Discovering the landscape of protein modifications

Abstract

Protein modifications modulate nearly every aspect of cell biology in organisms, ranging from Archaea to Eukaryotes. The earliest evidence of covalent protein modifications was found in the early 20th century by studying the amino acid composition of proteins by chemical hydrolysis. These discoveries challenged what defined a canonical amino acid. The advent and rapid adoption of mass-spectrometry-based proteomics in the latter part of the 20th century enabled a veritable explosion in the number of known protein modifications, with more than 500 discrete modifications counted today. Now, new computational tools in data science, machine learning, and artificial intelligence are poised to allow researchers to make significant progress in discovering new protein modifications and determining their function. In this review, we take an opportunity to revisit the historical discovery of key post-translational modifications, quantify the current landscape of covalent protein adducts, and assess the role that new computational tools will play in the future of this field.

Keywords: amino acid; metabolism; post-translational modifications; protein acylation; protein modifications.

Figure 1.

Twenty proteinogenic amino acid structures depicted using standard Corey–Pauling–Koltun coloring convention: black, carbon; red, oxygen; blue, nitrogen, yellow, sulfur; thin lines, single bond; medium lines, double bond; hydrogens omitted for clarity.

Figure 2.

Timeline of protein modification discovery. Cumulative number of protein modifications known at each year. For methods of calculation and code, see www.github.com/matthewhirschey.

Figure 3.

Current landscape of protein modifications. A. Approximately 500 protein modifications have been described across all 20 protein amino acids; colors represent most frequent modifications by ‘keyword’; protein crosslinks were removed from analysis. Data accessed from www.uniprot.org (UniProt Consortium, 2018). For methods of calculation and code, see www.github.com/hirscheylab. B. Distribution of masses of modifications appended to proteins. Line represents frequency of masses, scaled to 1; rug-plot hashes represent individual masses. Protein crosslinks were removed from analysis. For methods of calculation and code, see www.github.com/matthewhirschey.

Figure 4.

Known acyl-CoA species. Chain length and chemical properties of all known acyl-CoA metabolites in humans. Data accessed from www.hmdb.ca (Wishart et al., 2018). For methods of calculation and code, see www.github.com/matthewhirschey.

Figure 5.

Predicted protein modifications. A. Acyl-CoA species from the human metabolome database were compared to known masses matching lysine modifications; B. The human metabolome database was queried for known reactive acyl-phosphates, thioesters, or aldehydes, and plotted for number of possible carbons appended to proteins. Data accessed from www.hmdb.ca (Wishart et al., 2018). For methods of calculation and code, see www.github.com/matthewhirschey.