Author

Rafael I. Brent

University of WashingtonORCID 0000-0002-3233-7914Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Approach:diffusion model· 3 Assay:protein structure· 3 Research Focus:protein design· 2 Assay:protein sequence· 2 Research Focus:software / tool· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1

Frequent Co-Authors

David A. BakeriD· 3 shared Frank DiMaioiD· 3 shared Rohith KrishnaiD· 3 shared Bo QiangiD· 2 shared Brian CoventryiD· 2 shared Jasper ButcheriD· 2 shared Jonathan FunkiD· 2 shared Kieran DidiiD· 2 shared

Activity by Year

2025-2025 · peak 3/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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Author profile →

3 papers

Recent·Most cited

De novo design of phosphotyrosine peptide binders

Magnus S. Bauer, Jason Z. Zhang, Kejia Wu, Gyu Rie Lee, Brian Coventry, Isabella M Silvestri, Kody A. Klupt, Jiuhan Shi, Rafael I. Brent, Xinting Li, Carolina Möller, Nicole Roullier, Dionne K. Vafeados, Indrek Kalvet, Rebecca Skotheim, , David A. Baker

This paper introduces RFD2-MI, a deep learning framework for designing phosphotyrosine peptide binders, showing high specificity and affinity for phosphorylated targets.

bioRxiv · Sep 29, 2025 · PreprintBiochemistry

De novo Design of All-atom Biomolecular Interactions with RFdiffusion3

Jasper Butcher, Rohith Krishna, Raktim Mitra, Rafael I. Brent, Yanjing Li, Nathaniel Corley, Paul T. Kim, Jonathan Funk, Simon V. Mathis, Saman Salike, Aiko Muraishi, Helen E. Eisenach, Tuscan Rock Thompson, Jie Chen, Yuliya Politanska, , David A. Baker

An all-atom diffusion model using a 14-atom residue representation and sparse U-Net architecture accelerates biomolecular design 10-fold, enabling experimentally validated DNA-binding proteins and multi-turnover cysteine hydrolases.

bioRxiv · Sep 18, 2025 · PreprintBiochemistry✦ In-depth digest

Accelerating Biomolecular Modeling with AtomWorks andRF3

Nathaniel Corley, Simon V. Mathis, Rohith Krishna, Magnus S. Bauer, Tuscan R. Thompson, Woody Ahern, Maxwell W. Kazman, Rafael I. Brent, Kieran Didi, Andrew Kubaney, Ryan McHugh, Arnav Nagle, Andrew Favor, Meghana Kshirsagar, Pascal Sturmfels, , Frank DiMaio

AtomWorks, a modular atom-level data framework, trains structure prediction and design models; the resulting RF3 improves chirality handling and performs between AF3 and Boltz.

bioRxiv · Aug 14, 2025 · PreprintBiochemistry✦ In-depth digest