Open AccessStructure-based validation can drastically underestimate error rate in proteome-wide cross-linking mass spectrometry studies
Author(s) -
Kumar Yugandhar,
TingYi Wang,
Shayne D. Wierbowski,
Elnur Elyar Shayhidin,
Haiyuan Yu
Publication year - 2020
Publication title -
nature methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 19.469
H-Index - 318
eISSN - 1548-7105
pISSN - 1548-7091
DOI - 10.1038/s41592-020-0959-9
Subject(s) - proteome , mass spectrometry , computational biology , computer science , cross validation , set (abstract data type) , data mining , data set , chemistry , bioinformatics , biology , chromatography , machine learning , artificial intelligence , programming language
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.