Author

Claire J. LeBlanc

University of California, Berkeley

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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Systems Biology· 2 Synthetic Biology· 1

Signature Assays, Organisms & Methods

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Assay:protein sequence· 3 Approach:deep learning· 1 Organism:fungi (other)· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Research Focus:protein design· 1 Research Focus:protein structure prediction· 1 Approach:statistical model· 1

Frequent Co-Authors

Marissa A. ZinteliD· 3 shared Angelica Lam· 2 shared G.F. HuiD· 2 shared Max Valentín StalleriD· 2 shared Melvin Soriano· 2 shared Pooja Agarwal· 2 shared Sanjana R. Kotha· 2 shared Aaron FultineeriD· 1 shared

Activity by Year

2026-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 38 mutual co-author links

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3 shared papersSystems Biology

Marissa A. ZintelORCID

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Author profile →

3 papers

Recent·Most cited

Machine learning guided design of strong transcriptional activation domains

Marissa A. Zintel, Claire J. LeBlanc, Sanjana R. Kotha, Melvin Soriano, Rishaan J. Bansal, Daniel Aguilera, Touss Majidi, Izaiah Ornelas, Liam D. Kirkpatrick, Aditya Udupa, Gean Hu, Pooja Agarwal, Angelica W. Y. Lam, James K. Nunez, Patrick M. Shih, , Max V. Staller

The paper designs functional transcriptional activation domains using machine learning and experimental validation, finding that over 90% of predicted sequences work in yeast.

bioRxiv · Nov 2, 2026 · PreprintSynthetic Biology

Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding

Claire J. LeBlanc, Pooja Agarwal, Jack Demaray, G.F. Hu, Marissa A. Zintel, Angelica Lam, Joel Enrique Castro Hernandez, Max Valentín Staller

This paper develops interpretable biophysical neural networks to predict transcriptional activation domains from protein sequences, revealing how residue properties influence both activation strength and protein abundance.

Cell Systems · Aug 17, 2026Peer-reviewedSystems Biology

Evolutionary turnover of key amino acids explains conservation of function without conservation of sequence in transcriptional activation domains

Claire J. LeBlanc, Jordan Stefani, Melvin Soriano, Angelica Lam, Marissa A. Zintel, Sanjana R. Kotha, Emily Chase, Giovani Pimentel-Solorio, Aditya Vunnum, G.F. Hu, Katherine Flug, Aaron Fultineer, Niklas F. C. Hummel, Max Valentín Staller

The paper shows that function can be conserved in transcriptional activation domains without sequence conservation through evolutionary turnover of key residues.

PLoS Genetics · Mar 16, 2026Peer-reviewedSystems Biology