Browse
Submit Data
Databases
API
Help

Dataset Information

23 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Influence of Disease-Causing Mutations on Protein Structural Networks.

ABSTRACT: The interactions between residues in a protein tertiary structure can be studied effectively using the approach of protein structure network (PSN). A PSN is a node-edge representation of the structure with nodes representing residues and interactions between residues represented by edges. In this study, we have employed weighted PSNs to understand the influence of disease-causing mutations on proteins of known 3D structures. We have used manually curated information on disease mutations from UniProtKB/Swiss-Prot and their corresponding protein structures of wildtype and disease variant from the protein data bank. The PSNs of the wildtype and disease-causing mutant are compared to analyse variation of global and local dissimilarity in the overall network and at specific sites. We study how a mutation at a given site can affect the structural network at a distant site which may be involved in the function of the protein. We have discussed specific examples of the disease cases where the protein structure undergoes limited structural divergence in their backbone but have large dissimilarity in their all atom networks and vice versa, wherein large conformational alterations are observed while retaining overall network. We analyse the effect of variation of network parameters that characterize alteration of function or stability.

SUBMITTER: Prabantu VM

PROVIDER: S-EPMC7987782 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Disease-causing mutations in proteins: structural analysis of the CYP1B1 mutations causing primary congenital glaucoma in humans.

Project description:In this communication, we report an in-depth structure-based analysis of the human CYP1b1 protein carrying disease-causing mutations that are discovered in patients suffering from primary congenital glaucoma (PCG). The "wild-type" and the PCG mutant structures of the human CYP1b1 protein obtained from comparative modeling were subjected to long molecular dynamics simulations with an intention of studying the possible impact of these mutations on the protein structure and hence its function. Analysis of time evolution as well as time averaged values of various structural properties--especially of those of the functionally important regions: the heme binding region, substrate binding region, and substrate access channel--gave some insights into the possible structural characteristics of the disease mutant and the wild-type forms of the protein. In a nutshell, compared to the wild-type the core regions in the mutant structures are associated with subtle but significant changes, and the functionally important regions seem to adopt such structures that are not conducive for the wild-type-like functionality.

| S-EPMC1779944 | biostudies-literature

Disease-causing point-mutations in metal-binding domains of Wilson disease protein decrease stability and increase structural dynamics.

Project description:After cellular uptake, Copper (Cu) ions are transferred from the chaperone Atox1 to the Wilson disease protein (ATP7B) for incorporation into Cu-dependent enzymes in the secretory pathway. Human ATP7B is a large multi-domain membrane-spanning protein which, in contrast to homologues in other organisms, has six similar cytoplasmic metal-binding domains (MBDs). The reason for multiple MBDs is proposed to be indirect modulation of enzymatic activity and it is thus intriguing that point mutations in MBDs can promote Wilson disease. We here investigated, in vitro and in silico, the biophysical consequences of clinically-observed Wilson disease mutations, G85V in MBD1 and G591D in MBD6, incorporated in domain 4. Because G85 and G591 correspond to a conserved Gly found in all MBDs, we introduced the mutations in the well-characterized MBD4. We found the mutations to dramatically reduce the MBD4 thermal stability, shifting the midpoint temperature of unfolding by more than 20 °C. In contrast to wild type MBD4 and MBD4D, MBD4V adopted a misfolded structure with a large β-sheet content at high temperatures. Molecular dynamic simulations demonstrated that the mutations increased backbone fluctuations that extended throughout the domain. Our findings imply that reduced stability and enhanced dynamics of MBD1 or MBD6 is the origin of ATP7B dysfunction in Wilson disease patients with the G85V or G591D mutation.

| S-EPMC5285417 | biostudies-literature

Structural basis of disease-causing mutations in hepatocyte nuclear factor 1beta.

