Author

Karson Chrispens

University of California San FranciscoORCID 0000-0002-2115-3132Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Biophysics· 1 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Assay:proteomics· 2 Organism:human· 1 Approach:statistical model· 1

Frequent Co-Authors

Gabriella O. EstevamiD· 2 shared James S. FraseriD· 2 shared Jingyou RaoiD· 2 shared Willow Coyote-MaestasiD· 2 shared Ashraya RavikumariD· 1 shared Christian B. MacdonaldiD· 1 shared Edmond M. LinossiiD· 1 shared Eric Andrew CollissoniD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 40 mutual co-author links

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2 shared papersBiophysics

Gabriella O. EstevamORCID

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2 papers

Recent·Most cited

Learning inhibitor interactions from protein deep mutational scanning data and deep learning models

Ziyue Zou, Isaiah Hazelwood, Gabriella Estevam, Jingyou Rao, Karson Chrispens, Matthew K. Howard, Willow Coyote-Maestas, James S Fraser

The paper benchmarks AI models for predicting mutational effects and drug-target affinity using deep mutational scanning data, finding that current models fail to capture inhibitor-dependent fitness landscapes.

bioRxiv · Nov 3, 2026 · PreprintBiophysics

Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning

Gabriella O. Estevam, Edmond M. Linossi, Jingyou Rao, Christian B. Macdonald, Ashraya Ravikumar, Karson M Chrispens, John A. Capra, Willow Coyote‐Maestas, Harold Pimentel, Eric Andrew Collisson, Natalia Jura, James S. Fraser

The paper uses deep mutational scanning to analyze MET kinase domain mutations and their resistance to 11 inhibitors, identifying resistance and sensitizing mutations and improving prediction with a protein language model.

eLife · Feb 17, 2025Peer-reviewedCancer Biology