&C I&I, Sub NMR Spectroscopy, Afd Biomol.Mass Spect. and Proteomics, Biomolecular Mass Spectrometry and Proteomics, Rabouw, Huib H, Visser, Linda J, Passchier, Tim C, Langereis, Martijn A, Liu, Fan, Giansanti, Piero, van Vliet, Arno L W, Dekker, José G, van der Grein, Susanne G, Saucedo, Jesús G, Anand, Aditya A, Trellet, Mikael E, Bonvin, Alexandre M J J, Walter, Peter, Heck, Albert J R, de Groot, Raoul J, van Kuppeveld, Frank J M, LS Virologie, dI&I I&I-1, Sub Biomol.Mass Spectrometry & Proteom., Virologie, dB&C I&I, Sub NMR Spectroscopy, Afd Biomol.Mass Spect. and Proteomics, Biomolecular Mass Spectrometry and Proteomics, Rabouw, Huib H, Visser, Linda J, Passchier, Tim C, Langereis, Martijn A, Liu, Fan, Giansanti, Piero, van Vliet, Arno L W, Dekker, José G, van der Grein, Susanne G, Saucedo, Jesús G, Anand, Aditya A, Trellet, Mikael E, Bonvin, Alexandre M J J, Walter, Peter, Heck, Albert J R, de Groot, Raoul J, and van Kuppeveld, Frank J M

Published
2020

15. Protein–Protein Modeling Using Cryo-EM Restraints

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Gáspári, Zoltán, Trellet, Mikael, van Zundert, Gydo, Bonvin, Alexandre M.J.J., Sub NMR Spectroscopy, NMR Spectroscopy, Gáspári, Zoltán, Trellet, Mikael, van Zundert, Gydo, and Bonvin, Alexandre M.J.J.

Published
2020

16. An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jorg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li C., Honorato, Rodrigo V., Moreira, Irina, Kurkcuoglu, Zeynep, Vangone, Anna, Bonvin, Alexandre M. J. J., NMR Spectroscopy, Sub NMR Spectroscopy, Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jorg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li C., Honorato, Rodrigo V., Moreira, Irina, Kurkcuoglu, Zeynep, Vangone, Anna, and Bonvin, Alexandre M. J. J.

Published
2020

17. Sharing Data from Molecular Simulations

Author

Abraham, Mark James, Apostolov, Rossen Pavlov, Barnoud, Jonathan, Bauer, Paul, Blau, Christian, Bonvin, Alexandre M.J.J., Chavent, Matthieu, Chodera, John Damon, Čondić-Jurkić, Karmen, Delemotte, Lucie, Grubmüller, Helmut, Howard, Rebecca J., Jordan, E. Joseph, Lindal, Erik, Ollila, O.H. Samuli, Selent, Jana, Smith, Daniel G. A., Stansfeld, Phill James, Tiemann, Johanna K. S., Trellet, Mikael, Woods, Christopher J., Zhmurov, Artem, Abraham, Mark James, Apostolov, Rossen Pavlov, Barnoud, Jonathan, Bauer, Paul, Blau, Christian, Bonvin, Alexandre M.J.J., Chavent, Matthieu, Chodera, John Damon, Čondić-Jurkić, Karmen, Delemotte, Lucie, Grubmüller, Helmut, Howard, Rebecca J., Jordan, E. Joseph, Lindal, Erik, Ollila, O.H. Samuli, Selent, Jana, Smith, Daniel G. A., Stansfeld, Phill James, Tiemann, Johanna K. S., Trellet, Mikael, Woods, Christopher J., and Zhmurov, Artem

Abstract

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations have become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, and each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simu...

Published
2019

18. Large-scale prediction of binding affinity in protein-small ligand complexes: The PRODIGY-LIG web server

Author

Vangone, Anna, Schaarschmidt, Joerg, Koukos, Panagiotis, Geng, Cunliang, Citro, Nevia, Trellet, Mikael E., Xue, Li C., Bonvin, Alexandre M.J.J., Vangone, Anna, Schaarschmidt, Joerg, Koukos, Panagiotis, Geng, Cunliang, Citro, Nevia, Trellet, Mikael E., Xue, Li C., and Bonvin, Alexandre M.J.J.

Abstract

Recently we published PROtein binDIng enerGY (PRODIGY), a web-server for the prediction of binding affinity in protein-protein complexes. By using a combination of simple structural properties, such as the residue-contacts made at the interface, PRODIGY has demonstrated a top performance compared with other state-of-the-art predictors in the literature. Here we present an extension of it, named PRODIGY-LIG, aimed at the prediction of affinity in protein-small ligand complexes. The predictive method, properly readapted for small ligand by making use of atomic instead of residue contacts, has been successfully applied for the blind prediction of 102 protein- ligand complexes during the D3R Grand Challenge 2. PRODIGY-LIG has the advantage of being simple, generic and applicable to any kind of protein-ligand complex. It provides an automatic, fast and user-friendly tool ensuring broad accessibility.