Project description:HNF1beta is an atypical POU transcription factor that participates in a hierarchical network of transcription factors controlling the development and proper function of vital organs such as liver, pancreas, and kidney. Many inheritable mutations on HNF1beta are the monogenic causes of diabetes and several kidney diseases. To elucidate the molecular mechanism of its function and the structural basis of mutations, we have determined the crystal structure of human HNF1beta DNA binding domain in complex with a high-affinity promoter. Disease-causing mutations have been mapped to our structure, and their predicted effects have been tested by a set of biochemical/ functional studies. These findings together with earlier findings with a homologous protein HNF1alpha, help us to understand the structural basis of promoter recognition by these atypical POU transcription factors and the site-specific functional disruption by disease-causing mutations.

| S-EPMC2367142 | biostudies-literature

Spectrum of disease-causing mutations in protein secondary structures.

Project description:BACKGROUND: Most genetic disorders are linked to missense mutations as even minor changes in the size or properties of an amino acid can alter or prevent the function of the protein. Further, the effect of a mutation is also dependent on the sequence and structure context of the alteration. RESULTS: We investigated the spectrum of disease-causing missense mutations in secondary structure elements in proteins with numerous known mutations and for which an experimentally defined three-dimensional structure is available. We obtained a comprehensive map of the differences in mutation frequencies, location and contact energies, and the changes in residue volume and charge - both in the mutated (original) amino acids and in the mutant amino acids in the different secondary structure types. We collected information for 44 different proteins involved in a large number of diseases. The studied proteins contained a total of 2413 mutations of which 1935 (80%) appeared in secondary structures. Differences in mutation patterns between secondary structures and whole proteins were generally not statistically significant whereas within the secondary structural elements numerous highly significant features were observed. CONCLUSION: Numerous trends in mutated and mutant amino acids are apparent. Among the original residues, arginine clearly has the highest relative mutability. The overall relative mutability among mutant residues is highest for cysteine and tryptophan. The mutability values are higher for mutated residues than for mutant residues. Arginine and glycine are among the most mutated residues in all secondary structures whereas the other amino acids have large variations in mutability between structure types. Statistical analysis was used to reveal trends in different secondary structural elements, residue types as well as for the charge and volume changes.

| S-EPMC1995201 | biostudies-literature

Background sequence characteristics influence the occurrence and severity of disease-causing mtDNA mutations.

Project description:Inherited mitochondrial DNA (mtDNA) mutations have emerged as a common cause of human disease, with mutations occurring multiple times in the world population. The clinical presentation of three pathogenic mtDNA mutations is strongly associated with a background mtDNA haplogroup, but it is not clear whether this is limited to a handful of examples or is a more general phenomenon. To address this, we determined the characteristics of 30,506 mtDNA sequences sampled globally. After performing several quality control steps, we ascribed an established pathogenicity score to the major alleles for each sequence. The mean pathogenicity score for known disease-causing mutations was significantly different between mtDNA macro-haplogroups. Several mutations were observed across all haplogroup backgrounds, whereas others were only observed on specific clades. In some instances this reflected a founder effect, but in others, the mutation recurred but only within the same phylogenetic cluster. Sequence diversity estimates showed that disease-causing mutations were more frequent on young sequences, and genomes with two or more disease-causing mutations were more common than expected by chance. These findings implicate the mtDNA background more generally in recurrent mutation events that have been purified through natural selection in older populations. This provides an explanation for the low frequency of mtDNA disease reported in specific ethnic groups.

| S-EPMC5757940 | biostudies-literature

KinMutBase, a database of human disease-causing protein kinase mutations.

Project description:KinMutBase (http://www.uta.fi/imt/bioinfo/KinMutBase/) is a registry of mutations in human protein kinases related to disorders. Kinases are essential cellular signaling molecules, in which mutations can lead to diseases, including immunodeficiencies, cancers and endocrine disorders. The first release of KinMutBase contained information for protein tyrosine kinases. The current release includes also serine/threonine protein kinases, as well as an update of the tyrosine kinases. There are 251 entries altogether, representing 337 families and 621 patients. Mutations appear both in conserved hallmark residues of the kinases as well as in non-homologous sites. The KinMutBase WWW pages provide plenty of information, namely mutation statistics and display, clickable sequences with mutations and changes to restriction enzyme patterns.

| S-EPMC102425 | biostudies-literature

Structural consequences of disease-causing mutations in the ATRX-DNMT3-DNMT3L (ADD) domain of the chromatin-associated protein ATRX.

