Wim Vranken

The paper investigates how Achromatium sp. expresses protein variants in response to temperature changes using proteomics and a newly assembled protein atlas.

The paper evaluates machine learning predictors of enzyme turnover numbers (kcat) by testing their impact on downstream metabolic models, finding that accuracy varies significantly and that validation must consider system-level effects.

Scop3P-Toolkit provides an open-source framework for integrating PTMs, mutations, and proteomics data with structural and functional annotations to enable reproducible analysis.

Scop3P is updated to integrate phosphorylation sites with structural, functional, and mutational data, enhancing phosphosite interpretation and disease context.

The paper introduces NEF, a standard for NMR data exchange, and demonstrates its application in structure determination, metabolomics, and ligand screening.

D2D improves protein mutation effect predictions using evolutionary constraints and a language model, outperforming existing methods in stability, binding, and epistasis.

The paper evaluates AlphaFold models' ability to predict phosphorylation-induced structural changes in proteins using experimentally derived conformational ensembles.

The paper reports cryo-EM structures of the MnmE-MnmG complex in different oligomeric states, revealing conformational changes and a mechanism for substrate and cofactor transfer during tRNA modification.

The paper uses structural biology and computational methods to design MSI-1 mutants that alter RNA binding selectivity, validated by NMR and SPR experiments.

The paper develops Xenusia, a neural network tool to identify DNA-binding proteins in archaea using metagenomics data and validates its predictions experimentally.

Resource of 212 literature-curated chimeric class A GPCRs linked to 1,755 natural class A mammalian GPCRs, annotated at sequence, structure and biophysics level, with guidelines for designing new chimeras.

The paper evaluates missense variant effect predictors using CAGI6 data, finding strong performance across settings and highlighting biases in some predictors.

This study compares AlphaFold2's pLDDT metric with NMR and computational dynamics metrics, finding that AlphaFold2 poorly captures the gradations of protein dynamics observed by NMR.

D2Deep predicts cancer driver mutations by combining protein language models and evolutionary information, outperforming existing methods and enabling result interpretation.

The paper defines six low energy conformational states for amino acid residues using solution NMR data and introduces a variability parameter to assess conformational state transitions.

This study uses molecular dynamics simulations to analyze how phosphorylations affect protein backbone dynamics and conformational preferences, validated against NMR data.

The paper identifies and characterizes Sut1, a tyrosine kinase in Sulfolobus acidocaldarius, showing it phosphorylates tyrosine residues in vitro and belongs to a novel archaeal kinase family.

This paper engineers a bacterial gene regulation system using the mammalian RNA-binding protein Musashi-1 to repress translation through an allosteric mechanism.

The paper analyzes phospho-proteomics data to characterize phosphorylation sites, showing phosphorylated residues are more flexible and disease-linked mutations in rigid regions are more prevalent.

The paper introduces DisoMine, a method predicting intrinsically disordered regions using protein dynamics and sequence context, outperforming existing predictors.

The paper investigates factors stabilizing pre-folded helical structures in intrinsically disordered proteins and finds that leucine residues contribute significantly to this stabilization.

The paper develops SECRiFY, a method to assess secretability of protein fragments in yeast, revealing intrinsic disorder and flexibility as key factors.

MutaFrame provides a visualization framework to interpret deleteriousness predictions of missense variants by integrating sequence, structure, and functional data.

The paper evaluates 43 methods for predicting intrinsically disordered regions and binding regions in proteins, finding that deep learning methods outperform older physicochemical approaches but binding prediction remains challenging.

Scop3P provides a comprehensive database of human phosphosites with structural and biophysical context, integrating sequence, structure, and phosphoproteomics data.

The paper introduces superimposition-free metrics to compare conformational ensembles of intrinsically disordered proteins by analyzing residue-residue distance distributions.