Author

Carla P. Perez

Stanford UniversityORCID 0000-0002-9154-2360Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Approach:microscopy & imaging analysis· 1 Research Focus:protein design· 1 Research Focus:protein folding & dynamics· 1 Assay:protein sequence· 1

Frequent Co-Authors

Po‐Ssu HuangiD· 2 shared Raphael R. EguchiiD· 2 shared Alexander DerryiD· 1 shared Cameron L. NolandiD· 1 shared Christian A. ChoeiD· 1 shared Irimpan I. MathewsiD· 1 shared Namrata AnandiD· 1 shared Nicole V. DelRossoiD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersBiophysics

Po‐Ssu HuangORCID

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2 papers

Recent·Most cited

ADAPT-M: A workflow for rapid, quantitative in vitro measurements of enriched protein libraries

Carla P. Perez, Nicole V. DelRosso, Cameron L. Noland, Udit Parekh, Christian A. Choe, Raphael R. Eguchi, Qi Wen, Polly Morrell Fordyce, Po‐Ssu Huang

ADAPT-M rapidly quantifies protein binder affinities and kinetics using microfluidics, enabling efficient selection of high-affinity variants from yeast-display libraries.

DOAJ (DOAJ: Directory of Open Access Journals) · Jul 1, 2026Peer-reviewedBiophysics

Protein sequence design with a learned potential

Namrata Anand, Raphael R. Eguchi, Irimpan I. Mathews, Carla P. Perez, Alexander Derry, Russ Biagio Altman, Po‐Ssu Huang

This paper designs protein sequences using a deep neural network trained on crystal structures, producing stable designs that match high-resolution structures.

bioRxiv · Jan 6, 2020 · PreprintBioinformatics