化学
串联质谱法
计算生物学
DNA测序
质谱法
肽
串联质量标签
蛋白质组学
色谱法
生物化学
定量蛋白质组学
基因
生物
作者
Cheuk Chi Albert Ng,Yonglie Zhou,Zhongping Yao
标识
DOI:10.1016/j.aca.2023.341330
摘要
Peptide sequencing is of great significance to fundamental and applied research in the fields such as chemical, biological, medicinal and pharmaceutical sciences. With the rapid development of mass spectrometry and sequencing algorithms, de-novo peptide sequencing using tandem mass spectrometry (MS/MS) has become the main method for determining amino acid sequences of novel and unknown peptides. Advanced algorithms allow the amino acid sequence information to be accurately obtained from MS/MS spectra in short time. In this review, algorithms from exhaustive search to the state-of-art machine learning and neural network for high-throughput and automated de-novo sequencing are introduced and compared. Impacts of datasets on algorithm performance are highlighted. The current limitations and promising direction of de-novo peptide sequencing are also discussed in this review.
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