Ultra-large chemical libraries for the discovery of high-affinity peptide binders

Anthony J. Quartararo, Zachary P. Gates, Bente A. Somsen, Nina Hartrampf, Xiyun Ye, Arisa Shimada, Yasuhiro Kajihara, Christian Ottmann, Bradley L. Pentelute (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

77 Citations (Scopus)

Abstract

High-diversity genetically-encoded combinatorial libraries (108−1013 members) are a rich source of peptide-based binding molecules, identified by affinity selection. Synthetic libraries can access broader chemical space, but typically examine only ~ 106 compounds by screening. Here we show that in-solution affinity selection can be interfaced with nano-liquid chromatography-tandem mass spectrometry peptide sequencing to identify binders from fully randomized synthetic libraries of 108 members—a 100-fold gain in diversity over standard practice. To validate this approach, we show that binders to a monoclonal antibody are identified in proportion to library diversity, as diversity is increased from 106–108. These results are then applied to the discovery of p53-like binders to MDM2, and to a family of 3–19 nM-affinity, α/β-peptide-based binders to 14-3-3. An X-ray structure of one of these binders in complex with 14-3-3σ is determined, illustrating the role of β-amino acids in facilitating a key binding contact.

Original languageEnglish
Article number3183
Number of pages11
JournalNature Communications
Volume11
Issue number1
DOIs
Publication statusPublished - 23 Jun 2020

Funding

This work was supported by the NIH/NIGMS Interdepartmental Biotechnology Training Program (T32 GM008334 to A.J.Q.), the Defense Advanced Research Projects Agency (DARPA; Award 023504-001 to B.L.P.), and Calico (to B.L.P.). We gratefully acknowledge Anne Fischer and Tyler Stukenbroeker (DARPA) for their support and guidance; Earl Moore, Mark Paul, Louis Abruzzese, and David Sarracino for their technical assistance with nanoLC and Orbitrap mass spectrometry; and Eric Spooner, Marko Jovanovic, and Dan Maloney for their discussions regarding MS-based analysis and sequencing. We thank Joseph Brown for assistance with library synthesis, Suan Tuang for assistance with automated peptide synthesis, and Faycal Touti, Ethan Evans, and Alex Vinogradov for many fruitful scientific discussions.

FundersFunder number
NIH/NIGMST32 GM008334
National Institute of General Medical SciencesT32GM008334
Defense Advanced Research Projects Agency023504-001

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