Project description:The chromatin-associated protein ATRX was originally identified because mutations in the ATRX gene cause a severe form of syndromal X-linked mental retardation associated with alpha-thalassemia. Half of all of the disease-associated missense mutations cluster in a cysteine-rich region in the N terminus of ATRX. This region was named the ATRX-DNMT3-DNMT3L (ADD) domain, based on sequence homology with a family of DNA methyltransferases. Here, we report the solution structure of the ADD domain of ATRX, which consists of an N-terminal GATA-like zinc finger, a plant homeodomain finger, and a long C-terminal alpha-helix that pack together to form a single globular domain. Interestingly, the alpha-helix of the GATA-like finger is exposed and highly basic, suggesting a DNA-binding function for ATRX. The disease-causing mutations fall into two groups: the majority affect buried residues and hence affect the structural integrity of the ADD domain; another group affects a cluster of surface residues, and these are likely to perturb a potential protein interaction site. The effects of individual point mutations on the folding state and stability of the ADD domain correlate well with the levels of mutant ATRX protein in patients, providing insights into the molecular pathophysiology of ATR-X syndrome.

| S-EPMC1924575 | biostudies-literature

Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks.

Project description:The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction (PPI) network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network. However, to date, these methods have used a static picture of human PPIs, while diseases impact specific tissues in which the PPI networks may be dramatically different. Here, for the first time, we perform a large-scale assessment of the contribution of tissue-specific information to gene prioritization. By integrating tissue-specific gene expression data with PPI information, we construct tissue-specific PPI networks for 60 tissues and investigate their prioritization power. We find that tissue-specific PPI networks considerably improve the prioritization results compared to those obtained using a generic PPI network. Furthermore, they allow predicting novel disease-tissue associations, pointing to sub-clinical tissue effects that may escape early detection.

| S-EPMC3459874 | biostudies-literature

Identifying disease-causing mutations with privacy protection.

Project description:MotivationThe use of genome data for diagnosis and treatment is becoming increasingly common. Researchers need access to as many genomes as possible to interpret the patient genome, to obtain some statistical patterns and to reveal disease-gene relationships. The sensitive information contained in the genome data and the high risk of re-identification increase the privacy and security concerns associated with sharing such data. In this article, we present an approach to identify disease-associated variants and genes while ensuring patient privacy. The proposed method uses secure multi-party computation to find disease-causing mutations under specific inheritance models without sacrificing the privacy of individuals. It discloses only variants or genes obtained as a result of the analysis. Thus, the vast majority of patient data can be kept private.ResultsOur prototype implementation performs analyses on thousands of genomic data in milliseconds, and the runtime scales logarithmically with the number of patients. We present the first inheritance model (recessive, dominant and compound heterozygous) based privacy-preserving analyses of genomic data to find disease-causing mutations. Furthermore, we re-implement the privacy-preserving methods (MAX, SETDIFF and INTERSECTION) proposed in a previous study. Our MAX, SETDIFF and INTERSECTION implementations are 2.5, 1122 and 341 times faster than the corresponding operations of the state-of-the-art protocol, respectively.Availability and implementationhttps://gitlab.com/DIFUTURE/privacy-preserving-genomic-diagnosis.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC7850099 | biostudies-literature

Forces and Disease: Electrostatic force differences caused by mutations in kinesin motor domains can distinguish between disease-causing and non-disease-causing mutations.

Project description:The ability to predict if a given mutation is disease-causing or not has enormous potential to impact human health. Typically, these predictions are made by assessing the effects of mutation on macromolecular stability and amino acid conservation. Here we report a novel feature: the electrostatic component of the force acting between a kinesin motor domain and tubulin. We demonstrate that changes in the electrostatic component of the binding force are able to discriminate between disease-causing and non-disease-causing mutations found in human kinesin motor domains using the receiver operating characteristic (ROC). Because diseases may originate from multiple effects not related to kinesin-microtubule binding, the prediction rate of 0.843 area under the ROC plot due to the change in magnitude of the electrostatic force alone is remarkable. These results reflect the dependence of kinesin's function on motility along the microtubule, which suggests a precise balance of microtubule binding forces is required.

| S-EPMC5557957 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data