Biophysics, Biochemistry, Genetics and Molecular Biology, Structural Biology
139
Scopus Publications
15564
Scholar Citations
48
Scholar h-index
102
Scholar i10-index
Scopus Publications
Peroxidasin enables melanoma immune escape by inhibiting natural killer cell cytotoxicity Hsu‐Min Sung, David Bickel, Lena C. M. Krause, Daria Ezeriņa, Christian Ickes, Julian Wojtachnia, Christine S. Gibhardt, Magdalena Shumanska, Khadija Wahni, Andrea Paluschkiwitz, Julia Malo Pueyo, Ekaterina Baranova, Wim Vranken, Hedwig Stanisz, Ioana Stejerean‐Todoran, Michael P. Schön, Joris Messens, Ivan Bogeski Molecular Oncology, 2026 Peroxidasin (PXDN), an extracellular matrix (ECM)‐associated peroxidase, has been implicated in cancer progression. However, its roles in melanoma biology and therapeutic sensitivity remain unclear. Here, we demonstrate that elevated PXDN expression is associated with poor prognosis and reduced survival in melanoma patients. Functional studies revealed that PXDN depletion impairs melanoma cell proliferation, disrupts the cell cycle, and reduces melanoma cell invasive capacities. Furthermore, we found that secreted PXDN modulates anti‐melanoma immunity by enhancing melanoma resistance to natural killer (NK)‐cell‐mediated cytotoxicity. Structural modeling identified a trimeric organization of PXDN, stabilized by disulfide‐linked peroxidase domains. Molecular dynamics simulations identified a previously unknown inhibitory interaction between the PXDN N‐terminal leucine‐rich repeat domain and the NK cell‐activating receptor NKG2‐D type II integral membrane protein (NKG2D). These findings uncover a redox‐independent role for PXDN in promoting immune evasion and tumor progression. Overall, our study highlights PXDN as a critical regulator of melanoma cell biology and a potential therapeutic target for NK‐cell‐based immunotherapy in melanoma and other solid cancers.
Toward a unified framework for determining conformational ensembles of disordered proteins Hamidreza Ghafouri, Pavel Kadeřávek, Ana M. Melo, Maria Cristina Aspromonte, Pau Bernadó, Juan Cortés, Zsuzsanna Dosztányi, Gábor Erdős, Michael Feig, Giacomo Janson, Kresten Lindorff-Larsen, Frans A. A. Mulder, Peter Nagy, Richard Pestell, Damiano Piovesan, Marco Schiavina, Benjamin Schuler, Nathalie Sibille, Giulio Tesei, Peter Tompa, Michele Vendruscolo, Jiri Vondrasek, Wim Vranken, Lukas Zidek, Silvio C. E. Tosatto, Alexander Miguel Monzon Nature Methods, 2026
Unlocking Health Data for Research: Legal, Technical, and Organisational Lessons from a Belgian Interdisciplinary Case Study Audrey Van Scharen, Karen Cruyt, Jeroen Colon, Selene De Sutter, Johnny Duerinck, Ramses Forsyth, Catharina Olsen, Paul Quinn, Konstantina Tzavella, Sonia Van Dooren, Wim Waelput, Arne Witdouck, Pieter Cornu, Jef Vandemeulebroucke, Wim Vranken Journal of Healthcare Informatics Research, 2026 The reuse of clinical health data holds immense promise for advancing medical research, yet remains constrained by complex legal, technical, and organisational barriers. This article examines these challenges through the case study of TumorScope, a Belgian interdisciplinary initiative developing a secure, multimodal data environment for glioblastoma research. Drawing on five years of practical experience integrating imaging, genetic, tissue-based, and clinical datasets, the study identifies key legal, ethical, technical, and operational obstacles to effective data access, linkage, and reuse. Technical issues included fragmented data flows, pseudonymisation complexities, and limited interoperability, while legal and ethical barriers arose from strict interpretations of the General Data Protection Regulation, medical secrecy obligations, and intellectual property constraints. These were compounded by operational challenges such as unclear governance structures, resource limitations, and the limited capacity of Medical Research Ethics Committees to assess data-driven research. The analysis further considers the European Health Data Space Regulation (EHDS) as a potential enabler of responsible secondary data use, while noting uncertainties in its national implementation. Overall, the study demonstrates that meaningful health data reuse requires more than regulatory compliance, it depends on robust governance frameworks, institutional coordination, and sustained investment in infrastructure and expertise. The findings contribute to ongoing debates in healthcare informatics on how to translate the vision of the EHDS into practical, ethically grounded data reuse for patient benefit.
Assessing the relation between protein phosphorylation, AlphaFold3 models, and conformational variability Pathmanaban Ramasamy, Jasper Zuallaert, Lennart Martens, Wim F. Vranken Protein Science, 2026 Proteins perform diverse functions critical to cellular processes. Transitions between functional states are often regulated by post‐translational modifications (PTMs) such as phosphorylation, which dynamically influence protein structure, function, folding, and interactions. Dysregulation of PTMs can therefore contribute to diseases such as cancer and Alzheimer's. However, the structure–function relationship between proteins and their modifications remains poorly understood due to a lack of experimental structural data, the inherent diversity of PTMs, and the dynamic nature of proteins. Recent advances in deep learning, particularly AlphaFold, have transformed protein structure prediction with near‐experimental accuracy. However, it remains unclear whether these models can effectively capture PTM‐driven conformational changes, such as those induced by phosphorylation. Here, we systematically evaluated AlphaFold models (AF2, AF3‐non phospho, and AF3‐phospho) to assess their ability to predict phosphorylation‐induced structural diversity. By analyzing experimentally derived conformational ensembles, we found that all models predominantly aligned with dominant structural states, often failing to capture phosphorylation‐specific conformations. Despite its phosphorylation‐aware design, AF3‐phospho predictions provided only modest improvement over AF2 and AF3‐non phospho predictions. Our findings highlight key challenges in modeling PTM‐driven structural landscapes and underscore the need for more adaptable structure prediction frameworks capable of capturing modification‐induced conformational variability.