Published
2019

19. West-Life: A Virtual Research Environment for structural biology

Author

Morris, Chris, Andreetto, Paolo, Banci, Lucia, Bonvin, Alexandre M.J.J., Chojnowski, Grzegorz, Cano, Laura del, Carazo, José Marıa, Conesa, Pablo, Daenke, Susan, Damaskos, George, Giachetti, Andrea, Haley, Natalie E.C., Hekkelman, Maarten L., Heuser, Philipp, Joosten, Robbie P., Kouřil, Daniel, Křenek, Aleš, Kulhánek, Tomáš, Lamzin, Victor S., Nadzirin, Nurul, Perrakis, Anastassis, Rosato, Antonio, Sanderson, Fiona, Segura, Joan, Schaarschmidt, Joerg, Sobolev, Egor, Traldi, Sergio, Trellet, Mikael E., Velankar, Sameer, Verlato, Marco, Winn, Martyn, Morris, Chris, Andreetto, Paolo, Banci, Lucia, Bonvin, Alexandre M.J.J., Chojnowski, Grzegorz, Cano, Laura del, Carazo, José Marıa, Conesa, Pablo, Daenke, Susan, Damaskos, George, Giachetti, Andrea, Haley, Natalie E.C., Hekkelman, Maarten L., Heuser, Philipp, Joosten, Robbie P., Kouřil, Daniel, Křenek, Aleš, Kulhánek, Tomáš, Lamzin, Victor S., Nadzirin, Nurul, Perrakis, Anastassis, Rosato, Antonio, Sanderson, Fiona, Segura, Joan, Schaarschmidt, Joerg, Sobolev, Egor, Traldi, Sergio, Trellet, Mikael E., Velankar, Sameer, Verlato, Marco, and Winn, Martyn

Abstract

The West-Life project (https://about.west-life.eu/)is a Horizon 2020 project funded by the European Commission to provide data processing and data management services for the international community of structural biologists, and in particular to support integrative experimental approaches within the field of structural biology. It has developed enhancements to existing web services for structure solution and analysis, created new pipelines to link these services into more complex higher-level workflows, and added new data management facilities. Through this work it has striven to make the benefits of European e-Infrastructures more accessible to life-science researchers in general and structural biologists in particular.

Published
2019

20. Sharing Data from Molecular Simulations

Author

Abraham, Mark James, Apostolov, Rossen, Barnoud, Jonathan, Bauer, Paul, Blau, Christian, Bonvin, Alexandre M. J. J., Chavent, Matthieu, Chodera, John, Condic-Jurkic, Karmen, Delemotte, Lucie, Grubmueller, Helmut, Howard, Rebecca, Jordan, E. Joseph, Lindahl, Erik, Ollila, O. H. Samuli, Selent, Jana, Smith, Daniel G. A., Stansfeld, Phillip J., Tiemann, Johanna K. S., Trellet, Mikael, Woods, Christopher, Zhmurov, Artem, Abraham, Mark James, Apostolov, Rossen, Barnoud, Jonathan, Bauer, Paul, Blau, Christian, Bonvin, Alexandre M. J. J., Chavent, Matthieu, Chodera, John, Condic-Jurkic, Karmen, Delemotte, Lucie, Grubmueller, Helmut, Howard, Rebecca, Jordan, E. Joseph, Lindahl, Erik, Ollila, O. H. Samuli, Selent, Jana, Smith, Daniel G. A., Stansfeld, Phillip J., Tiemann, Johanna K. S., Trellet, Mikael, Woods, Christopher, and Zhmurov, Artem

Abstract

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations has become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 (https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way toward more open, interoperable, and reproducible outputs coming from research studies using MD simulations., QC 20200131

Published
2019
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21. Sharing Data from Molecular Simulations

Author

Abraham, Mark, Apostolov, Rossen, Barnoud, Jonathan, Bauer, Paul, Blau, Christian, Bonvin, Alexandre M. J. J., Chavent, Matthieu, Chodera, John, Condic-Jurkic, Karmen, Delemotte, Lucie, Grubmueller, Helmut, Howard, Rebecca J., Jordan, E. Joseph, Lindahl, Erik, Ollila, O. H. Samuli, Selent, Jana, Smith, Daniel G. A., Stansfeld, Phillip J., Tiemann, Johanna K. S., Trellet, Mikael, Woods, Christopher, Zhmurov, Artem, Abraham, Mark, Apostolov, Rossen, Barnoud, Jonathan, Bauer, Paul, Blau, Christian, Bonvin, Alexandre M. J. J., Chavent, Matthieu, Chodera, John, Condic-Jurkic, Karmen, Delemotte, Lucie, Grubmueller, Helmut, Howard, Rebecca J., Jordan, E. Joseph, Lindahl, Erik, Ollila, O. H. Samuli, Selent, Jana, Smith, Daniel G. A., Stansfeld, Phillip J., Tiemann, Johanna K. S., Trellet, Mikael, Woods, Christopher, and Zhmurov, Artem

Abstract

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations has become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 (https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way toward more open, interoperable, and reproducible outputs coming from research studies using MD simulations.

Published
2019
Full Text
View/download PDF

22. Sharing Data from Molecular Simulations

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Abraham, Mark James, Apostolov, Rossen Pavlov, Barnoud, Jonathan, Bauer, Paul, Blau, Christian, Bonvin, Alexandre M.J.J., Chavent, Matthieu, Chodera, John Damon, Čondić-Jurkić, Karmen, Delemotte, Lucie, Grubmüller, Helmut, Howard, Rebecca J., Jordan, E. Joseph, Lindal, Erik, Ollila, O.H. Samuli, Selent, Jana, Smith, Daniel G. A., Stansfeld, Phill James, Tiemann, Johanna K. S., Trellet, Mikael, Woods, Christopher J., Zhmurov, Artem, Sub NMR Spectroscopy, NMR Spectroscopy, Abraham, Mark James, Apostolov, Rossen Pavlov, Barnoud, Jonathan, Bauer, Paul, Blau, Christian, Bonvin, Alexandre M.J.J., Chavent, Matthieu, Chodera, John Damon, Čondić-Jurkić, Karmen, Delemotte, Lucie, Grubmüller, Helmut, Howard, Rebecca J., Jordan, E. Joseph, Lindal, Erik, Ollila, O.H. Samuli, Selent, Jana, Smith, Daniel G. A., Stansfeld, Phill James, Tiemann, Johanna K. S., Trellet, Mikael, Woods, Christopher J., and Zhmurov, Artem

