Structure-based validation can drastically underestimate error rate in proteome-wide cross-linking mass spectrometry...

Area of Science:

• Biochemistry
• Proteomics
• Structural Biology

Background:

• Accurate quality assessment of novel protein interactions identified by proteome-wide cross-linking mass spectrometry (XL-MS) is critical for biological discovery.
• Current XL-MS studies predominantly validate cross-links against known three-dimensional protein complex structures.
• This validation approach may lead to an underestimation of error rates in large-scale XL-MS datasets.

Purpose of the Study:

• To demonstrate that validating cross-links against known structures underestimates error rates in proteome-wide XL-MS data.
• To propose a comprehensive set of four novel data-quality metrics for more rigorous XL-MS data assessment.
• To improve the reliability and accuracy of findings from XL-MS studies.

Main Methods:

• Theoretical analysis of existing XL-MS validation strategies.
• Experimental validation of proposed data-quality metrics.
• Development and application of four new metrics for assessing XL-MS dataset quality.

Main Results:

• Provided theoretical and experimental evidence that validating against known structures significantly underestimates error rates.
• Introduced a new set of four comprehensive data-quality metrics for XL-MS.
• Demonstrated the inadequacy of current validation methods for proteome-wide XL-MS datasets.

Conclusions:

• The standard method of validating cross-links against known structures is insufficient for accurate error assessment in proteome-wide XL-MS.
• The proposed four data-quality metrics offer a more robust framework for evaluating XL-MS data reliability.
• Implementing these metrics will enhance the quality and trustworthiness of novel protein interaction discoveries from XL-MS studies.