Highly Robust de Novo Full-Length Protein Sequencing.

Area of Science:

• Proteomics
• Bioinformatics
• Biotechnology

Background:

• Mass spectrometry (MS)-based protein sequencing is hindered by errors in de novo peptide identification and detection biases, leading to incomplete sequences.
• Current methods for assembling full-length protein sequences often lack robustness for practical applications, despite advances in multi-enzyme hydrolysis and algorithms.

Purpose of the Study:

• To develop a robust and accurate strategy for full-length protein sequencing using a novel contig-scaffolding approach.
• To address limitations in current MS-based protein sequencing, including peptide identification errors and coverage gaps.

Main Methods:

• A contig-scaffolding strategy inspired by genome assembly was employed, integrating multiple unspecific hydrolysis methods to minimize bias.
• The Multiple Contigs & Scaffolding (MuCS) algorithm was developed to assemble de novo identified peptides in a multistep, contig-scaffold manner with integrated error correction.
• Complementary MS data from different hydrolysis experiments were utilized for robust contig extension and error correction.

Main Results:

• The strategy achieved high accuracy (98.69-100%) and coverage (98.85-100%) for three tested proteins across three replications.
• The method demonstrated effectiveness for membrane proteins, achieving 88.85-92.57% coverage and 97.57-100% accuracy, despite limitations in sequencing transmembrane regions.
• The MuCS software provides a practical solution for accurate and robust full-length protein sequencing.

Conclusions:

• The developed contig-scaffolding strategy and MuCS algorithm offer a significant advancement in robust and accurate full-length protein sequencing.
• This approach effectively addresses major challenges in MS-based proteomics, improving the reliability of protein sequence assembly.
• The MuCS software is publicly available, facilitating broader application in protein research.