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Insights into Disease-Associated Mutations in the Human Proteome through Protein Structural Analysis.

ABSTRACT: Most known disease-associated mutations are missense mutations involving changes of amino acids of proteins encoded by their genes. Given the plethora of genetic studies, sequenced exomes, and new protein structures determined each year, it is appropriate to revisit the role that structure plays in providing insights into the molecular basis of disease-associated mutations. In that regard, a large-scale structural analysis of 6,025 disease-associated mutations as well as 4,536 neutral variations for comparison was performed. While buried amino acids are common among the disease-associated mutations, as reported previously, more are statistically significantly enriched at observed or predicted functional sites. Interesting findings are that ligand-binding sites adjacent to protein-protein interfaces and residues involved in enzymatic function are especially vulnerable to disease-associated mutations. Finally, a compositional analysis of disease-associated mutations in comparison with variants identified in the 1000 Genomes Project provides a structural rationalization of the most disease-associated residue types.

SUBMITTER: Gao M

PROVIDER: S-EPMC4497952 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Insights into Disease-Associated Mutations in the Human Proteome through Protein Structural Analysis.

Gao Mu M Zhou Hongyi H Skolnick Jeffrey J

Structure (London, England : 1993) 20150528 7

Most known disease-associated mutations are missense mutations involving changes of amino acids of proteins encoded by their genes. Given the plethora of genetic studies, sequenced exomes, and new protein structures determined each year, it is appropriate to revisit the role that structure plays in providing insights into the molecular basis of disease-associated mutations. In that regard, a large-scale structural analysis of 6,025 disease-associated mutations as well as 4,536 neutral variations ...[more]

PMID: 26027735

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Project description:Mitochondria are the energy producing organelles of the cell, and mutations within their genome can cause numerous and often severe human diseases. At the heart of every mitochondrion is a set of five large multi-protein machines collectively known as the mitochondrial respiratory chain (MRC). This cellular machinery is central to several processes important for maintaining homeostasis within cells, including the production of ATP. The MRC is unique due to the bigenomic origin of its interacting proteins, which are encoded in the nucleus and mitochondria. It is this, in combination with the sheer number of protein-protein interactions that occur both within and between the MRC complexes, which makes the prediction of function and pathological outcome from primary sequence mutation data extremely challenging. Here we demonstrate how 3D structural analysis can be employed to predict the functional importance of mutations in mtDNA protein-coding genes. We mined the MITOMAP database and, utilizing the latest structural data, classified mutation sites based on their location within the MRC complexes III and IV. Using this approach, four structural classes of mutation were identified, including one underexplored class that interferes with nuclear-mitochondrial protein interactions. We demonstrate that this class currently eludes existing predictive approaches that do not take into account the quaternary structural organization inherent within and between the MRC complexes. The systematic and detailed structural analysis of disease-associated mutations in the mitochondrial Complex III and IV genes significantly enhances the predictive power of existing approaches and our understanding of how such mutations contribute to various pathologies. Given the general lack of any successful therapeutic approaches for disorders of the MRC, these findings may inform the development of new diagnostic and prognostic biomarkers, as well as new drugs and targets for gene therapy.

Project description:BackgroundMutation(s) in proteins are a natural byproduct of evolution but can also cause serious diseases. Aminoacyl-tRNA synthetases (aaRSs) are indispensable components of all cellular protein translational machineries, and in humans they drive translation in both cytoplasm and mitochondria. Mutations in aaRSs have been implicated in a plethora of diseases including neurological conditions, metabolic disorders and cancer.ResultsWe have developed an algorithmic approach for genome-wide analyses of sequence substitutions that combines evolutionary, structural and functional information. This pipeline enabled us to super-annotate human aaRS mutations and analyze their linkage to health disorders. Our data suggest that in some but not all cases, aaRS mutations occur in functional and structural sectors where they can manifest their pathological effects by altering enzyme activity or causing structural instability. Further, mutations appear in both solvent exposed and buried regions of aaRSs indicating that these alterations could lead to dysfunctional enzymes resulting in abnormal protein translation routines by affecting inter-molecular interactions or by disruption of non-bonded interactions. Overall, the prevalence of mutations is much higher in mitochondrial aaRSs, and the two most often mutated aaRSs are mitochondrial glutamyl-tRNA synthetase and dual localized glycyl-tRNA synthetase. Out of 63 mutations annotated in this work, only 12 (~20%) were observed in regions that could directly affect aminoacylation activity via either binding to ATP/amino-acid, tRNA or by involvement in dimerization. Mutations in structural cores or at potential biomolecular interfaces account for ~55% mutations while remaining mutations (~25%) remain structurally un-annotated.ConclusionThis work provides a comprehensive structural framework within which most defective human aaRSs have been structurally analyzed. The methodology described here could be employed to annotate mutations in other protein families in a high-throughput manner.

Dataset Information

Insights into Disease-Associated Mutations in the Human Proteome through Protein Structural Analysis.

Publications

Insights into Disease-Associated Mutations in the Human Proteome through Protein Structural Analysis.

Similar Datasets

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