Wim Vranken

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, et al.
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, et al.
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, et al.
Journal of Healthcare Informatics Research, 2026
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, et al.
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, et al.
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.
GPCRchimeraDB: A Database of Chimeric G Protein-Coupled Receptors (GPCRs) to Assist Their Design
Charlotte Crauwels, Adrián Díaz, Wim Vranken
Journal of Molecular Biology, 2025
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, et al.
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.
Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics
Jose Gavalda-Garcia, Bhawna Dixit, Adrián Díaz, An Ghysels, Wim Vranken
Journal of Molecular Biology, 2025
Combining evolution and protein language models for an interpretable cancer driver mutation prediction with D2Deep
Konstantina Tzavella, Adrian Diaz, Catharina Olsen, Wim Vranken
Briefings in Bioinformatics, 2025
Viral replication modulated by hallmark conformational ensembles: how AlphaFold-predicted features of RdRp folding dynamics combined with intrinsic disorder-mediated function enable RNA virus discovery
Rachid Tahzima, Justine Charon, Adrian Diaz, Kris De Jonghe, Sebastien Massart, et al.
Frontiers in Virology, 2025
Bio2Byte Tools deployment as a Python package and Galaxy tool to predict protein biophysical properties
Jose Gavalda-Garcia, Adrián Díaz, Wim Vranken
Bioinformatics, 2024
Data-driven probabilistic definition of the low energy conformational states of protein residues
Jose Gavalda-Garcia, David Bickel, Joel Roca-Martinez, Daniele Raimondi, Gabriele Orlando, et al.
Nar Genomics and Bioinformatics, 2024
Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences
David Bickel, Wim Vranken
Journal of Chemical Theory and Computation, 2024
Large-scale structure-informed multiple sequence alignment of proteins with SIMSApiper
Charlotte Crauwels, Sophie-Luise Heidig, Adrián Díaz, Wim F Vranken
Bioinformatics, 2024
Analysis of the inter-domain orientation of tandem RRM domains with diverse linkers: connecting experimental with AlphaFold2 predicted models
Joel Roca-Martínez, Hyun-Seo Kang, Michael Sattler, Wim Vranken
Nar Genomics and Bioinformatics, 2024
Leucine Motifs Stabilize Residual Helical Structure in Disordered Proteins
Uroš Zavrtanik, Tadej Medved, Samo Purič, Wim Vranken, Jurij Lah, et al.
Journal of Molecular Biology, 2024
Conformational dynamics of α-1 acid glycoprotein (AGP) in cancer: A comparative study of glycosylated and unglycosylated AGP
Bhawna Dixit, Wim Vranken, An Ghysels
Proteins Structure Function and Bioinformatics, 2024
Deep mutational scanning of essential bacterial proteins can guide antibiotic development
Liselot Dewachter, Aaron N. Brooks, Katherine Noon, Charlotte Cialek, Alia Clark-ElSayed, et al.
Nature Communications, 2023
Author Correction: Deep mutational scanning of essential bacterial proteins can guide antibiotic development (Nature Communications, (2023), 14, 1, (241), 10.1038/s41467-023-35940-3)
Liselot Dewachter, Aaron N. Brooks, Katherine Noon, Charlotte Cialek, Alia Clark-ElSayed, et al.
Nature Communications, 2023
Author Correction: Massively parallel interrogation of protein fragment secretability using SECRiFY reveals features influencing secretory system transit (Nature Communications, (2021), 12, 1, (6414), 10.1038/s41467-021-26720-y)
Morgane Boone, Pathmanaban Ramasamy, Jasper Zuallaert, Robbin Bouwmeester, Berre Van Moer, et al.
Nature Communications, 2023
Minimum information guidelines for experiments structurally characterizing intrinsically disordered protein regions
Bálint Mészáros, András Hatos, Nicolas Palopoli, Federica Quaglia, Edoardo Salladini, et al.
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, et al.
