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De novo Peptide Sequencing

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De novo peptide sequencing refers to the process of determining a peptide’s amino acid sequence from its MS/MS spectrum alone. The principle of this process is fairly straightforward: a high-quality spectrum may present a ladder of fragment ion peaks. The mass difference between every two adjacent peaks in the ladder is used to determine a residue of the peptide. However, most practical spectra do not have sufficient quality to support this straightforward process. Therefore, research in de novo sequencing has largely been a battle against the errors in the data. This chapter reviews some of the major developments in this field. The chapter starts with a quick review of the history in Section 1. Then manual de novo sequencing is examined in Section 2. Section 3 introduces a few commonly used de novo sequencing algorithms. An important aspect of automated de novo sequencing software is a good scoring function that serves as the optimization goal of the algorithm. Thus, Section 4 is devoted for the methods to define good scoring functions. Section 5 reviews a list of relevant software. The chapter concludes with a discussion of the applications and limitations of de novosequencing in Section 6.
The Royal Society of Chemistry
Title: De novo Peptide Sequencing
Description:
De novo peptide sequencing refers to the process of determining a peptide’s amino acid sequence from its MS/MS spectrum alone.
The principle of this process is fairly straightforward: a high-quality spectrum may present a ladder of fragment ion peaks.
The mass difference between every two adjacent peaks in the ladder is used to determine a residue of the peptide.
However, most practical spectra do not have sufficient quality to support this straightforward process.
Therefore, research in de novo sequencing has largely been a battle against the errors in the data.
This chapter reviews some of the major developments in this field.
The chapter starts with a quick review of the history in Section 1.
Then manual de novo sequencing is examined in Section 2.
Section 3 introduces a few commonly used de novo sequencing algorithms.
An important aspect of automated de novo sequencing software is a good scoring function that serves as the optimization goal of the algorithm.
Thus, Section 4 is devoted for the methods to define good scoring functions.
Section 5 reviews a list of relevant software.
The chapter concludes with a discussion of the applications and limitations of de novosequencing in Section 6.

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