Cryo-EM structures of the MnmE-MnmG complex reveal large conformational changes and provide new insights into the mechanism of tRNA modification Laila Maes, Israel Mares-Mejía, Ella Martin, David Bickel, Siemen Claeys, Wim Vranken, Marcus Fislage, Christian Galicia, Wim Versées Nucleic Acids Research, 2025 MnmE and MnmG form a conserved protein complex responsible for the addition of a 5-carboxymethylaminomethyl (cmnm5) group onto the wobble uridine of several transfer RNAs (tRNAs). Within this complex, both proteins collaborate intensively to catalyze a tRNA modification reaction that involves glycine as a substrate in addition to three different cofactors, with FAD and NADH binding to MnmG and methylenetetrahydrofolate (5,10-CH2-THF) to MnmE. Without structures of the MnmEG complex, it remained enigmatic how these substrates and co-factors can be brought together in a concerted manner. Prior small angle X-ray scattering data suggested that the MnmE (α2) and MnmG (β2) homo-dimers can adopt either an α2β2 or α4β2 complex, depending on the nucleotide state of MnmE. Here, we report the cryo-EM structures of the MnmEG complex in the α2β2 and α4β2 oligomeric states. These structures reveal that MnmE undergoes large conformational changes upon interaction with MnmG, resulting in an asymmetric MnmE dimer. In particular, the functionally important C-terminal helix of MnmE relocates from the 5,10-CH2-THF-binding pocket of MnmE to the FAD-binding pocket of MnmG, thus suggesting a mechanism for the transfer of an activated methylene group from one active site to the other. Together, these findings provide crucial new insights into the MnmEG-catalyzed reaction.
Deciphering the RNA recognition by Musashi-1 to design protein and RNA variants for in vitro and in vivo applications Anna Pérez-Ràfols, Guillermo Pérez-Ropero, Linda Cerofolini, Luca Sperotto, Joel Roca-Martínez, R Anahí Higuera-Rodríguez, Pasquale Russomanno, Wolfgang Kaiser, Wim Vranken, U Helena Danielson, Alessandro Provenzani, Tommaso Martelli, Michael Sattler, Jos Buijs, Marco Fragai Nucleic Acids Research, 2025 The Human Musashi-1 (MSI-1) is an RNA-binding protein that recognizes (G/A)U1-3AGU and UAG sequences in diverse RNAs through two RNA Recognition Motif (RRM) domains and regulates the fate of target RNA. Here, we have combined structural biology and computational approaches to analyse the binding of the RRM domains of human MSI-1 with single-stranded and structured RNA ligands. We have used our recently developed computational tool RRMScorer to design a set of substitutions in the MSI-1 protein and the investigated RNA strands to modulate the binding affinity and selectivity. The in silico predictions of the designed protein–RNA interactions are assessed by nuclear magnetic resonance and surface plasmon resonance. These experiments have also been used to study the competition of the two RRM domains of MSI-1 for the same binding site within linear and harpin RNA. Our experimental results shed light on MSI–RNA interactions, thus opening the way for the development of new biomolecules for in vitro and in vivo studies and downstream applications.
RRMScorer: A web server for predicting RNA recognition motif binding preferences Adrian Diaz, Joel Roca-Martínez, Wim Vranken Nucleic Acids Research, 2025 RRMScorer is a web server designed to predict RNA binding preferences for proteins containing RNA recognition motifs (RRMs), the most prevalent RNA binding domain in eukaryotes. By carefully analysing a dataset of 187 RRM–RNA structural complexes, we calculated residue-level binding scores using a probabilistic model derived from amino acid–nucleotide interaction propensities, which are the basis of RRMScorer. The server accepts protein sequences and optional RNA sequences as input, providing detailed outputs, including bar plots, sequence logos, and downloadable CSV/JSON files, to visualize and interpret RNA binding preferences. RRMScorer is particularly useful for studying the impact of single-point mutations and comparing binding preferences across multiple RRM domains. The web server, freely accessible at https://bio2byte.be/rrmscorer without login requirements, offers a user-friendly interface and integrates precomputed predictions for over 1400 RRM-containing proteins. With its ability to provide residue-level insights and accurate predictions, RRMScorer serves as a valuable tool for researchers exploring the functional landscape of RRM–RNA interactions.
In silico identification of archaeal DNA-binding proteins Linus Donvil, Joëlle A J Housmans, Eveline Peeters, Wim Vranken, Gabriele Orlando Bioinformatics, 2025 Motivation The rapid advancement of next-generation sequencing technologies has generated an immense volume of genetic data. However, these data are unevenly distributed, with well-studied organisms being disproportionately represented, while other organisms, such as from archaea, remain significantly underexplored. The study of archaea is particularly challenging due to the extreme environments they inhabit and the difficulties associated with culturing them in the laboratory. Despite these challenges, archaea likely represent a crucial evolutionary link between eukaryotic and prokaryotic organisms, and their investigation could shed light on the early stages of life on Earth. Yet, a significant portion of archaeal proteins are annotated with limited or inaccurate information. Among the various classes of archaeal proteins, DNA-binding proteins are of particular importance. While they represent a large portion of every known proteome, their identification in archaea is complicated by the substantial evolutionary divergence between archaeal and the other better studied organisms. Results To address the challenges of identifying DNA-binding proteins in archaea, we developed Xenusia, a neural network-based tool capable of screening entire archaeal proteomes to identify DNA-binding proteins. Xenusia has proven effective across diverse datasets, including metagenomics data, successfully identifying novel DNA-binding proteins, with experimental validation of its predictions. Availability and implementation Xenusia is available as a PyPI package, with source code accessible at https://github.com/grogdrinker/xenusia, and as a Google Colab web server application at xenusia.ipynb.