Nucleic Acids Research, 2023
The ACPYPE web server for small-molecule MD topology generation
Luciano Kagami, Alan Wilter, Adrian Diaz, Wim Vranken
Bioinformatics, 2023
New simulation insights on the structural transition mechanism of bovine rhodopsin activation
Kamalesh Damodaran, Taushif Khan, David Bickel, Sreeshma Jaya, Wim F. Vranken, et al.
Proteins Structure Function and Bioinformatics, 2023
Deciphering the RRM-RNA recognition code: A computational analysis
Joel Roca-Martínez, Hrishikesh Dhondge, Michael Sattler, Wim F. Vranken
Plos Computational Biology, 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, et al.
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, et al.
Nature Microbiology, 2023
Evolutionary adaptation of the protein folding pathway for secretability
Dries Smets, Alexandra Tsirigotaki, Jochem H Smit, Srinath Krishnamurthy, Athina G Portaliou, et al.
EMBO Journal, 2022
Panoramic Perspective on Human Phosphosites
Pathmanaban Ramasamy, Elien Vandermarliere, Wim F. Vranken, Lennart Martens
Journal of Proteome Research, 2022
Challenges in describing the conformation and dynamics of proteins with ambiguous behavior
Joel Roca-Martinez, Tamas Lazar, Jose Gavalda-Garcia, David Bickel, Rita Pancsa, et al.
Frontiers in Molecular Biosciences, 2022
Prediction of Disordered Regions in Proteins with Recurrent Neural Networks and Protein Dynamics
Gabriele Orlando, Daniele Raimondi, Francesco Codicè, Francesco Tabaro, Wim Vranken
Journal of Molecular Biology, 2022
MutaFrame - an interpretative visualization framework for deleteriousness prediction of missense variants in the human exome
François Ancien, Fabrizio Pucci, Wim Vranken, Marianne Rooman
Bioinformatics, 2022
PDBe-KB: collaboratively defining the biological context of structural data
PDBe-KB consortium, Mihaly Varadi, Stephen Anyango, David Armstrong, John Berrisford, et al.
Nucleic Acids Research, 2022
Online biophysical predictions for SARS-CoV-2 proteins
Luciano Kagami, Joel Roca-Martínez, Jose Gavaldá-García, Pathmanaban Ramasamy, K. Anton Feenstra, et al.
BMC Molecular and Cell Biology, 2021
Massively parallel interrogation of protein fragment secretability using SECRiFY reveals features influencing secretory system transit
Morgane Boone, Pathmanaban Ramasamy, Jasper Zuallaert, Robbin Bouwmeester, Berre Van Moer, et al.
Nature Communications, 2021
Computational resources for identifying and describing proteins driving liquid-liquid phase separation
Rita Pancsa, Wim Vranken, Bálint Mészáros
Briefings in Bioinformatics, 2021
B2bTools: Online predictions for protein biophysical features and their conservation
Luciano Porto Kagami, Gabriele Orlando, Daniele Raimondi, Francois Ancien, Bhawna Dixit, et al.
Nucleic Acids Research, 2021
Critical assessment of protein intrinsic disorder prediction
Marco Necci, Damiano Piovesan, CAID Predictors, Md Tamjidul Hoque, Ian Walsh, et al.
Nature Methods, 2021
MobiDB: Intrinsically disordered proteins in 2021
Damiano Piovesan, Marco Necci, Nahuel Escobedo, Alexander Miguel Monzon, András Hatos, et al.
Nucleic Acids Research, 2021
Interpreting a black box predictor to gain insights into early folding mechanisms
Isel Grau, Ann Nowé, Wim Vranken
Computational and Structural Biotechnology Journal, 2021
ShiftCrypt: A web server to understand and biophysically align proteins through their NMR chemical shift values
Gabriele Orlando, Daniele Raimondi, Luciano Porto Kagami, Wim F Vranken
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, et al.
