Browse
Submit Data
Databases
API
Help

Dataset Information

22 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Integrating structural and evolutionary data to interpret variation and pathogenicity in adapter protein complex 4.

ABSTRACT: Genetic variation in the membrane trafficking adapter protein complex 4 (AP-4) can result in pathogenic neurological phenotypes including microencephaly, spastic paraplegias, epilepsy, and other developmental defects. We lack molecular mechanisms responsible for impaired AP-4 function arising from genetic variation, because AP-4 remains poorly understood structurally. Here, we analyze patterns of AP-4 genetic evolution and conservation to identify regions that are likely important for function and thus more susceptible to pathogenic variation. We map known variants onto an AP-4 homology model and predict the likelihood of pathogenic variation at a given location on the structure of AP-4. We find significant clustering of likely pathogenic variants located at the interface between the β4 and N-μ4 subunits, as well as throughout the C-μ4 subunit. Our work offers an integrated perspective on how genetic and evolutionary forces affect AP-4 structure and function. As more individuals with uncharacterized AP-4 variants are identified, our work provides a foundation upon which their functional effects and disease relevance can be interpreted.

SUBMITTER: Gadbery JE

PROVIDER: S-EPMC7255511 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Web-based Gene Pathogenicity Analysis (WGPA): a web platform to interpret gene pathogenicity from personal genome data.

Project description:UnlabelledAs the volume of patient-specific genome sequences increases the focus of biomedical research is switching from the detection of disease-mutations to their interpretation. To this end a number of techniques have been developed that use mutation data collected within a population to predict whether individual genes are likely to be disease-causing or not. As both sequence data and associated analysis tools proliferate, it becomes increasingly difficult for the community to make sense of these data and their implications. Moreover, no single analysis tool is likely to capture all relevant genomic features that contribute to the gene's pathogenicity. Here, we introduce Web-based Gene Pathogenicity Analysis (WGPA), a web-based tool to analyze genes impacted by mutations and rank them through the integration of existing prioritization tools, which assess different aspects of gene pathogenicity using population-level sequence data. Additionally, to explore the polygenic contribution of mutations to disease, WGPA implements gene set enrichment analysis to prioritize disease-causing genes and gene interaction networks, therefore providing a comprehensive annotation of personal genomes data in disease.Availability and implementationwgpa.systems-genetics.net.

| S-EPMC4743624 | biostudies-literature

svpluscnv: analysis and visualization of complex structural variation data.

Project description:MotivationDespite widespread prevalence of somatic structural variations (SVs) across most tumor types, understanding of their molecular implications often remains poor. SVs are extremely heterogeneous in size and complexity, hindering the interpretation of their pathogenic role. Tools integrating large SV datasets across platforms are required to fully characterize the cancer's somatic landscape.Resultssvpluscnv R package is a swiss army knife for the integration and interpretation of orthogonal datasets including copy number variant segmentation profiles and sequencing-based structural variant calls. The package implements analysis and visualization tools to evaluate chromosomal instability and ploidy, identify genes harboring recurrent SVs and detects complex rearrangements such as chromothripsis and chromoplexia. Further, it allows systematic identification of hot-spot shattered genomic regions, showing reproducibility across alternative detection methods and datasets.Availability and implementationhttps://github.com/ccbiolab/svpluscnv.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC8487630 | biostudies-literature

An evolutionary and structural characterization of mammalian protein complex organization.

Project description:BACKGROUND: We have recently released a comprehensive, manually curated database of mammalian protein complexes called CORUM. Combining CORUM with other resources, we assembled a dataset of over 2700 mammalian complexes. The availability of a rich information resource allows us to search for organizational properties concerning these complexes. RESULTS: As the complexity of a protein complex in terms of the number of unique subunits increases, we observed that the number of such complexes and the mean non-synonymous to synonymous substitution ratio of associated genes tend to decrease. Similarly, as the number of different complexes a given protein participates in increases, the number of such proteins and the substitution ratio of the associated gene also tends to decrease. These observations provide evidence relating natural selection and the organization of mammalian complexes. We also observed greater homogeneity in terms of predicted protein isoelectric points, secondary structure and substitution ratio in annotated versus randomly generated complexes. A large proportion of the protein content and interactions in the complexes could be predicted from known binary protein-protein and domain-domain interactions. In particular, we found that large proteins interact preferentially with much smaller proteins. CONCLUSION: We observed similar trends in yeast and other data. Our results support the existence of conserved relations associated with the mammalian protein complexes.

| S-EPMC2645396 | biostudies-literature

Protein structural variation in computational models and crystallographic data.