Critical assessment of missense variant effect predictors on disease-relevant variant data Ruchir Rastogi, Ryan Chung, Sindy Li, Chang Li, Kyoungyeul Lee, Junwoo Woo, Dong-Wook Kim, Changwon Keum, Giulia Babbi, Pier Luigi Martelli, Castrense Savojardo, Rita Casadio, Kirsley Chennen, Thomas Weber, Olivier Poch, François Ancien, Gabriel Cia, Fabrizio Pucci, Daniele Raimondi, Wim Vranken, Marianne Rooman, Céline Marquet, Tobias Olenyi, Burkhard Rost, Gaia Andreoletti, Akash Kamandula, Yisu Peng, Constantina Bakolitsa, Matthew Mort, David N. Cooper, Timothy Bergquist, Vikas Pejaver, Xiaoming Liu, Predrag Radivojac, Steven E. Brenner, Nilah M. Ioannidis Human Genetics, 2025 Regular, systematic, and independent assessments of computational tools that are used to predict the pathogenicity of missense variants are necessary to evaluate their clinical and research utility and guide future improvements. The Critical Assessment of Genome Interpretation (CAGI) conducts the ongoing Annotate-All-Missense (Missense Marathon) challenge, in which missense variant effect predictors (also called variant impact predictors) are evaluated on missense variants added to disease-relevant databases following the prediction submission deadline. Here we assess predictors submitted to the CAGI 6 Annotate-All-Missense challenge, predictors commonly used in clinical genetics, and recently developed deep learning methods. We examine performance across a range of settings relevant for clinical and research applications, focusing on different subsets of the evaluation data as well as high-specificity and high-sensitivity regimes. Our evaluations reveal notable advances in current methods relative to older, well-cited tools in the field. While meta-predictors tend to outperform their constituent individual predictors, several newer individual predictors perform comparably to commonly used meta-predictors. Predictor performance varies between high-specificity and high-sensitivity regimes, highlighting that different methods may be optimal for different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors trained on pathogenicity labels from curated variant databases often inherit gene-level label imbalances. Our findings help illuminate the clinical and research utility of modern missense variant effect predictors and identify potential areas for future development.
Minimum information guidelines for experiments structurally characterizing intrinsically disordered protein regions Bálint Mészáros, András Hatos, Nicolas Palopoli, Federica Quaglia, Edoardo Salladini, Kim Van Roey, Haribabu Arthanari, Zsuzsanna Dosztányi, Isabella C. Felli, Patrick D. Fischer, Jeffrey C. Hoch, Cy M. Jeffries, Sonia Longhi, Emiliano Maiani, Sandra Orchard, Rita Pancsa, Elena Papaleo, Roberta Pierattelli, Damiano Piovesan, Iva Pritisanac, Luiggi Tenorio, Thibault Viennet, Peter Tompa, Wim Vranken, Silvio C. E. Tosatto, Norman E. Davey Nature Methods, 2023
CAID prediction portal: A comprehensive service for predicting intrinsic disorder and binding regions in proteins Alessio Del Conte, Adel Bouhraoua, Mahta Mehdiabadi, Damiano Clementel, Alexander Miguel Monzon, , Alex S Holehouse, Daniel Griffith, Ryan J Emenecker, Ashwini Patil, Ronesh Sharma, Tatsuhiko Tsunoda, Alok Sharma, Yi Jun Tang, Bin Liu, Claudio Mirabello, Björn Wallner, Burkhard Rost, Dagmar Ilzhöfer, Maria Littmann, Michael Heinzinger, Lea I M Krautheimer, Michael Bernhofer, Liam J McGuffin, Isabelle Callebaut, Tristan Bitard Feildel, Jian Liu, Jianlin Cheng, Zhiye Guo, Jinbo Xu, Sheng Wang, Nawar Malhis, Jörg Gsponer, Chol-Song Kim, Kun-Sop Han, Myong-Chol Ma, Lukasz Kurgan, Sina Ghadermarzi, Akila Katuwawala, Bi Zhao, Zhenling Peng, Zhonghua Wu, Gang Hu, Kui Wang, Md Tamjidul Hoque, Md Wasi Ul Kabir, Michele Vendruscolo, Pietro Sormanni, Min Li, Fuhao Zhang, Pengzhen Jia, Yida Wang, Michail Yu Lobanov, Oxana V Galzitskaya, Wim Vranken, Adrián Díaz, Thomas Litfin, Yaoqi Zhou, Jack Hanson, Kuldip Paliwal, Zsuzsanna Dosztányi, Gábor Erdős, Silvio C E Tosatto, Damiano Piovesan Nucleic Acids Research, 2023
Experiences with a training DSW knowledge model for early-stage researchers Marie-Dominique Devignes, Malika Smaïl-Tabbone, Hrishikesh Dhondge, Roswitha Dolcemascolo, Jose Gavaldá-García, R. Anahí Higuera-Rodriguez, Anna Kravchenko, Joel Roca Martínez, Niki Messini, Anna Pérez-Ràfols, Guillermo Pérez Ropero, Luca Sperotto, Isaure Chauvot de Beauchêne, Wim Vranken Open Research Europe, 2023
Evolution of CRISPR-associated endonucleases as inferred from resurrected proteins Borja Alonso-Lerma, Ylenia Jabalera, Sara Samperio, Matias Morin, Almudena Fernandez, Logan T. Hille, Rachel A. Silverstein, Ane Quesada-Ganuza, Antonio Reifs, Sergio Fernández-Peñalver, Yolanda Benitez, Lucia Soletto, Jose A. Gavira, Adrian Diaz, Wim Vranken, Avencia Sanchez-Mejias, Marc Güell, Francisco J. M. Mojica, Benjamin P. Kleinstiver, Miguel A. Moreno-Pelayo, Lluis Montoliu, Raul Perez-Jimenez Nature Microbiology, 2023
