Dataset Information

Disease-related mutations predicted to impact protein function.

ABSTRACT:

Background

Non-synonymous single nucleotide polymorphisms (nsSNPs) alter the protein sequence and can cause disease. The impact has been described by reliable experiments for relatively few mutations. Here, we study predictions for functional impact of disease-annotated mutations from OMIM, PMD and Swiss-Prot and of variants not linked to disease.

Results

Most disease-causing mutations were predicted to impact protein function. More surprisingly, the raw predictions scores for disease-causing mutations were higher than the scores for the function-altering data set originally used for developing the prediction method (here SNAP). We might expect that diseases are caused by change-of-function mutations. However, it is surprising how well prediction methods developed for different purposes identify this link. Conversely, our predictions suggest that the set of nsSNPs not currently linked to diseases contains very few strong disease associations to be discovered.

Conclusions

Firstly, annotations of disease-causing nsSNPs are on average so reliable that they can be used as proxies for functional impact. Secondly, disease-causing nsSNPs can be identified very well by methods that predict the impact of mutations on protein function. This implies that the existing prediction methods provide a very good means of choosing a set of suspect SNPs relevant for disease.

SUBMITTER: Schaefer C

PROVIDER: S-EPMC3394413 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Publications

Disease-related mutations predicted to impact protein function.

Schaefer Christian C Bromberg Yana Y Achten Dominik D Rost Burkhard B

BMC genomics 20120618

<h4>Background</h4>Non-synonymous single nucleotide polymorphisms (nsSNPs) alter the protein sequence and can cause disease. The impact has been described by reliable experiments for relatively few mutations. Here, we study predictions for functional impact of disease-annotated mutations from OMIM, PMD and Swiss-Prot and of variants not linked to disease.<h4>Results</h4>Most disease-causing mutations were predicted to impact protein function. More surprisingly, the raw predictions scores for dis ...[more]

PMID: 22759649

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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:LRP5 loss-of-function mutations have been shown to cause profound osteoporosis and have been associated with impaired insulin sensitivity and dysregulated lipid metabolism. We hypothesized that gain-of-function mutations in LRP5 would also affect these parameters. We therefore studied individuals with LRP5 gain-of-function mutations exhibiting high bone mass (HBM) phenotypes and found that while there was no detected change in insulin sensitivity, there was a significant reduction in serum LDL.IntroductionWnt signaling through LRP5 represents a newly appreciated metabolic pathway, which potentially represents a target for drug discovery in type 2 diabetes and hyperlipidemia. Studies in animal models suggest a physiologic link between LRP5 and glucose and lipid homeostasis; however, whether it plays a similar role in humans is unclear. As current literature links loss-of-function LRP5 to impaired glucose and lipid metabolism, we hypothesized that individuals with an HBM-causing mutation in LRP5 would exhibit improved glucose and lipid homeostasis. Since studies in animal models have suggested that Wnt signaling augments insulin secretion, we also examined the effect of Wnt signaling on glucose-stimulated insulin secretion on human pancreatic islets.MethodsThis was a matched case-control study. We used several methods to assess glucose and lipid metabolism in 11 individuals with HBM-causing mutations in LRP5. Affected study participants were recruited from previously identified kindreds with HBM-causing LRP5 mutations and included 9 males and 2 females. Two subjects that were being treated with insulin for type 2 diabetes were excluded from our analysis, as this would have obscured our ability to determine the impact of gain-of-function LRP5 mutations on glucose metabolism. The mean age of the evaluated study subjects was 55 ± 7 with a mean BMI of 27.2 ± 2.0. Control subjects were matched and recruited from the general community at an equivalent ratio, with 18 males and 4 females (mean age 56 ± 4; mean BMI 27.2 ± 1.0). Study testing was conducted at an academic medical center.ResultsThere were no statistically significant differences between affected and matched control populations for HbA1c (p = 0.06), eAG (p = 0.06), insulin (p = 0.82), HOMA-B (p = 0.34), or HOMA-IR (p = 0.66). The mean Insulin Sensitivity Index (ISI) was also similar between control and affected individuals. Total cholesterol (p = 0.43), triglycerides (TG) (p = 0.56), and HDL (p = 0.32) were not different between the same two groups. In a small subset of studied subjects, intramyocellular and hepatic lipid content were similar in the affected individuals and controls when quantified by proton magnetic resonance spectroscopy (MRS). However, the mean value for serum LDL was significantly lower (p = 0.04) in affected individuals. In primary human islets, there were no differences between control and Wnt treatment groups for insulin secretion measured as area under the curve (AUC) for first phase (p = 0.17) or second phase (p = 0.33) insulin secretion.ConclusionsAlthough our sample size was small, our data do not support the hypothesis that HBM-causing LRP5 mutations, associated with increased Wnt signaling, improve glucose metabolism in humans. However, it does appear that LRP5 variants may affect LDL metabolism, a major risk factor for coronary artery disease. The molecular mechanisms underpinning this effect warrant further study.

Dataset Information

Disease-related mutations predicted to impact protein function.

Background

Results

Conclusions

Publications

Disease-related mutations predicted to impact protein function.

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