Abstract

Cyclic peptides are of great interest as therapeutic compounds due to their potential for specificity and intracellular activity, but specific compounds can be difficult to identify from large libraries without resorting to molecular encoding techniques. Large libraries of cyclic peptides are often DNA-encoded or linearized before sequencing, but both of those deconvolution strategies constrain the chemistry, assays, and quantification methods which can be used. We developed an automated sequencing program, CycLS, to identify cyclic peptides contained within large synthetic libraries. CycLS facilitates quick and easy identification of all library-members via tandem mass spectrometry data without requiring any specific chemical moieties or modifications within the library. Validation of CycLS against a library of 400 cyclic hexapeptide peptoid hybrids (peptomers) of unique mass yielded a result of 95% accuracy when compared against a simulated library size of 234,256 compounds. CycLS was also evaluated by resynthesizing pure compounds from a separate 1800-member library of cyclic hexapeptides and hexapeptomers with high mass redundancy. Of 22 peptides resynthesized, 17 recapitulated the retention times and fragmentation patterns assigned to them from the whole-library bulk assay results. Implementing a database-matching approach, CycLS is fast and provides a robust method for sequencing cyclic peptides that is particularly applicable to the deconvolution of synthetic libraries.

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