PDBe-KB: collaboratively defining the biological context of structural data PDBe-KB consortium, Mihaly Varadi, Stephen Anyango, David Armstrong, John Berrisford, Preeti Choudhary, Mandar Deshpande, Nurul Nadzirin, Sreenath S Nair, Lukas Pravda, Ahsan Tanweer, Bissan Al-Lazikani, Claudia Andreini, Geoffrey J Barton, David Bednar, Karel Berka, Tom Blundell, Kelly P Brock, Jose Maria Carazo, Jiri Damborsky, Alessia David, Sucharita Dey, Roland Dunbrack, Juan Fernandez Recio, Franca Fraternali, Toby Gibson, Manuela Helmer-Citterich, David Hoksza, Thomas Hopf, David Jakubec, Natarajan Kannan, Radoslav Krivak, Manjeet Kumar, Emmanuel D Levy, Nir London, Jose Ramon Macias, Madhusudhan M Srivatsan, Debora S Marks, Lennart Martens, Stuart A McGowan, Jake E McGreig, Vivek Modi, R Gonzalo Parra, Gerardo Pepe, Damiano Piovesan, Jaime Prilusky, Valeria Putignano, Leandro G Radusky, Pathmanaban Ramasamy, Atilio O Rausch, Nathalie Reuter, Luis A Rodriguez, Nathan J Rollins, Antonio Rosato, Paweł Rubach, Luis Serrano, Gulzar Singh, Petr Skoda, Carlos Oscar S Sorzano, Jan Stourac, Joanna I Sulkowska, Radka Svobodova, Natalia Tichshenko, Silvio C E Tosatto, Wim Vranken, Mark N Wass, Dandan Xue, Daniel Zaidman, Janet Thornton, Michael Sternberg, Christine Orengo, Sameer Velankar Nucleic Acids Research, 2022
Critical assessment of protein intrinsic disorder prediction Marco Necci, Damiano Piovesan, CAID Predictors, Md Tamjidul Hoque, Ian Walsh, Sumaiya Iqbal, Michele Vendruscolo, Pietro Sormanni, Chen Wang, Daniele Raimondi, Ronesh Sharma, Yaoqi Zhou, Thomas Litfin, Oxana Valerianovna Galzitskaya, Michail Yu. Lobanov, Wim Vranken, Björn Wallner, Claudio Mirabello, Nawar Malhis, Zsuzsanna Dosztányi, Gábor Erdős, Bálint Mészáros, Jianzhao Gao, Kui Wang, Gang Hu, Zhonghua Wu, Alok Sharma, Jack Hanson, Kuldip Paliwal, Isabelle Callebaut, Tristan Bitard-Feildel, Gabriele Orlando, Zhenling Peng, Jinbo Xu, Sheng Wang, David T. Jones, Domenico Cozzetto, Fanchi Meng, Jing Yan, Jörg Gsponer, Jianlin Cheng, Tianqi Wu, Lukasz Kurgan, DisProt Curators, Vasilis J. Promponas, Stella Tamana, Cristina Marino-Buslje, Elizabeth Martínez-Pérez, Anastasia Chasapi, Christos Ouzounis, A. Keith Dunker, Andrey V. Kajava, Jeremy Y. Leclercq, Burcu Aykac-Fas, Matteo Lambrughi, Emiliano Maiani, Elena Papaleo, Lucia Beatriz Chemes, Lucía Álvarez, Nicolás S. González-Foutel, Valentin Iglesias, Jordi Pujols, Salvador Ventura, Nicolás Palopoli, Guillermo Ignacio Benítez, Gustavo Parisi, Claudio Bassot, Arne Elofsson, Sudha Govindarajan, John Lamb, Marco Salvatore, András Hatos, Alexander Miguel Monzon, Martina Bevilacqua, Ivan Mičetić, Giovanni Minervini, Lisanna Paladin, Federica Quaglia, Emanuela Leonardi, Norman Davey, Tamas Horvath, Orsolya Panna Kovacs, Nikoletta Murvai, Rita Pancsa, Eva Schad, Beata Szabo, Agnes Tantos, Sandra Macedo-Ribeiro, Jose Antonio Manso, Pedro José Barbosa Pereira, Radoslav Davidović, Nevena Veljkovic, Borbála Hajdu-Soltész, Mátyás Pajkos, Tamás Szaniszló, Mainak Guharoy, Tamas Lazar, Mauricio Macossay-Castillo, Peter Tompa, Silvio C. E. Tosatto Nature Methods, 2021
MobiDB: Intrinsically disordered proteins in 2021 Damiano Piovesan, Marco Necci, Nahuel Escobedo, Alexander Miguel Monzon, András Hatos, Ivan Mičetić, Federica Quaglia, Lisanna Paladin, Pathmanaban Ramasamy, Zsuzsanna Dosztányi, Wim F Vranken, Norman E Davey, Gustavo Parisi, Monika Fuxreiter, Silvio C E Tosatto Nucleic Acids Research, 2021
Megabodies expand the nanobody toolkit for protein structure determination by single-particle cryo-EM Tomasz Uchański, Simonas Masiulis, Baptiste Fischer, Valentina Kalichuk, Uriel López-Sánchez, Eleftherios Zarkadas, Miriam Weckener, Andrija Sente, Philip Ward, Alexandre Wohlkönig, Thomas Zögg, Han Remaut, James H. Naismith, Hugues Nury, Wim Vranken, A. Radu Aricescu, Els Pardon, Jan Steyaert Nature Methods, 2021
PDBE-KB: A community-driven resource for structural and functional annotations PDBe-KB consortium, Mihaly Varadi, John Berrisford, Mandar Deshpande, Sreenath S Nair, Aleksandras Gutmanas, David Armstrong, Lukas Pravda, Bissan Al-Lazikani, Stephen Anyango, Geoffrey J Barton, Karel Berka, Tom Blundell, Neera Borkakoti, Jose Dana, Sayoni Das, Sucharita Dey, Patrizio Di Micco, Franca Fraternali, Toby Gibson, Manuela Helmer-Citterich, David Hoksza, Liang-Chin Huang, Rishabh Jain, Harry Jubb, Christos Kannas, Natarajan Kannan, Jaroslav Koca, Radoslav Krivak, Manjeet Kumar, Emmanuel D Levy, F Madeira, M S Madhusudhan, Henry J Martell, Stuart MacGowan, Jake E McGreig, Saqib Mir, Abhik Mukhopadhyay, Luca Parca, Typhaine Paysan-Lafosse, Leandro Radusky, Antonio Ribeiro, Luis Serrano, Ian Sillitoe, Gulzar Singh, Petr Skoda, Radka Svobodova, Jonathan Tyzack, Alfonso Valencia, Eloy Villasclaras Fernandez, Wim Vranken, Mark Wass, Janet Thornton, Michael Sternberg, Christine Orengo, Sameer Velankar Nucleic Acids Research, 2020
