Open AccessEngineered peptide barcodes for in-depth analyses of binding protein libraries
Author(s) -
Pascal Egloff,
Iwan Zimmermann,
Fabian Arnold,
Cedric A. J. Hutter,
Damien Morger,
Lennart Opitz,
Lucy Poveda,
Hans-Anton Keserue,
Christian Panse,
Bernd Roschitzki,
Markus A. Seeger
Publication year - 2019
Publication title -
nature methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 19.469
H-Index - 318
eISSN - 1548-7105
pISSN - 1548-7091
DOI - 10.1038/s41592-019-0389-8
Subject(s) - computational biology , selection (genetic algorithm) , identification (biology) , process (computing) , biology , peptide library , mass spectrometry , computer science , peptide , nanotechnology , biological system , chemistry , gene , artificial intelligence , materials science , genetics , chromatography , biochemistry , peptide sequence , botany , operating system
Binding protein generation typically relies on laborious screening cascades that process candidate molecules individually. We have developed NestLink, a binder selection and identification technology able to biophysically characterize thousands of library members at once without the need to handle individual clones at any stage of the process. NestLink uses genetically encoded barcoding peptides termed flycodes, which were designed for maximal detectability by mass spectrometry and support accurate deep sequencing. We demonstrate NestLink's capacity to overcome the current limitations of binder-generation methods in three applications. First, we show that hundreds of binder candidates can be simultaneously ranked according to kinetic parameters. Next, we demonstrate deep mining of a nanobody immune repertoire for membrane protein binders, carried out entirely in solution without target immobilization. Finally, we identify rare binders against an integral membrane protein directly in the cellular environment of a human pathogen. NestLink opens avenues for the selection of tailored binder characteristics directly in tissues or in living organisms.