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

High-diversity genetically-encoded combinatorial libraries (10 8 −10 13 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 ~ 10 6 compounds by screening. Here we show that...

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Vydáno v:Nature communications Ročník 11; číslo 1; s. 3183 - 11
Hlavní autoři: Quartararo, Anthony J., Gates, Zachary P., Somsen, Bente A., Hartrampf, Nina, Ye, Xiyun, Shimada, Arisa, Kajihara, Yasuhiro, Ottmann, Christian, Pentelute, Bradley L.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 23.06.2020
Nature Publishing Group
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ISSN:2041-1723, 2041-1723
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Shrnutí:High-diversity genetically-encoded combinatorial libraries (10 8 −10 13 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 ~ 10 6 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 10 8 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 10 6 –10 8 . 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. Synthetic peptide libraries can access broad chemical space, but generally examine only ~ 10 6  compounds. Here, the authors show that in-solution affinity selection, interfaced with nLC-MS/MS sequencing, can identify binders from fully randomized synthetic libraries of 10 8 members.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-020-16920-3