Nature Methods, 2021
Scop3P: A Comprehensive Resource of Human Phosphosites within Their Full Context
Pathmanaban Ramasamy, Demet Turan, Natalia Tichshenko, Niels Hulstaert, Elien Vandermarliere, et al.
Journal of Proteome Research, 2020
Distance-Based Metrics for Comparing Conformational Ensembles of Intrinsically Disordered Proteins
Tamas Lazar, Mainak Guharoy, Wim Vranken, Sarah Rauscher, Shoshana J. Wodak, et al.
Biophysical Journal, 2020
Accurate prediction of protein beta-aggregation with generalized statistical potentials
Gabriele Orlando, Alexandra Silva, Sandra Macedo-Ribeiro, Daniele Raimondi, Wim Vranken
Bioinformatics, 2020
PDBE-KB: A community-driven resource for structural and functional annotations
PDBe-KB consortium, Mihaly Varadi, John Berrisford, Mandar Deshpande, Sreenath S Nair, et al.
Nucleic Acids Research, 2020
DisProt: Intrinsic protein disorder annotation in 2020
András Hatos, Borbála Hajdu-Soltész, Alexander M Monzon, Nicolas Palopoli, Lucía Álvarez, et al.
Nucleic Acids Research, 2020
Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index
Gabriele Orlando, Daniele Raimondi, Wim F. Vranken
Nature Communications, 2019
Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis
Daniele Raimondi, Gabriele Orlando, Wim F. Vranken, Yves Moreau
Scientific Reports, 2019
Author Correction: Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins (Scientific Reports, (2017), 7, 1, (8826), 10.1038/s41598-017-08366-3)
Daniele Raimondi, Gabriele Orlando, Rita Pancsa, Taushif Khan, Wim F. Vranken
Scientific Reports, 2019
Computational identification of prion-like RNA-binding proteins that form liquid phase-separated condensates
Gabriele Orlando, Daniele Raimondi, Francesco Tabaro, Francesco Codicè, Yves Moreau, et al.
Bioinformatics, 2019
Structural basis of the subcellular topology landscape of Escherichia coli
Maria S. Loos, Reshmi Ramakrishnan, Wim Vranken, Alexandra Tsirigotaki, Evrydiki-Pandora Tsare, et al.
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, et al.
F1000research, 2019
Large-scale in-silico statistical mutagenesis analysis sheds light on the deleteriousness landscape of the human proteome
Daniele Raimondi, Gabriele Orlando, Francesco Tabaro, Tom Lenaerts, Marianne Rooman, et al.
Scientific Reports, 2018
Ultra-fast global homology detection with Discrete Cosine Transform and Dynamic Time Warping
Daniele Raimondi, Gabriele Orlando, Yves Moreau, Wim F Vranken
Bioinformatics, 2018
Lexicon visualization library and javascript for scientific data visualization
Ibrahim Tanyalcin, Carla Al Assaf, Julien Ferte, Francois Ancien, Taushif Khan, et al.
Computing in Science and Engineering, 2018
RINspector: A Cytoscape app for centrality analyses and DynaMine flexibility prediction
Guillaume Brysbaert, Kevin Lorgouilloux, Wim F Vranken, Marc F Lensink
Bioinformatics, 2018
AmyPro: A database of proteins with validated amyloidogenic regions
Mihaly Varadi, Greet De Baets, Wim F Vranken, Peter Tompa, Rita Pancsa
Nucleic Acids Research, 2018
MobiDB 3.0: More annotations for intrinsic disorder, conformational diversity and interactions in proteins
Damiano Piovesan, Francesco Tabaro, Lisanna Paladin, Marco Necci, Ivan Mičetić, et al.
Nucleic Acids Research, 2018
SVM-dependent pairwise HMM: An application to protein pairwise alignments
Gabriele Orlando, Daniele Raimondi, Taushif Khan, Tom Lenaerts, Wim F Vranken
Bioinformatics, 2017
Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins
Daniele Raimondi, Gabriele Orlando, Rita Pancsa, Taushif Khan, Wim F. Vranken
Scientific Reports, 2017
DEOGEN2: Prediction and interactive visualization of single amino acid variant deleteriousness in human proteins
Daniele Raimondi, Ibrahim Tanyalcin, Julien Ferté, Andrea Gazzo, Gabriele Orlando, et al.