Published
2019

23. Large-scale prediction of binding affinity in protein-small ligand complexes: The PRODIGY-LIG web server

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Vangone, Anna, Schaarschmidt, Joerg, Koukos, Panagiotis, Geng, Cunliang, Citro, Nevia, Trellet, Mikael E., Xue, Li C., Bonvin, Alexandre M.J.J., Sub NMR Spectroscopy, NMR Spectroscopy, Vangone, Anna, Schaarschmidt, Joerg, Koukos, Panagiotis, Geng, Cunliang, Citro, Nevia, Trellet, Mikael E., Xue, Li C., and Bonvin, Alexandre M.J.J.

Published
2019

24. West-Life: A Virtual Research Environment for structural biology

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Morris, Chris, Andreetto, Paolo, Banci, Lucia, Bonvin, Alexandre M.J.J., Chojnowski, Grzegorz, Cano, Laura del, Carazo, José Marıa, Conesa, Pablo, Daenke, Susan, Damaskos, George, Giachetti, Andrea, Haley, Natalie E.C., Hekkelman, Maarten L., Heuser, Philipp, Joosten, Robbie P., Kouřil, Daniel, Křenek, Aleš, Kulhánek, Tomáš, Lamzin, Victor S., Nadzirin, Nurul, Perrakis, Anastassis, Rosato, Antonio, Sanderson, Fiona, Segura, Joan, Schaarschmidt, Joerg, Sobolev, Egor, Traldi, Sergio, Trellet, Mikael E., Velankar, Sameer, Verlato, Marco, Winn, Martyn, NMR Spectroscopy, Sub NMR Spectroscopy, Morris, Chris, Andreetto, Paolo, Banci, Lucia, Bonvin, Alexandre M.J.J., Chojnowski, Grzegorz, Cano, Laura del, Carazo, José Marıa, Conesa, Pablo, Daenke, Susan, Damaskos, George, Giachetti, Andrea, Haley, Natalie E.C., Hekkelman, Maarten L., Heuser, Philipp, Joosten, Robbie P., Kouřil, Daniel, Křenek, Aleš, Kulhánek, Tomáš, Lamzin, Victor S., Nadzirin, Nurul, Perrakis, Anastassis, Rosato, Antonio, Sanderson, Fiona, Segura, Joan, Schaarschmidt, Joerg, Sobolev, Egor, Traldi, Sergio, Trellet, Mikael E., Velankar, Sameer, Verlato, Marco, and Winn, Martyn

Published
2019

25. Large-scale prediction of binding affinity in protein-small ligand complexes: The PRODIGY-LIG web server

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Vangone, Anna, Schaarschmidt, Joerg, Koukos, Panagiotis, Geng, Cunliang, Citro, Nevia, Trellet, Mikael E., Xue, Li C., Bonvin, Alexandre M.J.J., Sub NMR Spectroscopy, NMR Spectroscopy, Vangone, Anna, Schaarschmidt, Joerg, Koukos, Panagiotis, Geng, Cunliang, Citro, Nevia, Trellet, Mikael E., Xue, Li C., and Bonvin, Alexandre M.J.J.

Published
2019

26. Sharing Data from Molecular Simulations

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Abraham, Mark James, Apostolov, Rossen Pavlov, Barnoud, Jonathan, Bauer, Paul, Blau, Christian, Bonvin, Alexandre M.J.J., Chavent, Matthieu, Chodera, John Damon, Čondić-Jurkić, Karmen, Delemotte, Lucie, Grubmüller, Helmut, Howard, Rebecca J., Jordan, E. Joseph, Lindal, Erik, Ollila, O.H. Samuli, Selent, Jana, Smith, Daniel G. A., Stansfeld, Phill James, Tiemann, Johanna K. S., Trellet, Mikael, Woods, Christopher J., Zhmurov, Artem, Sub NMR Spectroscopy, NMR Spectroscopy, Abraham, Mark James, Apostolov, Rossen Pavlov, Barnoud, Jonathan, Bauer, Paul, Blau, Christian, Bonvin, Alexandre M.J.J., Chavent, Matthieu, Chodera, John Damon, Čondić-Jurkić, Karmen, Delemotte, Lucie, Grubmüller, Helmut, Howard, Rebecca J., Jordan, E. Joseph, Lindal, Erik, Ollila, O.H. Samuli, Selent, Jana, Smith, Daniel G. A., Stansfeld, Phill James, Tiemann, Johanna K. S., Trellet, Mikael, Woods, Christopher J., and Zhmurov, Artem

