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.

中文翻译：

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文库设计促进合成肽文库的高通量测序

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米液体色谱结合基于串联质谱的文库混合物（最多 2000 个肽）从头测序以及自动数据分析协议来滤除不正确的分配、噪音和合成副产物。为了提高测序结果的可信度，开发了适合质谱分析的文库设计，能够每小时明确解码多达 600 个肽序列，同时在大多数情况下保持高于 85% 的序列识别率。所报道的解码策略的可靠性还通过匹配从文库测序样本中鉴定出的选定真实肽的碎片谱来证实。这里报道的方法直接适用于产生活性化合物混合物的筛选技术，包括一珠一化合物库的颗粒分选和在溶液中进行的合成库混合物的亲和富集。