Nucleic Acids Research, 2017
Seeing the trees through the forest: Sequencebased homo- and heteromeric protein-protein interaction sites prediction using random forest
Qingzhen Hou, Paul F G De Geest, Wim F Vranken, Jaap Heringa, K Anton Feenstra
Bioinformatics, 2017
Corrigendum: DisProt 7.0: a major update of the database of disordered proteins
Damiano Piovesan, Francesco Tabaro, Ivan Mičetić, Marco Necci, Federica Quaglia, et al.
Nucleic Acids Research, 2017
Investigating the Molecular Mechanisms Behind Uncharacterized Cysteine Losses from Prediction of Their Oxidation State
Daniele Raimondi, Gabriele Orlando, Joris Messens, Wim F. Vranken
Human Mutation, 2017
DisProt 7.0: A major update of the database of disordered proteins
Damiano Piovesan, Francesco Tabaro, Ivan Mičetić, Marco Necci, Federica Quaglia, et al.
Nucleic Acids Research, 2017
Observation selection bias in contact prediction and its implications for structural bioinformatics
G. Orlando, D. Raimondi, W. F. Vranken
Scientific Reports, 2016
Multilevel biological characterization of exomic variants at the protein level significantly improves the identification of their deleterious effects
Daniele Raimondi, Andrea M. Gazzo, Marianne Rooman, Tom Lenaerts, Wim F. Vranken
Bioinformatics, 2016
Early Folding Events, Local Interactions, and Conservation of Protein Backbone Rigidity
Rita Pancsa, Daniele Raimondi, Elisa Cilia, Wim F. Vranken
Biophysical Journal, 2016
Start2Fold: A database of hydrogen/deuterium exchange data on protein folding and stability
Rita Pancsa, Mihaly Varadi, Peter Tompa, Wim F. Vranken
Nucleic Acids Research, 2016
Analysis of the structural quality of the CASD-NMR 2013 entries
Timothy J. Ragan, Rasmus H. Fogh, Roberto Tejero, Wim Vranken, Gaetano T. Montelione, et al.
Journal of Biomolecular NMR, 2015
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, et al.
Journal of Biomolecular NMR, 2015
Computational approaches for inferring the functions of intrinsically disordered proteins
Mihaly Varadi, Wim Vranken, Mainak Guharoy, Peter Tompa
Frontiers in Molecular Biosciences, 2015
An evolutionary view on Disulfide bond connectivities prediction using phylogenetic trees and a simple cysteine mutation model
Daniele Raimondi, Gabriele Orlando, Wim F. Vranken
Plos One, 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, et al.
Nature Structural and Molecular Biology, 2015
Clustering-based model of cysteine co-evolution improves disulfide bond connectivity prediction and reduces homologous sequence requirements
Daniele Raimondi, Gabriele Orlando, Wim F. Vranken
Bioinformatics, 2015
NMR-based modeling and refinement of protein 3D structures
Wim F. Vranken, Geerten W. Vuister, Alexandre M. J. J. Bonvin
Methods in Molecular Biology, 2015
NMR-based modeling and refinement of protein 3D structures
Wim F. Vranken, Geerten W. Vuister, Alexandre M. J. J. Bonvin
Molecular Modeling of Proteins Second Edition, 2014
The DynaMine webserver: Predicting protein dynamics from sequence
Elisa Cilia, Rita Pancsa, Peter Tompa, Tom Lenaerts, Wim F. Vranken
Nucleic Acids Research, 2014
Small-angle X-ray scattering- and nuclear magnetic resonance-derived conformational ensemble of the highly flexible antitoxin PaaA2
Yann G.J. Sterckx, Alexander N. Volkov, Wim F. Vranken, Jaka Kragelj, Malene Ringkjøbing Jensen, et al.
