Author

Enrico F. Semeraro

University of GrazORCID 0000-0002-6096-1108Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:Bayesian inference· 1 Research Focus:protein folding & dynamics· 1 Approach:simulation· 1 Research Focus:software / tool· 1 Approach:statistical model· 1 Assay:structural biology· 1

Frequent Co-Authors

Georg PabstiD· 2 shared Ladislav BartošiD· 1 shared Paulina PilleriD· 1 shared Rahul DebiD· 1 shared Robert VáchaiD· 1 shared Sandro KelleriD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

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6 top collaborators · 15 mutual co-author links

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2 shared papersBiophysics

Georg PabstORCID

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2 papers

Recent·Most cited

Membrane Thickness Strain from Protein Inclusions: A Multiscale Simulation and X-Ray Scattering Study of Proteoliposomes

Enrico F. Semeraro, Ladislav Bartoš, Paulina Piller, Rahul Deb, Sandro Keller, Robert Vácha, Georg Pabst

This study uses multiscale simulations and X-ray scattering to quantify membrane strain caused by protein inclusions in proteoliposomes, showing agreement between simulations and experiments.

bioRxiv · Jul 8, 2026 · PreprintBiophysics

SAS_MoCa: a software for small-angle scattering data analysis of large unilamellar vesicles

Enrico F. Semeraro, Georg Pabst

SAS_MoCa is an open-source Python package for analyzing small-angle scattering data from large unilamellar vesicles, providing posterior parameter distributions and uncertainties using a Bayesian framework.

bioRxiv · Jul 2, 2026 · PreprintBiophysics