Structure-based validation can drastically underestimate error rate in proteome-wide cross-linking mass spectrometry
Kumar Yugandhar1,2, Ting-Yi Wang1,2, Shayne D Wierbowski1,2
1Department of Computational Biology, Cornell University, Ithaca, NY, USA.
Nature Methods
|September 30, 2020
Abstract:
Thorough quality assessment of novel interactions identified by proteome-wide cross-linking mass spectrometry (XL-MS) studies is critical. Almost all current XL-MS studies have validated cross-links against known three-dimensional structures of representative protein complexes. Here, we provide theoretical and experimental evidence demonstrating that this approach can drastically underestimate error rates for proteome-wide XL-MS datasets, and propose a comprehensive set of four data-quality metrics to address this issue.