Structure, 2014
Study of the structural and dynamic effects in the FimH adhesin upon α-D-heptyl mannose binding
Sophie Vanwetswinkel, Alexander N. Volkov, Yann G. J. Sterckx, Abel Garcia-Pino, Lieven Buts, et al.
Journal of Medicinal Chemistry, 2014
NMR structure validation in relation to dynamics and structure determination
Wim F. Vranken
Progress in Nuclear Magnetic Resonance Spectroscopy, 2014
From protein sequence to dynamics and disorder with DynaMine
Elisa Cilia, Rita Pancsa, Peter Tompa, Tom Lenaerts, Wim F. Vranken
Nature Communications, 2013
Recommendations of the wwPDB NMR validation task force
Gaetano T. Montelione, Michael Nilges, Ad Bax, Peter Güntert, Torsten Herrmann, et al.
Structure, 2013
Improving 3D structure prediction from chemical shift data
Gijs van der Schot, Zaiyong Zhang, Robert Vernon, Yang Shen, Wim F. Vranken, et al.
Journal of Biomolecular NMR, 2013
WeNMR: Structural Biology on the Grid
Tsjerk A. Wassenaar, Marc van Dijk, Nuno Loureiro-Ferreira, Gijs van der Schot, Sjoerd J. de Vries, et al.
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, et al.
Journal of Biomolecular NMR, 2012
ACPYPE - AnteChamber PYthon Parser interfacE
Alan W Sousa da Silva, Wim F Vranken
BMC Research Notes, 2012
Protein structure validation using side-chain chemical shifts
Aleksandr B. Sahakyan, Andrea Cavalli, Wim F. Vranken, Michele Vendruscolo
Journal of Physical Chemistry B, 2012
Determination of secondary structure populations in disordered states of proteins using nuclear magnetic resonance chemical shifts
Carlo Camilloni, Alfonso De Simone, Wim F. Vranken, Michele Vendruscolo
Biochemistry, 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, et al.
Structure, 2012
NRG-CING: Integrated validation reports of remediated experimental biomolecular NMR data and coordinates in wwPDB
J. F. Doreleijers, W. F. Vranken, C. Schulte, J. L. Markley, E. L. Ulrich, et al.
Nucleic Acids Research, 2012
PDBe: Protein Data Bank in Europe
S. Velankar, Y. Alhroub, C. Best, S. Caboche, M. J. Conroy, et al.
Nucleic Acids Research, 2012
WeNMR: Structural biology on the grid
Ceur Workshop Proceedings, 2011
Using side-chain aromatic proton chemical shifts for a quantitative analysis of protein structures
Aleksandr B. Sahakyan, Wim F. Vranken, Andrea Cavalli, Michele Vendruscolo
Angewandte Chemie International Edition, 2011
Structure-based prediction of methyl chemical shifts in proteins
Aleksandr B. Sahakyan, Wim F. Vranken, Andrea Cavalli, Michele Vendruscolo
Journal of Biomolecular NMR, 2011
Bayesian estimation of NMR restraint potential and weight: A validation on a representative set of protein structures
Aymeric Bernard, Wim F. Vranken, Benjamin Bardiaux, Michael Nilges, Thérèse E. Malliavin
Proteins Structure Function and Bioinformatics, 2011
EUROCarbDB: An open-access platform for glycoinformatics
C.-W. von der Lieth, A. A. Freire, D. Blank, M. P. Campbell, A. Ceroni, et al.
Glycobiology, 2011
PDBe: Protein data bank in Europe
S. Velankar, Y. Alhroub, A. Alili, C. Best, H. C. Boutselakis, et al.
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, et al.
Journal of Biomolecular NMR, 2010
Validation of archived chemical shifts through atomic coordinates
Wolfgang Rieping, Wim F. Vranken
Proteins Structure Function and Bioinformatics, 2010
MEMOPS: data modelling and automatic code generation.
