Department of Chemistry and Biochemistry, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, United States.
Modality Research Laboratories, Daiichi Sankyo Co., Ltd., 1-2-58 Hiromachi, Shinagawa-ku, Tokyo 140-8710, Japan.
Bioorg Med Chem. 2018 Mar 15;26(6):1232-1238. doi: 10.1016/j.bmc.2018.01.027. Epub 2018 Feb 3.
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.
环肽作为治疗化合物具有很大的研究价值，因为它们具有特异性和细胞内活性的潜力，但如果不采用分子编码技术，从大型文库中识别特定的化合物可能具有挑战性。大型环肽文库通常在测序前进行 DNA 编码或线性化，但这两种解卷积策略限制了可以使用的化学、测定和定量方法。我们开发了一种自动化测序程序 CycLS，用于鉴定大型合成文库中包含的环肽。CycLS 通过串联质谱数据快速轻松地识别所有文库成员，而无需在文库中使用任何特定的化学部分或修饰。当将 CycLS 与一个包含 400 个独特质量的环状六肽类肽杂种（肽同型物）的文库进行验证时，与模拟的 234,256 种化合物的文库大小相比，其准确率为 95%。CycLS 还通过重新合成具有高质量冗余的环状六肽和六肽同型物的另一个独立的 1800 成员文库中的纯化合物进行了评估。在所合成的 22 个肽中，有 17 个重现了从整个文库批量测定结果分配给它们的保留时间和碎裂模式。通过实施数据库匹配方法，CycLS 快速且提供了一种适用于合成文库解卷积的强大的环肽测序方法。
