Author

R. Capelli

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· 3 Biochemistry· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 5 Organism:human· 4 Approach:statistical model· 4 Research Focus:protein folding & dynamics· 3 Research Focus:drug response / drug discovery· 2 Assay:metagenomics· 1 Assay:multiome· 1 Assay:protein structure· 1

Frequent Co-Authors

P. Carloni· 3 shared M. Alfonso-Prieto· 2 shared M. Parrinello· 2 shared A. Bochicchio· 1 shared B. Luescher· 1 shared F. Ballabio· 1 shared G. Piccini· 1 shared G. Rossetti· 1 shared

Activity by Year

2019-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 15 mutual co-author links

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3 shared papersBiophysics

P. Carloni

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Author profile →

5 papers

Recent·Most cited

Calcium-driven In Silico Inactivation of a Human Olfactory Receptor

L. Pirona, F. Ballabio, M. Alfonso-Prieto, R. Capelli

This study uses in silico methods to investigate the role of calcium in the inactivation of the human olfactory receptor OR51E2, identifying a novel mechanism involving divalent ion coordination.

bioRxiv · Jan 31, 2024Biophysics

Machine Learning-based Modeling of Olfactory Receptors in their Inactive State: Human OR51E2 as a Case Study

M. Alfonso-Prieto, R. Capelli

The paper uses machine learning to predict olfactory receptor structures and validates them through molecular dynamics simulations, highlighting the role of specific residues in stabilizing the inactive state.

bioRxiv · Feb 22, 2023Biochemistry

An Enhanced Sampling Approach to the Induced Fit Docking Problem in Protein-Ligand Binding: the case of mono-ADP-ribosylation hydrolases inhibitors

Q. Zhao, R. Capelli, P. Carloni, B. Luescher, J. Li, G. Rossetti

The paper introduces an enhanced sampling method for protein-ligand binding with induced fit, validated on ADPr and MacroD1/MacroD2 complexes, showing accurate free energy predictions and structural changes.

bioRxiv · May 8, 2021Biochemistry

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

Chasing the full free energy landscape of neuroreceptor/ligand unbinding by metadynamics simulations

R. Capelli, A. Bochicchio, G. Piccini, R. Casasnovas, P. Carloni, M. Parrinello

The paper uses metadynamics simulations to reconstruct the full free energy landscape of neuroreceptor/ligand unbinding and identifies two escape routes for the ligand.

bioRxiv · Feb 8, 2019Biophysics