Rasmus H. Fogh, Wayne Boucher, John M.C. Ionides, Wim F. Vranken, Tim J. Stevens, et al.
Journal of Integrative Bioinformatics, 2010
The NMR restraints grid at BMRB for 5,266 protein and nucleic acid PDB entries
Jurgen F. Doreleijers, Wim F. Vranken, Christopher Schulte, Jundong Lin, Jonathan R. Wedell, et al.
Journal of Biomolecular NMR, 2009
Accurate random coil chemical shifts from an analysis of loop regions in native states of proteins
Alfonso De Simone, Andrea Cavalli, Shang-Te Danny Hsu, Wim Vranken, Michele Vendruscolo
Journal of the American Chemical Society, 2009
PDBe: Protein Data Bank in Europe
S. Velankar, C. Best, B. Beuth, C. H. Boutselakis, N. Cobley, et al.
Nucleic Acids Research, 2009
Relationship between chemical shift value and accessible surface area for all amino acid atoms
Wim F Vranken, Wolfgang Rieping
BMC Structural Biology, 2009
COCO: A simple tool to enrich the representation of conformational variability in NMR structures
Charles A. Laughton, Modesto Orozco, Wim Vranken
Proteins Structure Function and Bioinformatics, 2009
CASD-NMR: Critical assessment of automated structure determination by NMR
Antonio Rosato, Anurag Bagaria, David Baker, Benjamin Bardiaux, Andrea Cavalli, et al.
Nature Methods, 2009
Remediation of the protein data bank archive
K. Henrick, Z. Feng, W. F. Bluhm, D. Dimitropoulos, J. F. Doreleijers, et al.
Nucleic Acids Research, 2008
A global analysis of NMR distance constraints from the PDB
Wim Vranken
Journal of Biomolecular NMR, 2007
A nomenclature and data model to describe NMR experiments
Rasmus H. Fogh, Wim F. Vranken, Wayne Boucher, Tim J. Stevens, Ernest D. Laue
Journal of Biomolecular NMR, 2006
SPINE bioinformatics and data-management aspects of high-throughput structural biology
S. Albeck, P. Alzari, C. Andreini, L. Banci, I. M. Berry, et al.
Acta Crystallographica Section D Biological Crystallography, 2006
E-MSD: improving data deposition and structure quality.
M. Tagari
Nucleic Acids Research, 2006
The CCPN data model for NMR spectroscopy: Development of a software pipeline
Wim F. Vranken, Wayne Boucher, Tim J. Stevens, Rasmus H. Fogh, Anne Pajon, et al.
Proteins Structure Function and Genetics, 2005
RECOORD: A recalculated coordinate database of 500+ proteins from the PDB using restraints from the BioMagResBank
Aart J. Nederveen, Jurgen F. Doreleijers, Wim Vranken, Zachary Miller, Chris A.E.M. Spronk, et al.
Proteins Structure Function and Genetics, 2005
BioMagResBank databases DOCR and FRED containing converted and filtered sets of experimental NMR restraints and coordinates from over 500 protein PDB structures
Jurgen F. Doreleijers, Aart J. Nederveen, Wim Vranken, Jundong Lin, Alexandre M.J.J. Bonvin, et al.
Journal of Biomolecular NMR, 2005
A framework for scientific data modeling and automated software development
R. H. Fogh, W. Boucher, W. F. Vranken, A. Pajon, T. J. Stevens, et al.
Bioinformatics, 2005
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, et al.
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, et al.
Proteins Structure Function and Genetics, 2004
E-MSD: An integrated data resource for bioinformatics
Nucleic Acids Research, 2004
E-MSD: The European Bioinformatics Institute macromolecular structure database
H. Boutselakis
Nucleic Acids Research, 2003
Solution structure and backbone dynamics of the functional cytoplasmic subdomain of human ephrin B2, a cell-surface ligand with bidirectional signaling properties
Jianxing Song, Wim Vranken, Ping Xu, Richard Gingras, Ryan S. Noyce, et al.