DisProt: Intrinsic protein disorder annotation in 2020 András Hatos, Borbála Hajdu-Soltész, Alexander M Monzon, Nicolas Palopoli, Lucía Álvarez, Burcu Aykac-Fas, Claudio Bassot, Guillermo I Benítez, Martina Bevilacqua, Anastasia Chasapi, Lucia Chemes, Norman E Davey, Radoslav Davidović, A Keith Dunker, Arne Elofsson, Julien Gobeill, Nicolás S González Foutel, Govindarajan Sudha, Mainak Guharoy, Tamas Horvath, Valentin Iglesias, Andrey V Kajava, Orsolya P Kovacs, John Lamb, Matteo Lambrughi, Tamas Lazar, Jeremy Y Leclercq, Emanuela Leonardi, Sandra Macedo-Ribeiro, Mauricio Macossay-Castillo, Emiliano Maiani, José A Manso, Cristina Marino-Buslje, Elizabeth Martínez-Pérez, Bálint Mészáros, Ivan Mičetić, Giovanni Minervini, Nikoletta Murvai, Marco Necci, Christos A Ouzounis, Mátyás Pajkos, Lisanna Paladin, Rita Pancsa, Elena Papaleo, Gustavo Parisi, Emilie Pasche, Pedro J Barbosa Pereira, Vasilis J Promponas, Jordi Pujols, Federica Quaglia, Patrick Ruch, Marco Salvatore, Eva Schad, Beata Szabo, Tamás Szaniszló, Stella Tamana, Agnes Tantos, Nevena Veljkovic, Salvador Ventura, Wim Vranken, Zsuzsanna Dosztányi, Peter Tompa, Silvio C E Tosatto, Damiano Piovesan Nucleic Acids Research, 2020
Structural basis of the subcellular topology landscape of Escherichia coli Maria S. Loos, Reshmi Ramakrishnan, Wim Vranken, Alexandra Tsirigotaki, Evrydiki-Pandora Tsare, Valentina Zorzini, Jozefien De Geyter, Biao Yuan, Ioannis Tsamardinos, Maria Klappa, Joost Schymkowitz, Frederic Rousseau, Spyridoula Karamanou, Anastassios Economou Frontiers in Microbiology, 2019
An intrinsically disordered proteins community for ELIXIR [version 1; peer review: 2 approved] Norman E. Davey, M. Madan Babu, Martin Blackledge, Alan Bridge, Salvador Capella-Gutierrez, Zsuzsanna Dosztanyi, Rachel Drysdale, Richard J. Edwards, Arne Elofsson, Isabella C. Felli, Toby J. Gibson, Aleksandras Gutmanas, John M. Hancock, Jen Harrow, Desmond Higgins, Cy M. Jeffries, Philippe Le Mercier, Balint Mészáros, Marco Necci, Cedric Notredame, Sandra Orchard, Christos A. Ouzounis, Rita Pancsa, Elena Papaleo, Roberta Pierattelli, Damiano Piovesan, Vasilis J. Promponas, Patrick Ruch, Gabriella Rustici, Pedro Romero, Sirarat Sarntivijai, Gary Saunders, Benjamin Schuler, Malvika Sharan, Denis C. Shields, Joel L. Sussman, Jonathan A. Tedds, Peter Tompa, Michael Turewicz, Jiri Vondrasek, Wim F. Vranken, Bonnie Ann Wallace, Kanin Wichapong, Silvio C. E. Tosatto F1000research, 2019
Corrigendum: DisProt 7.0: a major update of the database of disordered proteins Damiano Piovesan, Francesco Tabaro, Ivan Mičetić, Marco Necci, Federica Quaglia, Christopher J. Oldfield, Maria Cristina Aspromonte, Norman E. Davey, Radoslav Davidović, Zsuzsanna Dosztányi, Arne Elofsson, Alessandra Gasparini, András Hatos, Andrey V. Kajava, Lajos Kalmar, Emanuela Leonardi, Tamas Lazar, Sandra Macedo-Ribeiro, Mauricio Macossay-Castillo, Attila Meszaros, Giovanni Minervini, Nikoletta Murvai, Jordi Pujols, Daniel B. Roche, Edoardo Salladini, Eva Schad, Antoine Schramm, Beata Szabo, Agnes Tantos, Fiorella Tonello, Konstantinos D. Tsirigos, Nevena Veljković, Salvador Ventura, Wim Vranken, Per Warholm, Vladimir N. Uversky, A. Keith Dunker, Sonia Longhi, Peter Tompa, Silvio C.E. Tosatto Nucleic Acids Research, 2017
DisProt 7.0: A major update of the database of disordered proteins Damiano Piovesan, Francesco Tabaro, Ivan Mičetić, Marco Necci, Federica Quaglia, Christopher J. Oldfield, Maria Cristina Aspromonte, Norman E. Davey, Radoslav Davidović, Zsuzsanna Dosztányi, Arne Elofsson, Alessandra Gasparini, András Hatos, Andrey V. Kajava, Lajos Kalmar, Emanuela Leonardi, Tamas Lazar, Sandra Macedo-Ribeiro, Mauricio Macossay-Castillo, Attila Meszaros, Giovanni Minervini, Nikoletta Murvai, Jordi Pujols, Daniel B. Roche, Edoardo Salladini, Eva Schad, Antoine Schramm, Beata Szabo, Agnes Tantos, Fiorella Tonello, Konstantinos D. Tsirigos, Nevena Veljković, Salvador Ventura, Wim Vranken, Per Warholm, Vladimir N. Uversky, A. Keith Dunker, Sonia Longhi, Peter Tompa, Silvio C.E. Tosatto Nucleic Acids Research, 2017
The second round of Critical Assessment of Automated Structure Determination of Proteins by NMR: CASD-NMR-2013 Antonio Rosato, Wim Vranken, Rasmus H. Fogh, Timothy J. Ragan, Roberto Tejero, Kari Pederson, Hsiau-Wei Lee, James H. Prestegard, Adelinda Yee, Bin Wu, Alexander Lemak, Scott Houliston, Cheryl H. Arrowsmith, Michael Kennedy, Thomas B. Acton, Rong Xiao, Gaohua Liu, Gaetano T. Montelione, Geerten W. Vuister Journal of Biomolecular NMR, 2015
NMR Exchange Format: A unified and open standard for representation of NMR restraint data Aleksandras Gutmanas, Paul D Adams, Benjamin Bardiaux, Helen M Berman, David A Case, Rasmus H Fogh, Peter Güntert, Pieter M S Hendrickx, Torsten Herrmann, Gerard J Kleywegt, Naohiro Kobayashi, Oliver F Lange, John L Markley, Gaetano T Montelione, Michael Nilges, Timothy J Ragan, Charles D Schwieters, Roberto Tejero, Eldon L Ulrich, Sameer Velankar, Wim F Vranken, Jonathan R Wedell, John Westbrook, David S Wishart, Geerten W Vuister Nature Structural and Molecular Biology, 2015
Recommendations of the wwPDB NMR validation task force Gaetano T. Montelione, Michael Nilges, Ad Bax, Peter Güntert, Torsten Herrmann, Jane S. Richardson, Charles D. Schwieters, Wim F. Vranken, Geerten W. Vuister, David S. Wishart, Helen M. Berman, Gerard J. Kleywegt, John L. Markley Structure, 2013
