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.

PTMscape: an open source tool to predict generic post-translational modifications and map hotspots of modification crosstalk

“…Including analysis of such features of the protein three-dimensional structure proved to be highly valuable in particular for modifications such as ubiquitin that lack a defined sequence motif. 44,45 These aspects also illustrate future extensions that address some of PTMscape's current limitations. For example, for statistically robust analysis, a few thousands of experimentally detected sites need to be available.…”

Tyrosine phosphorylation switching of a G protein

“…As a result, each MAP receives a probability score that can be used to rankorder the MAP from low to high function potential (low to high probability score, respectively). Previous results showed that SAPH-ire is highly effective for identification of functional PTM hotspots, and was validated both computationally as well as empirically (39,40).…”

“…S1) represented specifically by G subunits (IPR001019) -the vast majority of which are due to phosphorylation (39,40 (Fig. 1A).…”

“…Surveying from N-to C-terminus the density of MAPs along the length of a protein family reveals regions of local structure that exceed the average density of modification for the family -a characteristic that is common for functional MAPs (39). Therefore, we compared the MAP cluster density for every MAP between the N-and C-terminus of the G family -reporting the number of total MAPs contained within +/-2 residues of each modified position (Fig.…”

“…SAPH-ire is described in detail (39,40) and was utilized here with only minor adjustments. Briefly, neural network models for function potential probability scores were generated using maximum observed solvent accessible surface area for each hotspot.…”