Vinogradov Alexander A, Gates Zachary P, Zhang Chi, Quartararo Anthony J, Halloran Kathryn H, Pentelute Bradley L
Department of Chemistry, Massachusetts Institute of Technology , 18-563, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
ACS Comb Sci. 2017 Nov 13;19(11):694-701. doi: 10.1021/acscombsci.7b00109. Epub 2017 Sep 29.
A methodology to achieve high-throughput de novo sequencing of synthetic peptide mixtures is reported. The approach leverages shotgun nanoliquid chromatography coupled with tandem mass spectrometry-based de novo sequencing of library mixtures (up to 2000 peptides) as well as automated data analysis protocols to filter away incorrect assignments, noise, and synthetic side-products. For increasing the confidence in the sequencing results, mass spectrometry-friendly library designs were developed that enabled unambiguous decoding of up to 600 peptide sequences per hour while maintaining greater than 85% sequence identification rates in most cases. The reliability of the reported decoding strategy was additionally confirmed by matching fragmentation spectra for select authentic peptides identified from library sequencing samples. The methods reported here are directly applicable to screening techniques that yield mixtures of active compounds, including particle sorting of one-bead one-compound libraries and affinity enrichment of synthetic library mixtures performed in solution.
报道了一种实现合成肽混合物高通量从头测序的方法。该方法利用鸟枪法纳升液相色谱与基于串联质谱的文库混合物(多达2000种肽)从头测序相结合,以及自动化数据分析协议来滤除错误的归属、噪声和合成副产物。为了提高测序结果的可信度,开发了对质谱友好的文库设计,能够每小时明确解码多达600个肽序列,并且在大多数情况下保持大于85%的序列识别率。通过匹配从文库测序样品中鉴定出的选定真实肽的碎片谱,进一步证实了所报道解码策略的可靠性。本文报道的方法可直接应用于产生活性化合物混合物的筛选技术,包括单珠单化合物文库的颗粒分选和在溶液中进行的合成文库混合物的亲和富集。