Published
2019

27. West-Life: A Virtual Research Environment for structural biology

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Morris, Chris, Andreetto, Paolo, Banci, Lucia, Bonvin, Alexandre M.J.J., Chojnowski, Grzegorz, Cano, Laura del, Carazo, José Marıa, Conesa, Pablo, Daenke, Susan, Damaskos, George, Giachetti, Andrea, Haley, Natalie E.C., Hekkelman, Maarten L., Heuser, Philipp, Joosten, Robbie P., Kouřil, Daniel, Křenek, Aleš, Kulhánek, Tomáš, Lamzin, Victor S., Nadzirin, Nurul, Perrakis, Anastassis, Rosato, Antonio, Sanderson, Fiona, Segura, Joan, Schaarschmidt, Joerg, Sobolev, Egor, Traldi, Sergio, Trellet, Mikael E., Velankar, Sameer, Verlato, Marco, Winn, Martyn, NMR Spectroscopy, Sub NMR Spectroscopy, Morris, Chris, Andreetto, Paolo, Banci, Lucia, Bonvin, Alexandre M.J.J., Chojnowski, Grzegorz, Cano, Laura del, Carazo, José Marıa, Conesa, Pablo, Daenke, Susan, Damaskos, George, Giachetti, Andrea, Haley, Natalie E.C., Hekkelman, Maarten L., Heuser, Philipp, Joosten, Robbie P., Kouřil, Daniel, Křenek, Aleš, Kulhánek, Tomáš, Lamzin, Victor S., Nadzirin, Nurul, Perrakis, Anastassis, Rosato, Antonio, Sanderson, Fiona, Segura, Joan, Schaarschmidt, Joerg, Sobolev, Egor, Traldi, Sergio, Trellet, Mikael E., Velankar, Sameer, Verlato, Marco, and Winn, Martyn

Published
2019

28. Large-scale prediction of binding affinity in protein-small ligand complexes: The PRODIGY-LIG web server

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Vangone, Anna, Schaarschmidt, Joerg, Koukos, Panagiotis, Geng, Cunliang, Citro, Nevia, Trellet, Mikael E., Xue, Li C., Bonvin, Alexandre M.J.J., Sub NMR Spectroscopy, NMR Spectroscopy, Vangone, Anna, Schaarschmidt, Joerg, Koukos, Panagiotis, Geng, Cunliang, Citro, Nevia, Trellet, Mikael E., Xue, Li C., and Bonvin, Alexandre M.J.J.

Published
2019

29. West-Life: A Virtual Research Environment for structural biology

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Morris, Chris, Andreetto, Paolo, Banci, Lucia, Bonvin, Alexandre M.J.J., Chojnowski, Grzegorz, Cano, Laura del, Carazo, José Marıa, Conesa, Pablo, Daenke, Susan, Damaskos, George, Giachetti, Andrea, Haley, Natalie E.C., Hekkelman, Maarten L., Heuser, Philipp, Joosten, Robbie P., Kouřil, Daniel, Křenek, Aleš, Kulhánek, Tomáš, Lamzin, Victor S., Nadzirin, Nurul, Perrakis, Anastassis, Rosato, Antonio, Sanderson, Fiona, Segura, Joan, Schaarschmidt, Joerg, Sobolev, Egor, Traldi, Sergio, Trellet, Mikael E., Velankar, Sameer, Verlato, Marco, Winn, Martyn, NMR Spectroscopy, Sub NMR Spectroscopy, Morris, Chris, Andreetto, Paolo, Banci, Lucia, Bonvin, Alexandre M.J.J., Chojnowski, Grzegorz, Cano, Laura del, Carazo, José Marıa, Conesa, Pablo, Daenke, Susan, Damaskos, George, Giachetti, Andrea, Haley, Natalie E.C., Hekkelman, Maarten L., Heuser, Philipp, Joosten, Robbie P., Kouřil, Daniel, Křenek, Aleš, Kulhánek, Tomáš, Lamzin, Victor S., Nadzirin, Nurul, Perrakis, Anastassis, Rosato, Antonio, Sanderson, Fiona, Segura, Joan, Schaarschmidt, Joerg, Sobolev, Egor, Traldi, Sergio, Trellet, Mikael E., Velankar, Sameer, Verlato, Marco, and Winn, Martyn

Published
2019

30. Sharing Data from Molecular Simulations

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Abraham, Mark James, Apostolov, Rossen Pavlov, Barnoud, Jonathan, Bauer, Paul, Blau, Christian, Bonvin, Alexandre M.J.J., Chavent, Matthieu, Chodera, John Damon, Čondić-Jurkić, Karmen, Delemotte, Lucie, Grubmüller, Helmut, Howard, Rebecca J., Jordan, E. Joseph, Lindal, Erik, Ollila, O.H. Samuli, Selent, Jana, Smith, Daniel G. A., Stansfeld, Phill James, Tiemann, Johanna K. S., Trellet, Mikael, Woods, Christopher J., Zhmurov, Artem, Sub NMR Spectroscopy, NMR Spectroscopy, Abraham, Mark James, Apostolov, Rossen Pavlov, Barnoud, Jonathan, Bauer, Paul, Blau, Christian, Bonvin, Alexandre M.J.J., Chavent, Matthieu, Chodera, John Damon, Čondić-Jurkić, Karmen, Delemotte, Lucie, Grubmüller, Helmut, Howard, Rebecca J., Jordan, E. Joseph, Lindal, Erik, Ollila, O.H. Samuli, Selent, Jana, Smith, Daniel G. A., Stansfeld, Phill James, Tiemann, Johanna K. S., Trellet, Mikael, Woods, Christopher J., and Zhmurov, Artem