WeNMR: Structural Biology on the Grid Tsjerk A. Wassenaar, Marc van Dijk, Nuno Loureiro-Ferreira, Gijs van der Schot, Sjoerd J. de Vries, Christophe Schmitz, Johan van der Zwan, Rolf Boelens, Andrea Giachetti, Lucio Ferella, Antonio Rosato, Ivano Bertini, Torsten Herrmann, Hendrik R. A. Jonker, Anurag Bagaria, Victor Jaravine, Peter Güntert, Harald Schwalbe, Wim F. Vranken, Jurgen F. Doreleijers, Gert Vriend, Geerten W. Vuister, Daniel Franke, Alexey Kikhney, Dmitri I. Svergun, Rasmus H. Fogh, John Ionides, Ernest D. Laue, Chris Spronk, Simonas Jurkša, Marco Verlato, Simone Badoer, Stefano Dal Pra, Mirco Mazzucato, Eric Frizziero, Alexandre M. J. J. Bonvin Journal of Grid Computing, 2012
CING: An integrated residue-based structure validation program suite Jurgen F. Doreleijers, Alan W. Sousa da Silva, Elmar Krieger, Sander B. Nabuurs, Christian A. E. M. Spronk, Tim J. Stevens, Wim F. Vranken, Gert Vriend, Geerten W. Vuister Journal of Biomolecular NMR, 2012
Blind testing of routine, fully automated determination of protein structures from nmr data Antonio Rosato, James M. Aramini, Cheryl Arrowsmith, Anurag Bagaria, David Baker, Andrea Cavalli, Jurgen F. Doreleijers, Alexander Eletsky, Andrea Giachetti, Paul Guerry, Aleksandras Gutmanas, Peter Güntert, Yunfen He, Torsten Herrmann, Yuanpeng J. Huang, Victor Jaravine, Hendrik R.A. Jonker, Michael A. Kennedy, Oliver F. Lange, Gaohua Liu, Thérèse E. Malliavin, Rajeswari Mani, Binchen Mao, Gaetano T. Montelione, Michael Nilges, Paolo Rossi, Gijs van der Schot, Harald Schwalbe, Thomas A. Szyperski, Michele Vendruscolo, Robert Vernon, Wim F. Vranken, Sjoerd de Vries, Geerten W. Vuister, Bin Wu, Yunhuang Yang, Alexandre M.J.J. Bonvin Structure, 2012
PDBe: Protein Data Bank in Europe S. Velankar, Y. Alhroub, C. Best, S. Caboche, M. J. Conroy, J. M. Dana, M. A. Fernandez Montecelo, G. van Ginkel, A. Golovin, S. P. Gore, A. Gutmanas, P. Haslam, P. M. S. Hendrickx, E. Heuson, M. Hirshberg, M. John, I. Lagerstedt, S. Mir, L. E. Newman, T. J. Oldfield, A. Patwardhan, L. Rinaldi, G. Sahni, E. Sanz-Garcia, S. Sen, R. Slowley, A. Suarez-Uruena, G. J. Swaminathan, M. F. Symmons, W. F. Vranken, M. Wainwright, G. J. Kleywegt Nucleic Acids Research, 2012
WeNMR: Structural biology on the grid Ceur Workshop Proceedings, 2011
EUROCarbDB: An open-access platform for glycoinformatics C.-W. von der Lieth, A. A. Freire, D. Blank, M. P. Campbell, A. Ceroni, D. R. Damerell, A. Dell, R. A. Dwek, B. Ernst, R. Fogh, M. Frank, H. Geyer, R. Geyer, M. J. Harrison, K. Henrick, S. Herget, W. E. Hull, J. Ionides, H. J. Joshi, J. P. Kamerling, B. R. Leeflang, T. Lutteke, M. Lundborg, K. Maass, A. Merry, R. Ranzinger, J. Rosen, L. Royle, P. M. Rudd, S. Schloissnig, R. Stenutz, W. F. Vranken, G. Widmalm, S. M. Haslam Glycobiology, 2011
PDBe: Protein data bank in Europe S. Velankar, Y. Alhroub, A. Alili, C. Best, H. C. Boutselakis, S. Caboche, M. J. Conroy, J. M. Dana, G. van Ginkel, A. Golovin, S. P. Gore, A. Gutmanas, P. Haslam, M. Hirshberg, M. John, I. Lagerstedt, S. Mir, L. E. Newman, T. J. Oldfield, C. J. Penkett, J. Pineda-Castillo, L. Rinaldi, G. Sahni, G. Sawka, S. Sen, R. Slowley, A. W. Sousa da Silva, A. Suarez-Uruena, G. J. Swaminathan, M. F. Symmons, W. F. Vranken, M. Wainwright, G. J. Kleywegt Nucleic Acids Research, 2011
Straightforward and complete deposition of NMR data to the PDBe Christopher J. Penkett, Glen van Ginkel, Sameer Velankar, Jawahar Swaminathan, Eldon L. Ulrich, Steve Mading, Tim J. Stevens, Rasmus H. Fogh, Aleksandras Gutmanas, Gerard J. Kleywegt, Kim Henrick, Wim F. Vranken Journal of Biomolecular NMR, 2010
PDBe: Protein Data Bank in Europe S. Velankar, C. Best, B. Beuth, C. H. Boutselakis, N. Cobley, A. W. Sousa Da Silva, D. Dimitropoulos, A. Golovin, M. Hirshberg, M. John, E. B. Krissinel, R. Newman, T. Oldfield, A. Pajon, C. J. Penkett, J. Pineda-Castillo, G. Sahni, S. Sen, R. Slowley, A. Suarez-Uruena, J. Swaminathan, G. van Ginkel, W. F. Vranken, K. Henrick, G. J. Kleywegt Nucleic Acids Research, 2009
CASD-NMR: Critical assessment of automated structure determination by NMR Antonio Rosato, Anurag Bagaria, David Baker, Benjamin Bardiaux, Andrea Cavalli, Jurgen F Doreleijers, Andrea Giachetti, Paul Guerry, Peter Güntert, Torsten Herrmann, Yuanpeng J Huang, Hendrik R A Jonker, Binchen Mao, Thérèse E Malliavin, Gaetano T Montelione, Michael Nilges, Srivatsan Raman, Gijs van der Schot, Wim F Vranken, Geerten W Vuister, Alexandre M J J Bonvin Nature Methods, 2009
Remediation of the protein data bank archive K. Henrick, Z. Feng, W. F. Bluhm, D. Dimitropoulos, J. F. Doreleijers, S. Dutta, J. L. Flippen-Anderson, J. Ionides, C. Kamada, E. Krissinel, C. L. Lawson, J. L. Markley, H. Nakamura, R. Newman, Y. Shimizu, J. Swaminathan, S. Velankar, J. Ory, E. L. Ulrich, W. Vranken, J. Westbrook, R. Yamashita, H. Yang, J. Young, M. Yousufuddin, H. M. Berman Nucleic Acids Research, 2008
