Author

Yensi Flores Bueso

University of WashingtonORCID 0000-0002-2118-2195Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:protein structure· 3 Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Approach:experimental assay· 1 Research Focus:protein design· 1

Frequent Co-Authors

David A. BakeriD· 3 shared Gaurav BhardwajiD· 3 shared Stephen RettieiD· 3 shared Alex KangiD· 2 shared Andi LiuiD· 2 shared Asim K. BeraiD· 2 shared Gizem Gokce AlpkiliciD· 2 shared Joseph D. MougousiD· 2 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersBiochemistry

David A. BakerORCID

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3 papers

Recent·Most cited

A scalable recombinant pipeline for disulphide-stapled peptides

Yensi Flores Bueso, Gizem Gokce Alpkilic, Ji-Ung Jeung, Stephen Rettie, Xinting Li, Mark Tangney, David A. Baker, Gaurav Bhardwaj

This paper describes a scalable recombinant pipeline for producing disulphide-stapled cyclic peptides, achieving high yields and enabling high-throughput screening.

bioRxiv · Sep 30, 2026 · PreprintBiochemistry

De novo design of high-affinity miniprotein binders targeting Francisella tularensis virulence factor

Gizem Gokce Alpkilic, Buwei Huang, Andi Liu, Lieselotte S.M. Kreuk, Yaxi Wang, Victor Adebomi, Yensi Flores Bueso, Asim K. Bera, Alex Kang, Stacey Gerben, Stephen Rettie, Dionne K. Vafeados, Nicole Roullier, Inna Goreshnik, Xinting Li, , Gaurav Bhardwaj

The paper designs high-affinity miniprotein binders targeting Flpp3, a virulence factor of Francisella tularensis, using physics-based and deep-learning methods, achieving sub-nanomolar affinities.

Angewandte Chemie International Edition · Oct 21, 2025Peer-reviewedBiochemistry

Accurate de novo design of high-affinity protein binding macrocycles using deep learning

Stephen Rettie, David Juergens, Victor Adebomi, Yensi Flores Bueso, Qinqin Zhao, Alexandria N. Leveille, Andi Liu, Asim K. Bera, Joana Alessandra Wilms, Alina Üffing, Alex Kang, Evans Brackenbrough, Mila Lamb, Stacey Gerben, A G Murray, , Gaurav Bhardwaj

This paper introduces RFpeptides, a deep learning framework for designing high-affinity macrocyclic peptides, achieving sub-nanomolar binding affinities and accurate structural predictions.

Nature Chemical Biology · Jun 20, 2025Peer-reviewedBiochemistry