Author

Dane Evan Zambrano

University of WashingtonORCID 0000-0002-7252-2508Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Assay:cryo-EM· 1 Approach:deep learning· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:protein design· 1 Research Focus:protein folding & dynamics· 1

Frequent Co-Authors

Neil P. KingiD· 2 shared Alex GoodsoniD· 1 shared Alex KangiD· 1 shared Alexis CourbetiD· 1 shared Andrew J. BorstiD· 1 shared Asim K. BeraiD· 1 shared Banumathi SankaraniD· 1 shared Connor WeidleiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersBiochemistry

Neil P. KingORCID

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2 papers

Recent·Most cited

Computational design of membrane fusion proteins

Masaharu Somiya, Sydney Funk, Dane Evan Zambrano, Takumi Yanase, Noriyuki Hamaoka, Alex Kang, Banumathi Sankaran, Asim K. Bera, Neil P. King

This paper designs synthetic fusogens using machine learning, showing they enable efficient cell-cell and intracellular membrane fusion with higher activity than native SNARE complexes.

bioRxiv · May 7, 2026 · PreprintBiochemistry

Protein identification using cryo-EM and artificial intelligence guides improved sample purification

Kenneth D. Carr, Dane Evan Zambrano, Connor Weidle, Alex Goodson, Helen E. Eisenach, Harley Pyles, Alexis Courbet, Neil P. King, Andrew J. Borst

This paper identifies an unknown contaminant in protein samples using Cryo-EM and AI tools, leading to improved purification protocols.

Journal of Structural Biology X · Jan 21, 2025Peer-reviewedBiochemistry