Author

U. Rothlisberger

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· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Organism:human· 1 Assay:metagenomics· 1 Research Focus:protein design· 1 Research Focus:protein folding & dynamics· 1 Assay:protein structure· 1 Research Focus:protein structure prediction· 1 Approach:statistical model· 1

Frequent Co-Authors

A. Levy· 1 shared J. M. H. Olsen· 1 shared M. Parrinello· 1 shared P. Carloni· 1 shared R. Capelli· 1 shared S. L. Dürr· 1 shared S. Meloni· 1 shared V. Bolnykh· 1 shared

Activity by Year

2020-2022 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 22 mutual co-author links

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1 shared paperBiophysics

A. Levy

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2 papers

Recent·Most cited

Accurate prediction of transition metal ion location via deep learning

S. L. Dürr, A. Levy, U. Rothlisberger

This paper introduces two tools, Metal3D and Metal1D, for predicting metal ion locations in protein structures, with Metal3D showing superior accuracy.

bioRxiv · Aug 22, 2022Biophysics

On the accuracy of molecular simulation-based predictions of koff values: a Metadynamics study

R. Capelli, W. Lyu, V. Bolnykh, S. Meloni, J. M. H. Olsen, U. Rothlisberger, M. Parrinello, P. Carloni

This paper evaluates the accuracy of molecular simulations in predicting koff values for drug unbinding using metadynamics and QM/MM methods, finding discrepancies due to force field limitations.

The Journal of Physical Chemistry Letters · Jul 16, 2020Peer-reviewedBiophysics