Published
2019

31. West-Life: A Virtual Research Environment for structural biology

Author

European Commission, Morris, Chris, Andreetto, Paolo, Banci, Lucia, Bonvin, A. M. J. J., Chojnowski, Grzegorz, Caño, Laura del, Carazo, José M., Conesa Mingo, Pablo, Daenke, Susan, Damaskos, George, Giachetti, Andrea, Haley, Natalie E.C., Hekkelman, Maarten L., Heuser, Philipp, Joosten, Robbie P., Kouřil, Daniel, Křenek, Aleš, Kulhánek, Tomáš, Lamzin, Victor S., Nadzirin, Nurul, Perrakis, Anastassis, Rosato, Antonio, Sanderson, Fiona, Segura, Joan, Schaarschmidt, Joerg, Sobolev, Egor, Traldi, Sergio, Trellet, Mikael E., Velankar, Sameer, Verlato, Marco, Winn, Martyn, European Commission, Morris, Chris, Andreetto, Paolo, Banci, Lucia, Bonvin, A. M. J. J., Chojnowski, Grzegorz, Caño, Laura del, Carazo, José M., Conesa Mingo, Pablo, Daenke, Susan, Damaskos, George, Giachetti, Andrea, Haley, Natalie E.C., Hekkelman, Maarten L., Heuser, Philipp, Joosten, Robbie P., Kouřil, Daniel, Křenek, Aleš, Kulhánek, Tomáš, Lamzin, Victor S., Nadzirin, Nurul, Perrakis, Anastassis, Rosato, Antonio, Sanderson, Fiona, Segura, Joan, Schaarschmidt, Joerg, Sobolev, Egor, Traldi, Sergio, Trellet, Mikael E., Velankar, Sameer, Verlato, Marco, and Winn, Martyn

Abstract

The West-Life project (https://about.west-life.eu/) is a Horizon 2020 project funded by the European Commission to provide data processing and data management services for the international community of structural biologists, and in particular to support integrative experimental approaches within the field of structural biology. It has developed enhancements to existing web services for structure solution and analysis, created new pipelines to link these services into more complex higher-level workflows, and added new data management facilities. Through this work it has striven to make the benefits of European e-Infrastructures more accessible to life-science researchers in general and structural biologists in particular.

Published
2019

32. Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment

Author

Agence Nationale de la Recherche (France), Cancer Research UK, European Commission, Medical Research Council (UK), National Institutes of Health (US), National Natural Science Foundation of China, National Research Foundation of Korea, National Science Foundation (US), Ministerio de Economía y Competitividad (España), Università degli Studi di Napoli PARTHENOPE, Wellcome Trust, Lensink, Marc F., Brysbaert, Guillaume, Nadzirin, Nurul, Velankar, Sameer, Chaleil, Raphaël A. G., Gerguri, Tereza, Bates, Paul A., Laine, Elodie, Carbone, Alessandra, Grudinin, Sergei, Kong, Ren, Weng, Zhiping, Guest, Johnathan D., Gowthaman, Ragul, Pierce, Brian G., Xu, Xianjin, Duan, Rui, Qiu, Liming, Hou, Jie, Merideth, Benjamin Ryan, Ma, Zhiwei, Cheng, Jianlin, Zou, Xiaoqin, Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jörg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li, Jiménez-García, Brian, Noort, Charlotte W. van, Honorato, Rodrigo V., Bonvin, A. M. J. J., Wodak, Shoshana J., Liu, Ran-Ran, Xu, Xi-Ming, Shi, Hang, Chang, Shan, Eisenstein, Miriam, Karczynska, Agnieszka, Czaplewski, Cezary, Emilia Lubecka, Emilia, Lipska, Agnieszka, Krupa, Paweł, Mozolewska, Magdalena, Golon, Łukasz, Samsonov, Sergey, Liwo, Adam, Crivelli, Silvia, Pagès, Guillaume, Karasikov, Mikhaill, Kadukova, Maria, Yan, Yumeng, Huang, Sheng-You, Rosell, Mireia, Rodríguez-Lumbreras, Luis A., Romero-Durana, Miguel, Díaz-Bueno, Lucía, Fernández-Recio, Juan, Christoffer, Charles, Terashi, Genki, Shin, Woong-Hee, Aderinwale, Tunde, Venkata Subraman, Sai Raghavendra Maddhuri, Kihara, Daisuke, Kozakov, Dima, Vajda, Sandor, Porter, Kathryn, Padhorny, Dzmitry, Desta, Israel, Beglov, Dmitri, Ignatov, Mikhail, Kotelnikov, Sergey, Moal, Iain H., Ritchie, David W., Chauvot de Beauchêne, Isaure, Maigret, Bernard, Devignes, Marie-Dominique, Ruiz Echartea, Maria E., Barradas-Bautista, Didier, Cao, Zhen, Cavallo, Luigi, Oliva, Romina, Cao, Yue, Shen, Yang, Baek, Minkyung, Park, Taeyong, Woo, Hyeonuk, Seok, Chaok, Braitbard, Merav, Bitton, Lirane, Scheidman-Duhovny, Dina, Dapkunas, Justas, Olechnovic, Kliment, Venclovas, Česlovas, Kundrotas, Petras J., Belkin, Saveliy, Chakravarty, Devlina, Badal, Varsha D., Vakser, Ilya A., Vreven, Thom, Vangaveti, Sweta, Borrman, Tyler, Agence Nationale de la Recherche (France), Cancer Research UK, European Commission, Medical Research Council (UK), National Institutes of Health (US), National Natural Science Foundation of China, National Research Foundation of Korea, National Science Foundation (US), Ministerio de Economía y Competitividad (España), Università degli Studi di Napoli PARTHENOPE, Wellcome Trust, Lensink, Marc F., Brysbaert, Guillaume, Nadzirin, Nurul, Velankar, Sameer, Chaleil, Raphaël A. G., Gerguri, Tereza, Bates, Paul A., Laine, Elodie, Carbone, Alessandra, Grudinin, Sergei, Kong, Ren, Weng, Zhiping, Guest, Johnathan D., Gowthaman, Ragul, Pierce, Brian G., Xu, Xianjin, Duan, Rui, Qiu, Liming, Hou, Jie, Merideth, Benjamin Ryan, Ma, Zhiwei, Cheng, Jianlin, Zou, Xiaoqin, Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jörg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li, Jiménez-García, Brian, Noort, Charlotte W. van, Honorato, Rodrigo V., Bonvin, A. M. J. J., Wodak, Shoshana J., Liu, Ran-Ran, Xu, Xi-Ming, Shi, Hang, Chang, Shan, Eisenstein, Miriam, Karczynska, Agnieszka, Czaplewski, Cezary, Emilia Lubecka, Emilia, Lipska, Agnieszka, Krupa, Paweł, Mozolewska, Magdalena, Golon, Łukasz, Samsonov, Sergey, Liwo, Adam, Crivelli, Silvia, Pagès, Guillaume, Karasikov, Mikhaill, Kadukova, Maria, Yan, Yumeng, Huang, Sheng-You, Rosell, Mireia, Rodríguez-Lumbreras, Luis A., Romero-Durana, Miguel, Díaz-Bueno, Lucía, Fernández-Recio, Juan, Christoffer, Charles, Terashi, Genki, Shin, Woong-Hee, Aderinwale, Tunde, Venkata Subraman, Sai Raghavendra Maddhuri, Kihara, Daisuke, Kozakov, Dima, Vajda, Sandor, Porter, Kathryn, Padhorny, Dzmitry, Desta, Israel, Beglov, Dmitri, Ignatov, Mikhail, Kotelnikov, Sergey, Moal, Iain H., Ritchie, David W., Chauvot de Beauchêne, Isaure, Maigret, Bernard, Devignes, Marie-Dominique, Ruiz Echartea, Maria E., Barradas-Bautista, Didier, Cao, Zhen, Cavallo, Luigi, Oliva, Romina, Cao, Yue, Shen, Yang, Baek, Minkyung, Park, Taeyong, Woo, Hyeonuk, Seok, Chaok, Braitbard, Merav, Bitton, Lirane, Scheidman-Duhovny, Dina, Dapkunas, Justas, Olechnovic, Kliment, Venclovas, Česlovas, Kundrotas, Petras J., Belkin, Saveliy, Chakravarty, Devlina, Badal, Varsha D., Vakser, Ilya A., Vreven, Thom, Vangaveti, Sweta, and Borrman, Tyler

