Cross-linked peptide identification: A computational forest of algorithms.

Area of Science:

• Biochemistry
• Structural Biology
• Proteomics

Background:

• Chemical cross-linking mass spectrometry (XL-MS) is crucial for studying protein structure and interactions.
• XL-MS data analysis presents unique computational challenges.
• Numerous computational tools have been developed to address these challenges.

Purpose of the Study:

• To provide a comprehensive overview of computational tools for XL-MS experiments.
• To discuss methods for identifying cross-linked peptides.
• To review tools for interpreting cross-links in a structural context.

Main Methods:

• Review of existing computational tools for XL-MS data analysis.
• Categorization of tools based on function (peptide identification, structural interpretation).
• Discussion of challenges in data management and dissemination.

Main Results:

• Identification of various computational tools for peptide identification with intact or MS-cleavable cross-linkers.
• Overview of software for interpreting cross-linked peptides within protein structures.
• Highlighting of data management and dissemination challenges and opportunities.

Conclusions:

• Computational tools are essential for effective XL-MS data analysis.
• The reviewed tools aid in unraveling protein structure, dynamics, and complex formation.
• Future directions include addressing data management and dissemination in XL-MS.