Author

N. van Hilten

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 4 Cell Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 4 Approach:statistical model· 3 Research Focus:benchmarking· 2 Research Focus:protein folding & dynamics· 2 Assay:proteomics· 2 Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1 Research Focus:development & morphogenesis· 1

Frequent Co-Authors

H. J. Risselada· 5 shared J. Methorst· 3 shared N. Verwei· 3 shared A. Bernatavicius· 1 shared A. Kros· 1 shared B. Coenen· 1 shared C. Hoffmann· 1 shared C. Koenig· 1 shared

Activity by Year

2021-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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5 shared papersBiophysics

H. J. Risselada

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Author profile →

5 papers

Recent·Most cited

Cofilin-Driven Nuclear Deformation Drives Dendritic Cell Migration through the Extracellular Matrix

H. Warner, G. Franciosa, G. van der Borg, F. Faas, C. Koenig, R. de Boer, R. Classens, S. Maassen, M. Baranov, S. Mahajan, D. D. Dabral, B. Coenen, F. Bianchi, N. van Hilten, H. J. Risselada, , G. V. van den Bogaart

This study shows that cofilin-1 phosphorylation drives nuclear deformation and enhances dendritic cell migration through extracellular matrix.

bioRxiv · Jul 10, 2023Cell Biology

PMIpred: A physics-informed web server for quantitative Protein-Membrane Interaction prediction

N. van Hilten, N. Verwei, J. Methorst, C. Nase, A. Bernatavicius, H. J. Risselada

PMIpred predicts membrane-binding free energy and classifies peptides as non-binding, curvature sensing, or membrane binding using a transformer model trained on molecular dynamics data.

bioRxiv · Apr 10, 2023Biophysics

Physics-based generative model of curvature sensing peptides; distinguishing sensors from binders

N. van Hilten, J. Methorst, N. Verwei, H. J. Risselada

The paper uses Evo-MD and a physics-trained neural network to identify curvature sensing peptides and distinguish them from binders using thermodynamic analysis.

bioRxiv · Sep 1, 2022Biophysics

Efficient quantification of lipid packing defect sensing by amphipathic peptides; comparing Martini 2 & 3 with CHARMM36

N. van Hilten, K. S. Stroh, H. J. Risselada

The paper introduces an efficient method to quantify lipid packing defect sensing by amphipathic peptides, comparing Martini 2 and 3 with CHARMM36 force fields.

bioRxiv · Mar 4, 2022Biophysics

Physics-based inverse design of cholesterolattracting transmembrane helices reveals aparadoxical role of hydrophobic length

J. Methorst, N. Verwei, C. Hoffmann, P. Chodnicki, R. Sansevrino, H. Wang, N. van Hilten, D. Aschmann, A. Kros, L. Andreas, J. Czub, D. Milovanovic, H. J. Risselada

This paper uses evolutionary algorithms and molecular dynamics to identify optimal cholesterol-attracting transmembrane helices, revealing a paradoxical role of hydrophobic length.

bioRxiv · Jul 1, 2021Biophysics