SPINE bioinformatics and data-management aspects of high-throughput structural biology S. Albeck, P. Alzari, C. Andreini, L. Banci, I. M. Berry, I. Bertini, C. Cambillau, B. Canard, L. Carter, S. X. Cohen, J. M. Diprose, O. Dym, R. M. Esnouf, C. Felder, F. Ferron, F. Guillemot, R. Hamer, M. Ben Jelloul, R. A. Laskowski, T. Laurent, S. Longhi, R. Lopez, C. Luchinat, H. Malet, T. Mochel, R. J. Morris, L. Moulinier, T. Oinn, A. Pajon, Y. Peleg, A. Perrakis, O. Poch, J. Prilusky, A. Rachedi, R. Ripp, A. Rosato, I. Silman, D. I. Stuart, J. L. Sussman, J.-C. Thierry, J. D. Thompson, J. M. Thornton, T. Unger, B. Vaughan, W. Vranken, J. D. Watson, G. Whamond, K. Henrick Acta Crystallographica Section D Biological Crystallography, 2006
Design of a data model for developing laboratory information management and analysis systems for protein production Anne Pajon, John Ionides, Jon Diprose, Joël Fillon, Rasmus Fogh, Alun W. Ashton, Helen Berman, Wayne Boucher, Miroslaw Cygler, Emeline Deleury, Robert Esnouf, Joël Janin, Rosalind Kim, Isabelle Krimm, Catherine L. Lawson, Eric Oeuillet, Anne Poupon, Stéphane Raymond, Tim Stevens, Herman van Tilbeurgh, John Westbrook, Peter Wood, Eldon Ulrich, Wim Vranken, Li Xueli, Ernest Laue, David I. Stuart, Kim Henrick Proteins Structure Function and Genetics, 2005
DRESS: A Database of REfined Solution NMR Structures Sander B. Nabuurs, Aart J. Nederveen, Wim Vranken, Jurgen F. Doreleijers, Alexandre M.J.J. Bonvin, Geerten W. Vuister, Gert Vriend, Christian A.E.M. Spronk Proteins Structure Function and Genetics, 2004
E-MSD: An integrated data resource for bioinformatics Nucleic Acids Research, 2004
The ccpn project: An interim report on a data model for the nmr community Rasmus Fogh, John Ionides, Eldon Ulrich, Wayne Boucher, Wim Vranken, Jens P. Linge, Michael Habeck, Wolfgang Rieping, T.N. Bhat, John Westbrook, Kim Henrick, Gary Gilliland, Helen Berman, Janet Thornton, Michael Nilges, John Markley, Ernest Laue Nature Structural Biology, 2002
Toward a unified framework for determining conformational ensembles of disordered proteins H Ghafouri, P Kadeřávek, AM Melo, MC Aspromonte, P Bernadó, J Cortés, ... Nature Methods, 1-15 , 2026 2026 Citations: 11
Unlocking health data for research: legal, technical, and organisational lessons from a Belgian interdisciplinary case study A Van Scharen, K Cruyt, J Colon, S De Sutter, J Duerinck, R Forsyth, ... Journal of Healthcare Informatics Research 10 (1), 179-208 , 2026 2026 Citations: 5
Peroxidasin enables melanoma immune escape by inhibiting natural killer cell cytotoxicity HM Sung, D Bickel, LCM Krause, D Ezeriņa, C Ickes, J Wojtachnia, ... Molecular Oncology , 2026 2026
On the state of protein function prediction: a report on the fourth CAFA challenge R Ramola, MC De Paolis Klauza, D Piovesan, Y Peng, P Joshi, ... bioRxiv, 2026.05. 06.722942 , 2026 2026 Citations: 3
The NMR Exchange Format (NEF): Specification and Applications E Ploskon, K Baskaran, R Tejero, CD Schwieters, B Bardiaux, P Guentert, ... bioRxiv, 2026.04. 22.715536 , 2026 2026 Citations: 1
Assessing the relation between protein phosphorylation, AlphaFold3 models, and conformational variability P Ramasamy, J Zuallaert, L Martens, WF Vranken Protein Science 35 (1), e70376 , 2026 2026 Citations: 4
Chimeric Designs to Investigate G Protein-Coupled Receptors C Crauwels, W Vranken Computational structural biology , 2025 2025
Defining Biophysical Constraints in the Evolution of β-Lactamases through Ancestral Sequence Reconstruction SL Heidig, R Malempré, WF Vranken EMBO Workshop: Computational structural biology , 2025 2025
Cryo-EM structures of the MnmE–MnmG complex reveal large conformational changes and provide new insights into the mechanism of tRNA modification L Maes, I Mares-Mejía, E Martin, D Bickel, S Claeys, W Vranken, ... Nucleic Acids Research 53 (16), gkaf824 , 2025 2025 Citations: 4
Deciphering the RNA recognition by Musashi-1 to design protein and RNA variants for in vitro and in vivo applications A Pérez-Ràfols, G Pérez-Ropero, L Cerofolini, L Sperotto, ... Nucleic Acids Research 53 (15), gkaf741 , 2025 2025 Citations: 1
Viral replication modulated by hallmark conformational ensembles: how AlphaFold-predicted features of RdRp folding dynamics combined with intrinsic disorder-mediated function … R Tahzima, J Charon, A Diaz, K De Jonghe, S Massart, T Michon, ... Frontiers in Virology 5, 1501616 , 2025 2025 Citations: 1
GPCRchimeraDB: A database of chimeric G protein-coupled receptors (GPCRs) to assist their design C Crauwels, A Díaz, W Vranken Journal of Molecular Biology 437 (14), 169164 , 2025 2025 Citations: 2
RRMScorer: A web server for predicting RNA recognition motif binding preferences A Diaz, J Roca-Martínez, W Vranken Nucleic Acids Research 53 (W1), W503-W511 , 2025 2025
Do you speak protein?: Understanding and predicting protein thermal stability A Bouillon, W Vranken Bioinformatics in Bergen , 2025 2025
In silico identification of archaeal DNA-binding proteins L Donvil, JAJ Housmans, E Peeters, W Vranken, G Orlando Bioinformatics 41 (5), btaf169 , 2025 2025 Citations: 1