Abstract

We present the results for CAPRI Round 46, the third joint CASP‐CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo‐oligomers and 6 heterocomplexes. Eight of the homo‐oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher‐order assemblies. These were more difficult to model, as their prediction mainly involved “ab‐initio” docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance “gap” was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template‐based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

Published
2019

33. Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2

Author

Kurkcuoglu Soner, Zeynep, Koukos, Panos, Citro, Nevia, Trellet, Mikael E., Garcia Lopes Maia Rodrigues, Joao, de Sousa Moreira, Irina, Roel-touris, Jorge, Melquiond, Adrien S. J., Geng, Cunliang, Schaarschmidt, Jörg, Xue, Li C., Vangone, Anna, Bonvin, A. M. J. J., Kurkcuoglu Soner, Zeynep, Koukos, Panos, Citro, Nevia, Trellet, Mikael E., Garcia Lopes Maia Rodrigues, Joao, de Sousa Moreira, Irina, Roel-touris, Jorge, Melquiond, Adrien S. J., Geng, Cunliang, Schaarschmidt, Jörg, Xue, Li C., Vangone, Anna, and Bonvin, A. M. J. J.

Abstract

We present the performance of HADDOCK, our information-driven docking software, in the second edition of the D3R Grand Challenge. In this blind experiment, participants were requested to predict the structures and binding affinities of complexes between the Farnesoid X nuclear receptor and 102 different ligands. The models obtained in Stage1 with HADDOCK and ligand-specific protocol show an average ligand RMSD of 5.1 Å from the crystal structure. Only 6/35 targets were within 2.5 Å RMSD from the reference, which prompted us to investigate the limiting factors and revise our protocol for Stage2. The choice of the receptor conformation appeared to have the strongest influence on the results. Our Stage2 models were of higher quality (13 out of 35 were within 2.5 Å), with an average RMSD of 4.1 Å. The docking protocol was applied to all 102 ligands to generate poses for binding affinity prediction. We developed a modified version of our contact-based binding affinity predictor PRODIGY, using the number of interatomic contacts classified by their type and the intermolecular electrostatic energy. This simple structure-based binding affinity predictor shows a Kendall’s Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups). Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor. This results in an enrichment factor of 2.5 compared to a random predictor for ranking ligands within the top 25%, making it a promising approach to identify lead compounds in virtual screening.

Published
2018

34. Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Kurkcuoglu Soner, Zeynep, Koukos, Panos, Citro, Nevia, Trellet, Mikael E., Garcia Lopes Maia Rodrigues, Joao, de Sousa Moreira, Irina, Roel-touris, Jorge, Melquiond, Adrien S. J., Geng, Cunliang, Schaarschmidt, Jörg, Xue, Li C., Vangone, Anna, Bonvin, A. M. J. J., NMR Spectroscopy, Sub NMR Spectroscopy, Kurkcuoglu Soner, Zeynep, Koukos, Panos, Citro, Nevia, Trellet, Mikael E., Garcia Lopes Maia Rodrigues, Joao, de Sousa Moreira, Irina, Roel-touris, Jorge, Melquiond, Adrien S. J., Geng, Cunliang, Schaarschmidt, Jörg, Xue, Li C., Vangone, Anna, and Bonvin, A. M. J. J.