Biochemistry, 2002
PESCADOR: The PEptides in solution conformation database: Online resource
Anne Pajon, Wim F. Vranken, Maria Angeles Jimenez, Manuel Rico, Shoshana J. Wodak
Journal of Biomolecular NMR, 2002
Solution structure of a llama single-domain antibody with hydrophobic residues typical of the VH/VL interface
Wim Vranken, Dmitri Tolkatchev, Ping Xu, Jamshid Tanha, Zhigang Chen, et al.
Biochemistry, 2002
Solution structures of a 30-residue amino-terminal domain of the carp granulin-1 protein and its amino-terminally truncated 3-30 subfragment: Implications for the conformational stability of the stack of two β-hairpins
Wim F. Vranken, Susan James, Hugh P.J. Bennett, Feng Ni
Proteins Structure Function and Genetics, 2002
The ccpn project: An interim report on a data model for the nmr community
Rasmus Fogh, John Ionides, Eldon Ulrich, Wayne Boucher, Wim Vranken, et al.
Nature Structural Biology, 2002
Conformational model for the consensus v3 loop of the envelope protein gp120 of hiv-1 in a 20% trifluoroethanol/water solution
Wim F. Vranken, Franky Fant, Milos Budesinsky, Frans A. M. Borremans
European Journal of Biochemistry, 2001
Design and solution structure of a well-folded stack of two β-hairpins based on the amino-terminal fragment of human granulin A
Dmitri Tolkatchev, Andy Ng, Wim Vranken, Feng Ni
Biochemistry, 2000
The three-dimensional solution structure of Aesculus hippocastanum antimicrobial protein 1 determined by 1H nuclear magnetic resonance
Franky Fant, Wim F. Vranken, Frans A.M. Borremans
Proteins Structure Function and Genetics, 1999
A 30-residue fragment of the carp granulin-1 protein folds into a stack of two β-hairpins similar to that found in the native protein
W.F. Vranken, Z.G. Chen, P. Xu, S. James, H.P.J. Bennett, et al.
Journal of Peptide Research, 1999
Conformation of a Cdc42/Rac interactive binding peptide in complex with Cdc42 and analysis of the binding interface
Willem K. Stevens, Wim Vranken, Nathalie Goudreau, Hui Xiang, Ping Xu, et al.
Biochemistry, 1999
An NMR-based identification of peptide fragments mimicking the interactions of the cathepsin B propeptide
Youlu Yu, Wim Vranken, Nathalie Goudreau, Elisa de Miguel, Marie-Claude Magny, et al.
FEBS Letters, 1998
Determination of the three-dimensional solution structure of Raphanus sativus antifungal protein 1 by 1H NMR
Franky Fant, Wim Vranken, Willem Broekaert, Frans Borremans
Journal of Molecular Biology, 1998
Conformational features of a synthetic cyclic peptide corresponding to the complete V3 loop of the ELI HIV-1 strain in water
Wim F. Vranken, Miloš Buděšínský, Franky Fant, Kris Boulez, Hélène Gras-Masse, et al.
Collection of Czechoslovak Chemical Communications, 1996
Conformational features of a synthetic-cyclic peptide corresponding to the complete V3 loop of the RF HIV-1 strain in water and water/trifluoroethanol solutions
Wim F. Vranken, Milos Budesinsky, José C. Martins, Franky Fant, Kris Boulez, et al.
European Journal of Biochemistry, 1996
The complete Consensus V3 loop peptide of the envelope protein gp120 of HIV-1 shows pronounced helical character in solution
Wim F. Vranken, Milos Budesinsky, Franky Fant, Kris Boulez, Frans A.M. Borremans
FEBS Letters, 1995

Wim Vranken

RESEARCH, TEACHING, or OTHER INTERESTS

Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS