Combinatorial post-translational modifications of proteins, such as histones, govern cell differentiation and organismal development, and are widely thought to play a key role in aging, development of cancers, neurodevelopmental disorders, neurodegenerative and other diseases. Nonetheless, our understanding of the precise biological function of different modification patterns is limited by the difficulty of identifying and quantifying different combinatorial isomers in their mixtures as they naturally occur, and in particular by the fundamental incompleteness of the information contained in a one-dimensional mass spectrum featuring the mass-to-charge ratios and relative abundances of the individual peptide fragments. Here we introduce the concept of “marker correlations”, obtained through the recently developed two-dimensional partial covariance mass spectrometry, and show that they can contain the missing information that enables identification of co-existing combinatorially modified isomers in their mixtures. We demonstrate experimentally how the marker correlations enable one to resolve mixtures of doubly acetylated histone H4 peptides, a problem that was previously branded “mathematically impossible” [D. Phanstiel et al., PNAS 105, 4093 (2008)]. Our accompanying comprehensive in silico study reveals that the marker correlations can be used to unambiguously identify five times more combinatorially modified tryptic peptides and three times more combinatorially modified Glu-C peptides of human histones than is possible using the standard MS/MS. The proof-of-concept study reported here shows that the marker correlation method has a great potential for solving the histone code problem, contingent on achieving compatibility of 2D-PC-MS with chromatography-based proteomic workflows.
Breaking the Histone Code with Two-Dimensional Partial Covariance Mass Spectrometry - Supplementary Materials