Author

F. Zamponi

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

Biology Disciplines

Click to filter author papers

Evolutionary Biology· 2 Biochemistry· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:protein folding & dynamics· 3 Approach:statistical model· 3 Assay:protein sequence· 2 Approach:deep learning· 1 Assay:proteomics· 1

Frequent Co-Authors

M. Bisardi· 3 shared M. Weigt· 3 shared S. Cotogno· 2 shared D. Lee· 1 shared J. Rodriguez-Rivas· 1 shared J. Z. Chen· 1 shared L. Di Bari· 1 shared N. Tokuriki· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 20 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersEvolutionary Biology

M. Bisardi

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Emergent time scales of epistasis in protein evolution

L. Di Bari, M. Bisardi, S. Cotogno, M. Weigt, F. Zamponi

This paper introduces a data-driven epistatic model of protein evolution that reproduces sequence statistics across different time scales and uncovers the collective nature of epistasis.

bioRxiv · Mar 14, 2024Evolutionary Biology

Understanding epistatic networks in the B1 -lactamases through coevolutionary statistical modeling and deep mutational scanning

J. Z. Chen, M. Bisardi, D. Lee, S. Cotogno, F. Zamponi, M. Weigt, N. Tokuriki

The paper uses coevolutionary analysis and deep mutational scanning to study epistatic networks in B1-lactamases, revealing both conserved and specific interactions across homologs.

bioRxiv · Nov 27, 2023Biochemistry

Modeling sequence-space exploration and emergence of epistatic signals in protein evolution

M. Bisardi, J. Rodriguez-Rivas, F. Zamponi, M. Weigt

This paper models sequence-space exploration in protein evolution to predict fitness distributions and epistatic signals, enabling the optimization of experimental protocols.

bioRxiv · Jun 4, 2021Evolutionary Biology