Published
2018

35. Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Kurkcuoglu Soner, Zeynep, Koukos, Panos, Citro, Nevia, Trellet, Mikael E., Garcia Lopes Maia Rodrigues, Joao, de Sousa Moreira, Irina, Roel-touris, Jorge, Melquiond, Adrien S. J., Geng, Cunliang, Schaarschmidt, Jörg, Xue, Li C., Vangone, Anna, Bonvin, A. M. J. J., NMR Spectroscopy, Sub NMR Spectroscopy, Kurkcuoglu Soner, Zeynep, Koukos, Panos, Citro, Nevia, Trellet, Mikael E., Garcia Lopes Maia Rodrigues, Joao, de Sousa Moreira, Irina, Roel-touris, Jorge, Melquiond, Adrien S. J., Geng, Cunliang, Schaarschmidt, Jörg, Xue, Li C., Vangone, Anna, and Bonvin, A. M. J. J.

Published
2018

36. Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2

Author

NMR Spectroscopy, Sub NMR Spectroscopy, Kurkcuoglu Soner, Zeynep, Koukos, Panos, Citro, Nevia, Trellet, Mikael E., Garcia Lopes Maia Rodrigues, Joao, de Sousa Moreira, Irina, Roel-touris, Jorge, Melquiond, Adrien S. J., Geng, Cunliang, Schaarschmidt, Jörg, Xue, Li C., Vangone, Anna, Bonvin, A. M. J. J., NMR Spectroscopy, Sub NMR Spectroscopy, Kurkcuoglu Soner, Zeynep, Koukos, Panos, Citro, Nevia, Trellet, Mikael E., Garcia Lopes Maia Rodrigues, Joao, de Sousa Moreira, Irina, Roel-touris, Jorge, Melquiond, Adrien S. J., Geng, Cunliang, Schaarschmidt, Jörg, Xue, Li C., Vangone, Anna, and Bonvin, A. M. J. J.

Published
2018

37. SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots

Author

de Sousa Moreira, Irina, Koukos, Panos, Melo, Rita, Almeida, Jose G, Preto, Antonio J., Schaarschmidt, Joerg, Trellet, Mikael, Gümüş, Zeynep H, Costa, Joaquim, Bonvin, Alexandre M. J. J., de Sousa Moreira, Irina, Koukos, Panos, Melo, Rita, Almeida, Jose G, Preto, Antonio J., Schaarschmidt, Joerg, Trellet, Mikael, Gümüş, Zeynep H, Costa, Joaquim, and Bonvin, Alexandre M. J. J.

Abstract

We present SpotOn, a web server to identify and classify interfacial residues as Hot-Spots (HS) and Null-Spots (NS). SpotON implements a robust algorithm with a demonstrated accuracy of 0.95 and sensitivity of 0.98 on an independent test set. The predictor was developed using an ensemble machine learning approach with up-sampling of the minor class. It was trained on 53 complexes using various features, based on both protein 3D structure and sequence. The SpotOn web interface is freely available at: http://milou.science.uu.nl/services/SPOTON/.

Published
2017

38. SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots

Author

NMR Spectroscopy, Sub NMR Spectroscopy, de Sousa Moreira, Irina, Koukos, Panos, Melo, Rita, Almeida, Jose G, Preto, Antonio J., Schaarschmidt, Joerg, Trellet, Mikael, Gümüş, Zeynep H, Costa, Joaquim, Bonvin, Alexandre M. J. J., NMR Spectroscopy, Sub NMR Spectroscopy, de Sousa Moreira, Irina, Koukos, Panos, Melo, Rita, Almeida, Jose G, Preto, Antonio J., Schaarschmidt, Joerg, Trellet, Mikael, Gümüş, Zeynep H, Costa, Joaquim, and Bonvin, Alexandre M. J. J.

Published
2017

39. SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots

Author

NMR Spectroscopy, Sub NMR Spectroscopy, de Sousa Moreira, Irina, Koukos, Panos, Melo, Rita, Almeida, Jose G, Preto, Antonio J., Schaarschmidt, Joerg, Trellet, Mikael, Gümüş, Zeynep H, Costa, Joaquim, Bonvin, Alexandre M. J. J., NMR Spectroscopy, Sub NMR Spectroscopy, de Sousa Moreira, Irina, Koukos, Panos, Melo, Rita, Almeida, Jose G, Preto, Antonio J., Schaarschmidt, Joerg, Trellet, Mikael, Gümüş, Zeynep H, Costa, Joaquim, and Bonvin, Alexandre M. J. J.

Published
2017

40. SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots

Author

NMR Spectroscopy, Sub NMR Spectroscopy, de Sousa Moreira, Irina, Koukos, Panos, Melo, Rita, Almeida, Jose G, Preto, Antonio J., Schaarschmidt, Joerg, Trellet, Mikael, Gümüş, Zeynep H, Costa, Joaquim, Bonvin, Alexandre M. J. J., NMR Spectroscopy, Sub NMR Spectroscopy, de Sousa Moreira, Irina, Koukos, Panos, Melo, Rita, Almeida, Jose G, Preto, Antonio J., Schaarschmidt, Joerg, Trellet, Mikael, Gümüş, Zeynep H, Costa, Joaquim, and Bonvin, Alexandre M. J. J.

Published
2017

41. Information-driven modeling of protein-peptide complexes

Author

Trellet, Mikael, Melquiond, Adrien S J, Bonvin, Alexandre M J J, Trellet, Mikael, Melquiond, Adrien S J, and Bonvin, Alexandre M J J

Abstract

Despite their biological importance in many regulatory processes, protein-peptide recognition mechanisms are diffi cult to study experimentally at the structural level because of the inherent fl exibility of peptides and the often transient interactions on which they rely. Complementary methods like biomolecular docking are therefore required. The prediction of the three-dimensional structure of protein-peptide complexes raises unique challenges for computational algorithms, as exemplifi ed by the recent introduction of proteinpeptide targets in the blind international experiment CAPRI (Critical Assessment of PRedicted Interactions). Conventional protein-protein docking approaches are often struggling with the high fl exibility of peptides whose short sizes impede protocols and scoring functions developed for larger interfaces. On the other side, protein-small ligand docking methods are unable to cope with the larger number of degrees of freedom in peptides compared to small molecules and the typically reduced available information to defi ne the binding site. In this chapter, we describe a protocol to model protein-peptide complexes using the HADDOCK web server, working through a test case to illustrate every steps. The fl exibility challenge that peptides represent is dealt with by combining elements of conformational selection and induced fi t molecular recognition theories. Key words Biomolecular interactions, Information-driven docking, Conformational changes, Flexibility, HADDOCK, Molecular modeling.

