Welcome to the Recombinant Antibody Network

The Recombinant Antibody Network is a consortium of highly integrated technology centers at UCSF, the University of Chicago, and the University of Toronto, unified under a common goal to generate therapeutic grade recombinant antibodies at a proteome wide scale for biology and biomedicine.

Given that over half the human proteome is not annotated and that functional antibodies are not reliably available, a complete set of validated antibodies would greatly advance all areas of biology, including cancer therapy and infectious disease control. To undertake these challenges, RAN is systematically and comprehensively profiling families of protein targets using novel, modern high-throughput in vitro technology.

Latest Publications

A universal chimeric antigen receptor (CAR)-fragment antibody binder (FAB) split system for cancer immunotherapy

Arina A; Arauz E; Masoumi E; Warzecha K W; Sääf A; Widło Ł; Slezak T; Zieminska A; Dudek K; Schaefer Z P; Lecka M; Usatyuk S; Weichselbaum R R; Kossiakoff A A

A universal chimeric antigen receptor (CAR)-fragment antibody binder (FAB) split system for cancer immunotherapy Journal Article

In: Sci Adv, vol. 11, no. 27, pp. eadv4937, 2025, ISSN: 2375-2548.

Abstract | Links

Biophysical basis of tight junction barrier modulation by a pan-claudin-binding molecule

Ogbu C P; de Las Alas M; Mandriota A M; Liu X; Kapoor S; Choudhury J; Ruma Y N; Goodman M C; Sanders C R; Gonen T; Kossiakoff A A; Duffey M E; Vecchio A J

Biophysical basis of tight junction barrier modulation by a pan-claudin-binding molecule Journal Article

In: PNAS Nexus, vol. 4, no. 6, pp. pgaf189, 2025, ISSN: 2752-6542.

Abstract | Links

Conformation-specific synthetic intrabodies modulate mTOR signaling with subcellular spatial resolution

O'Leary K M; Slezak T; Kossiakoff A A

Conformation-specific synthetic intrabodies modulate mTOR signaling with subcellular spatial resolution Journal Article

In: Proc Natl Acad Sci U S A, vol. 122, no. 24, pp. e2424679122, 2025, ISSN: 1091-6490.

Abstract | Links

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