Author

Benjamin Eltzner

Max Planck Institute for Biophysical ChemistryORCID 0000-0001-9826-4634Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Molecular Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Approach:machine learning (classical)· 1 Assay:protein structure· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Henrik WiechersiD· 2 shared Kanti V. MardiaiD· 2 shared Stephan HuckemanniD· 2 shared Bert de GrootiD· 1 shared Christopher J. WilliamsiD· 1 shared Daniel RudolfiD· 1 shared Ezra MilleriD· 1 shared Franziska Hoppe· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersMolecular Biology

Henrik WiechersORCID

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3 papers

Recent·Most cited

Molecular BiologyPLoS Computational BiologyPeer-reviewedMay 8, 2026

RNAprecis: Prediction of full-detail RNA conformation from the experimentally best-observed sparse parameters

Henrik Wiechers, Christopher J. Williams, Benjamin Eltzner…·Stephan Huckemann

The paper introduces RNAprecis, an algorithm that predicts detailed RNA conformations from low-resolution experimental data, showing improved performance on outlier cases.

1 citation

protein structurestatistical model

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BioinformaticsbioRxivPreprintSep 12, 2023

Bayesian Maximum Entropy Ensemble Refinement

Benjamin Eltzner, Julian Hofstadler, Daniel Rudolf…·Bert de Groot

This paper introduces a Bayesian method to refine molecular dynamics simulations using nuclear Overhauser effect measurements by estimating maximum entropy parameters through adaptive Markov Chain Monte Carlo methods.

DOI 10.1101/2023.09.12.557310

Bayesian inference

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BioinformaticsbioRxivPreprintAug 6, 2021

Learning torus PCA based classification for multiscale RNA backbone structure correction with application to SARS-CoV-2

Henrik Wiechers, Benjamin Eltzner, Kanti V. Mardia, Stephan Huckemann

The paper introduces a data-driven method to correct RNA structure clashes using torus PCA and classification, validated on SARS-CoV-2 RNA structures.

2 citations

statistical model

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