title: Proteome-wide Structural Analysis of PTM Hotspots Reveals Regulatory Elements Predicted to Impact Biological Function and Disease.
abstract: Post-translational modifications (PTMs) regulate protein behavior through modulation of protein-protein interactions, enzymatic activity, and protein stability essential in the translation of genotype to phenotype in eukaryotes. Currently, less than 4% of all eukaryotic PTMs are reported to have biological function - a statistic that continues to decrease with an increasing rate of PTM detection. Previously, we developed SAPH-ire (Structural Analysis of PTM Hotspots) - a method for the prioritization of PTM function potential that has been used effectively to reveal novel PTM regulatory elements in discrete protein families (Dewhurst et al., 2015). Here, we apply SAPH-ire to the set of eukaryotic protein families containing experimental PTM and 3D structure data - capturing 1,325 protein families with 50,839 unique PTM sites organized into 31,747 modified alignment positions (MAPs), of which 2010 (∼6%) possess known biological function. Here, we show that using an artificial neural network model (SAPH-ire NN) trained to identify MAP hotspots with biological function results in prediction outcomes that far surpass the use of single hotspot features, including nearest neighbor PTM clustering methods. We find the greatest enhancement in prediction for positions with PTM counts of five or less, which represent 98% of all MAPs in the eukaryotic proteome and 90% of all MAPs found to have biological function. Analysis of the top 1092 MAP hotspots revealed 267 of truly unknown function (containing 5443 distinct PTMs). Of these, 165 hotspots could be mapped to human KEGG pathways for normal and/or disease physiology. Many high-ranking hotspots were also found to be disease-associated pathogenic sites of amino acid substitution despite the lack of observable PTM in the human protein family member. Taken together, these experiments demonstrate that the functional relevance of a PTM can be predicted very effectively by neural network models, revealing a large but testable body of potential regulatory elements that impact hundreds of different biological processes important in eukaryotic biology and human health.
authors.affiliation: From the School of Biological Sciences; Georgia Institute of Technology; Atlanta, Georgia 30332 1matthew.torres@biology.gatech.edu.
authors.forename: Matthew P
authors.identifier: 0000-0001-7296-4536
authors.initials: MP
authors.lastname: Torres
authors.affiliation: From the School of Biological Sciences; Georgia Institute of Technology; Atlanta, Georgia 30332.
authors.forename: Henry
authors.initials: H
authors.lastname: Dewhurst
authors.affiliation: From the School of Biological Sciences; Georgia Institute of Technology; Atlanta, Georgia 30332.
authors.forename: Niveda
authors.initials: N
authors.lastname: Sundararaman
mesh_terms: D000595:Amino Acid Sequence; D019943:Amino Acid Substitution; D019295:Computational Biology / Q000379:methods*; D020022:Genetic Predisposition to Disease; D006801:Humans; D005810:Multigene Family; D016571:Neural Networks, Computer; D011487:Protein Conformation; D011499:Protein Processing, Post-Translational; D020543:Proteome / Q000737:chemistry* / Q000235:genetics; D012984:Software
chemical_list: D020543:Proteome