Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden 2300 RC, The Netherlands.
J Proteome Res. 2021 Jun 4;20(6):3395-3399. doi: 10.1021/acs.jproteome.1c00136. Epub 2021 Apr 27.
While mass spectrometry still dominates proteomics research, alternative and potentially disruptive, next-generation technologies are receiving increased investment and attention. Most of these technologies aim at the sequencing of single peptide or protein molecules, typically labeling or otherwise distinguishing a subset of the proteinogenic amino acids. This note considers some theoretical aspects of these future technologies from a bottom-up proteomics viewpoint, including the ability to uniquely identify human proteins as a function of which and how many amino acids can be read, enzymatic efficiency, and the maximum read length. This is done through simulations under ideal and non-ideal conditions to set benchmarks for what may be achievable with future single-molecule sequencing technology. The simulations reveal, among other observations, that the best choice of reading amino acids performs similarly to the average choice of +1 amino acids, and that the discrimination power of the amino acids scales with their frequency in the proteome. The simulations are agnostic with respect to the next-generation proteomics platform, and the results and conclusions should therefore be applicable to any single-molecule partial peptide sequencing technology.
虽然质谱仍然主导着蛋白质组学研究，但替代的、具有潜在颠覆性的下一代技术正在获得越来越多的投资和关注。这些技术大多旨在对单个肽或蛋白质分子进行测序，通常通过标记或其他方式区分蛋白质氨基酸的子集。本注释从自下而上的蛋白质组学角度考虑了这些未来技术的一些理论方面，包括根据可以读取的氨基酸的种类和数量以及酶的效率和最大读取长度来唯一识别人类蛋白质的能力。这是通过在理想和非理想条件下进行模拟来实现的，以便为未来的单分子测序技术可能实现的目标设定基准。模拟结果表明，在最佳选择读取氨基酸的情况下，其性能与平均选择+1 个氨基酸的性能相似，并且氨基酸的区分能力与其在蛋白质组中的频率成正比。模拟结果与下一代蛋白质组学平台无关，因此，结果和结论应该适用于任何单分子部分肽测序技术。
