Dataset Information

Structural Analysis of PTM Hotspots (SAPH-ire)--A Quantitative Informatics Method Enabling the Discovery of Novel Regulatory Elements in Protein Families.

ABSTRACT: Predicting the biological function potential of post-translational modifications (PTMs) is becoming increasingly important in light of the exponential increase in available PTM data from high-throughput proteomics. We developed structural analysis of PTM hotspots (SAPH-ire)--a quantitative PTM ranking method that integrates experimental PTM observations, sequence conservation, protein structure, and interaction data to allow rank order comparisons within or between protein families. Here, we applied SAPH-ire to the study of PTMs in diverse G protein families, a conserved and ubiquitous class of proteins essential for maintenance of intracellular structure (tubulins) and signal transduction (large and small Ras-like G proteins). A total of 1728 experimentally verified PTMs from eight unique G protein families were clustered into 451 unique hotspots, 51 of which have a known and cited biological function or response. Using customized software, the hotspots were analyzed in the context of 598 unique protein structures. By comparing distributions of hotspots with known versus unknown function, we show that SAPH-ire analysis is predictive for PTM biological function. Notably, SAPH-ire revealed high-ranking hotspots for which a functional impact has not yet been determined, including phosphorylation hotspots in the N-terminal tails of G protein gamma subunits--conserved protein structures never before reported as regulators of G protein coupled receptor signaling. To validate this prediction we used the yeast model system for G protein coupled receptor signaling, revealing that gamma subunit-N-terminal tail phosphorylation is activated in response to G protein coupled receptor stimulation and regulates protein stability in vivo. These results demonstrate the utility of integrating protein structural and sequence features into PTM prioritization schemes that can improve the analysis and functional power of modification-specific proteomics data.

SUBMITTER: Dewhurst HM

PROVIDER: S-EPMC4528253 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Publications

Structural Analysis of PTM Hotspots (SAPH-ire)--A Quantitative Informatics Method Enabling the Discovery of Novel Regulatory Elements in Protein Families.

Dewhurst Henry M HM Choudhury Shilpa S Torres Matthew P MP

Molecular & cellular proteomics : MCP 20150612 8

Predicting the biological function potential of post-translational modifications (PTMs) is becoming increasingly important in light of the exponential increase in available PTM data from high-throughput proteomics. We developed structural analysis of PTM hotspots (SAPH-ire)--a quantitative PTM ranking method that integrates experimental PTM observations, sequence conservation, protein structure, and interaction data to allow rank order comparisons within or between protein families. Here, we app ...[more]

PMID: 26070665

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Project description: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.

Project description:Post-transcriptional gene regulation controls the amount of protein produced from an individual mRNA by altering rates of decay and translation. Many sequence elements that direct post-transcriptional regulation have been found; in mammals, most such elements are located within the 3' untranslated regions (3'UTRs). Comparative genomic studies demonstrate that mammalian 3'UTRs contain extensive conserved sequence tracts, yet only a small fraction corresponds to recognized elements, implying that many additional novel elements exist. Despite a variety of computational, molecular, and biochemical approaches, identifying functional 3'UTRs elements remains difficult.We created a high-throughput cell-based screen that enables identification of functional post-transcriptional 3'UTR regulatory elements. Our system exploits integrated single-copy reporters, which are expressed and processed as endogenous genes. We screened many thousands of short random sequences for their regulatory potential. Control sequences with known effects were captured effectively using our approach, establishing that our methodology was robust. We found hundreds of functional sequences, which we validated in traditional reporter assays, including verifying their regulatory impact in native sequence contexts. Although 3'UTRs are typically considered repressive, most of the functional elements were activating, including ones that were preferentially conserved. Additionally, we adapted our screening approach to examine the effect of elements on RNA abundance, revealing that most elements act by altering mRNA stability.We developed and used a high-throughput approach to discover hundreds of post-transcriptional cis-regulatory elements. These results imply that most human 3'UTRs contain many previously unrecognized cis-regulatory elements, many of which are activating, and that the post-transcriptional fate of an mRNA is largely due to the actions of many individual cis-regulatory elements within its 3'UTR.

Dataset Information

Structural Analysis of PTM Hotspots (SAPH-ire)--A Quantitative Informatics Method Enabling the Discovery of Novel Regulatory Elements in Protein Families.

Publications

Structural Analysis of PTM Hotspots (SAPH-ire)--A Quantitative Informatics Method Enabling the Discovery of Novel Regulatory Elements in Protein Families.

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