Assessing the relation between protein phosphorylation, AlphaFold3 models and conformational variability P Ramasamy, J Zuallaert, L Martens, WF Vranken bioRxiv, 2025.04. 14.648669 , 2025 2025 Citations: 5
Critical assessment of missense variant effect predictors on disease-relevant variant data R Rastogi, R Chung, S Li, C Li, K Lee, J Woo, DW Kim, C Keum, G Babbi, ... Human genetics 144 (2), 281-293 , 2025 2025 Citations: 38
Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics J Gavalda-Garcia, B Dixit, A Diaz, A Ghysels, W Vranken Journal of Molecular Biology 437 (2), 168900 , 2025 2025 Citations: 16
Protein dynamics and conformational heterogeneity in solution are not well captured by AlphaFold and other computational approaches W Vranken, B Dixit, J Gavalda-Garcia, A Ghysels ISMB/ECCB 2025 , 2025 2025
Combining evolution and protein language models for an interpretable cancer driver mutation prediction with D2Deep K Tzavella, A Diaz, C Olsen, W Vranken Briefings in bioinformatics 26 (1), bbae664 , 2025 2025 Citations: 9
MOST CITED SCHOLAR PUBLICATIONS
The CCPN data model for NMR spectroscopy: development of a software pipeline WF Vranken, W Boucher, TJ Stevens, RH Fogh, A Pajon, M Llinas, ... Proteins: structure, function, and bioinformatics 59 (4), 687-696 , 2005 2005 Citations: 3806
ACPYPE-Antechamber python parser interface AW Sousa da Silva, WF Vranken BMC research notes 5 (1), 367 , 2012 2012 Citations: 3517
Determination of secondary structure populations in disordered states of proteins using nuclear magnetic resonance chemical shifts C Camilloni, A De Simone, WF Vranken, M Vendruscolo Biochemistry 51 (11), 2224-2231 , 2012 2012 Citations: 447
RECOORD: a recalculated coordinate database of 500+ proteins from the PDB using restraints from the BioMagResBank AJ Nederveen, JF Doreleijers, W Vranken, Z Miller, CAEM Spronk, ... PROTEINS: Structure, Function, and Bioinformatics 59 (4), 662-672 , 2005 2005 Citations: 390
DisProt 7.0: a major update of the database of disordered proteins D Piovesan, F Tabaro, I Mičetić, M Necci, F Quaglia, CJ Oldfield, ... Nucleic acids research 45 (D1), D219-D227 , 2017 2017 Citations: 355
PDBe: protein data bank in Europe S Velankar, C Best, B Beuth, CH Boutselakis, N Cobley, ... Nucleic acids research 38 (suppl_1), D308-D317 , 2010 2010 Citations: 331
DisProt: intrinsic protein disorder annotation in 2020 A Hatos, B Hajdu-Soltész, AM Monzon, N Palopoli, L Álvarez, ... Nucleic acids research 48 (D1), D269-D276 , 2020 2020 Citations: 292
MobiDB: intrinsically disordered proteins in 2021 D Piovesan, M Necci, N Escobedo, AM Monzon, A Hatos, I Mičetić, ... Nucleic acids research 49 (D1), D361-D367 , 2021 2021 Citations: 255
MobiDB 3.0: more annotations for intrinsic disorder, conformational diversity and interactions in proteins D Piovesan, F Tabaro, L Paladin, M Necci, I Mičetić, C Camilloni, N Davey, ... Nucleic acids research 46 (D1), D471-D476 , 2018 2018 Citations: 239
Determination of the three-dimensional solution structure of Raphanus sativus antifungal protein 1 by 1H NMR F Fant, W Vranken, W Broekaert, F Borremans Journal of molecular biology 279 (1), 257-270 , 1998 1998 Citations: 230
DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins D Raimondi, I Tanyalcin, J Ferté, A Gazzo, G Orlando, T Lenaerts, ... Nucleic acids research 45 (W1), W201-W206 , 2017 2017 Citations: 221
WeNMR: structural biology on the grid TA Wassenaar, M Van Dijk, N Loureiro-Ferreira, G Van Der Schot, ... Journal of Grid Computing 10 (4), 743-767 , 2012 2012 Citations: 221
From protein sequence to dynamics and disorder with DynaMine E Cilia, R Pancsa, P Tompa, T Lenaerts, WF Vranken Nature communications 4 (1), 2741 , 2013 2013 Citations: 200
The ACPYPE web server for small-molecule MD topology generation L Kagami, A Wilter, A Diaz, W Vranken Bioinformatics 39 (6), btad350 , 2023 2023 Citations: 199
Recommendations of the wwPDB NMR validation task force GT Montelione, M Nilges, A Bax, P Güntert, T Herrmann, JS Richardson, ... Structure 21 (9), 1563-1570 , 2013 2013 Citations: 191
The DynaMine webserver: predicting protein dynamics from sequence E Cilia, R Pancsa, P Tompa, T Lenaerts, WF Vranken Nucleic acids research 42 (W1), W264-W270 , 2014 2014 Citations: 188
Remediation of the protein data bank archive K Henrick, Z Feng, WF Bluhm, D Dimitropoulos, JF Doreleijers, S Dutta, ... Nucleic acids research 36 (suppl_1), D426-D433 , 2007 2007 Citations: 183
Megabodies expand the nanobody toolkit for protein structure determination by single-particle cryo-EM T Uchański, S Masiulis, B Fischer, V Kalichuk, U López-Sánchez, ... Nature methods 18 (1), 60-68 , 2021 2021 Citations: 180
E-MSD: the European bioinformatics institute macromolecular structure database H Boutselakis, D Dimitropoulos, J Fillon, A Golovin, K Henrick, A Hussain, ... Nucleic acids research 31 (1), 458-462 , 2003 2003 Citations: 149
The CCPN project: an interim report on a data model for the NMR community R Fogh, J Ionides, E Ulrich, W Boucher, W Vranken, JP Linge, M Habeck, ... nature structural biology 9 (6), 416-418 , 2002 2002 Citations: 149