Published
2015

42. Information-driven modeling of protein-peptide complexes

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Trellet, Mikael, Melquiond, Adrien S J, Bonvin, Alexandre M J J, Sub NMR Spectroscopy, NMR Spectroscopy, Trellet, Mikael, Melquiond, Adrien S J, and Bonvin, Alexandre M J J

Published
2015

43. Information-driven modeling of protein-peptide complexes

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Trellet, Mikael, Melquiond, Adrien S J, Bonvin, Alexandre M J J, Sub NMR Spectroscopy, NMR Spectroscopy, Trellet, Mikael, Melquiond, Adrien S J, and Bonvin, Alexandre M J J

Published
2015

44. Information-driven modeling of protein-peptide complexes

Author

Sub NMR Spectroscopy, NMR Spectroscopy, Trellet, Mikael, Melquiond, Adrien S J, Bonvin, Alexandre M J J, Sub NMR Spectroscopy, NMR Spectroscopy, Trellet, Mikael, Melquiond, Adrien S J, and Bonvin, Alexandre M J J

Published
2015

45. Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions

Author

Moretti, Rocco, Fleishman, Sarel J., Agius, Rudi, Torchala, Mieczyslaw, Bates, Paul A., Kastritis, Panagiotis L., Rodrigues, Joao P. G. L. M., Trellet, Mikael, Bonvin, Alexandre M. J. J., Cui, Meng, Rooman, Marianne, Gillis, Dimitri, Dehouck, Yves, Moal, Iain, Romero-Durana, Miguel, Perez-Cano, Laura, Pallara, Chiara, Jimenez, Brian, Fernandez-Recio, Juan, Flores, Samuel, Pacella, Michael, Kilambi, Krishna Praneeth, Gray, Jeffrey J., Popov, Petr, Grudinin, Sergei, Esquivel-Rodriguez, Juan, Kihara, Daisuke, Zhao, Nan, Korkin, Dmitry, Zhu, Xiaolei, Demerdash, Omar N. A., Mitchell, Julie C., Kanamori, Eiji, Tsuchiya, Yuko, Nakamura, Haruki, Lee, Hasup, Park, Hahnbeom, Seok, Chaok, Sarmiento, Jamica, Liang, Shide, Teraguchi, Shusuke, Standley, Daron M., Shimoyama, Hiromitsu, Terashi, Genki, Takeda-Shitaka, Mayuko, Iwadate, Mitsuo, Umeyama, Hideaki, Beglov, Dmitri, Hall, David R., Kozakov, Dima, Vajda, Sandor, Pierce, Brian G., Hwang, Howook, Vreven, Thom, Weng, Zhiping, Huang, Yangyu, Li, Haotian, Yang, Xiufeng, Ji, Xiaofeng, Liu, Shiyong, Xiao, Yi, Zacharias, Martin, Qin, Sanbo, Zhou, Huan-Xiang, Huang, Sheng-You, Zou, Xiaoqin, Velankar, Sameer, Janin, Joel, Wodak, Shoshana J., Baker, David, Moretti, Rocco, Fleishman, Sarel J., Agius, Rudi, Torchala, Mieczyslaw, Bates, Paul A., Kastritis, Panagiotis L., Rodrigues, Joao P. G. L. M., Trellet, Mikael, Bonvin, Alexandre M. J. J., Cui, Meng, Rooman, Marianne, Gillis, Dimitri, Dehouck, Yves, Moal, Iain, Romero-Durana, Miguel, Perez-Cano, Laura, Pallara, Chiara, Jimenez, Brian, Fernandez-Recio, Juan, Flores, Samuel, Pacella, Michael, Kilambi, Krishna Praneeth, Gray, Jeffrey J., Popov, Petr, Grudinin, Sergei, Esquivel-Rodriguez, Juan, Kihara, Daisuke, Zhao, Nan, Korkin, Dmitry, Zhu, Xiaolei, Demerdash, Omar N. A., Mitchell, Julie C., Kanamori, Eiji, Tsuchiya, Yuko, Nakamura, Haruki, Lee, Hasup, Park, Hahnbeom, Seok, Chaok, Sarmiento, Jamica, Liang, Shide, Teraguchi, Shusuke, Standley, Daron M., Shimoyama, Hiromitsu, Terashi, Genki, Takeda-Shitaka, Mayuko, Iwadate, Mitsuo, Umeyama, Hideaki, Beglov, Dmitri, Hall, David R., Kozakov, Dima, Vajda, Sandor, Pierce, Brian G., Hwang, Howook, Vreven, Thom, Weng, Zhiping, Huang, Yangyu, Li, Haotian, Yang, Xiufeng, Ji, Xiaofeng, Liu, Shiyong, Xiao, Yi, Zacharias, Martin, Qin, Sanbo, Zhou, Huan-Xiang, Huang, Sheng-You, Zou, Xiaoqin, Velankar, Sameer, Janin, Joel, Wodak, Shoshana J., and Baker, David

Abstract

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies. Proteins 2013; 81:1980-1987.

Published
2013
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