Project description:Normal mode analysis offers an efficient way of modeling the conformational flexibility of protein structures. We use anisotropic displacement parameters from crystallography to test the quality of prediction of both the magnitude and directionality of conformational flexibility. Normal modes from four simple elastic network model potentials and from the CHARMM force field are calculated for a data set of 83 diverse, ultrahigh-resolution crystal structures. While all five potentials provide good predictions of the magnitude of flexibility, all-atom potentials have a clear edge at prediction of directionality, and the CHARMM potential has the highest prediction quality. The low-frequency modes from different potentials are similar, but those computed from the CHARMM potential show the greatest difference from the elastic network models. The comprehensive evaluation demonstrates the costs and benefits of using normal mode potentials of varying complexity.

| S-EPMC2350181 | biostudies-literature

Integrating population variation and protein structural analysis to improve clinical interpretation of missense variation: application to the WD40 domain.

Project description:We present a generic, multidisciplinary approach for improving our understanding of novel missense variants in recently discovered disease genes exhibiting genetic heterogeneity, by combining clinical and population genetics with protein structural analysis. Using six new de novo missense diagnoses in TBL1XR1 from the Deciphering Developmental Disorders study, together with population variation data, we show that the ?-propeller structure of the ubiquitous WD40 domain provides a convincing way to discriminate between pathogenic and benign variation. Children with likely pathogenic mutations in this gene have severely delayed language development, often accompanied by intellectual disability, autism, dysmorphology and gastrointestinal problems. Amino acids affected by likely pathogenic missense mutations are either crucial for the stability of the fold, forming part of a highly conserved symmetrically repeating hydrogen-bonded tetrad, or located at the top face of the ?-propeller, where 'hotspot' residues affect the binding of ?-catenin to the TBLR1 protein. In contrast, those altered by population variation are significantly less likely to be spatially clustered towards the top face or to be at buried or highly conserved residues. This result is useful not only for interpreting benign and pathogenic missense variants in this gene, but also in other WD40 domains, many of which are associated with disease.

| S-EPMC4754046 | biostudies-literature

CONSERTING: integrating copy-number analysis with structural-variation detection.

Project description:We developed Copy Number Segmentation by Regression Tree in Next Generation Sequencing (CONSERTING), an algorithm for detecting somatic copy-number alteration (CNA) using whole-genome sequencing (WGS) data. CONSERTING performs iterative analysis of segmentation on the basis of changes in read depth and the detection of localized structural variations, with high accuracy and sensitivity. Analysis of 43 cancer genomes from both pediatric and adult patients revealed novel oncogenic CNAs, complex rearrangements and subclonal CNAs missed by alternative approaches.

| S-EPMC4591043 | biostudies-literature

Challenges and standards in integrating surveys of structural variation.

Project description:There has been an explosion of data describing newly recognized structural variants in the human genome. In the flurry of reporting, there has been no standard approach to collecting the data, assessing its quality or describing identified features. This risks becoming a rampant problem, in particular with respect to surveys of copy number variation and their application to disease studies. Here, we consider the challenges in characterizing and documenting genomic structural variants. From this, we derive recommendations for standards to be adopted, with the aim of ensuring the accurate presentation of this form of genetic variation to facilitate ongoing research.

| S-EPMC2698291 | biostudies-literature

Comparison of Algorithms for Prediction of Protein Structural Features from Evolutionary Data.

Project description:Proteins have many functions and predicting these is still one of the major challenges in theoretical biophysics and bioinformatics. Foremost amongst these functions is the need to fold correctly thereby allowing the other genetically dictated tasks that the protein has to carry out to proceed efficiently. In this work, some earlier algorithms for predicting protein domain folds are revisited and they are compared with more recently developed methods. In dealing with intractable problems such as fold prediction, when different algorithms show convergence onto the same result there is every reason to take all algorithms into account such that a consensus result can be arrived at. In this work it is shown that the application of different algorithms in protein structure prediction leads to results that do not converge as such but rather they collude in a striking and useful way that has never been considered before.

| S-EPMC4786192 | biostudies-literature

The effects of incomplete protein interaction data on structural and evolutionary inferences.

Project description:BackgroundPresent protein interaction network data sets include only interactions among subsets of the proteins in an organism. Previously this has been ignored, but in principle any global network analysis that only looks at partial data may be biased. Here we demonstrate the need to consider network sampling properties explicitly and from the outset in any analysis.ResultsHere we study how properties of the yeast protein interaction network are affected by random and non-random sampling schemes using a range of different network statistics. Effects are shown to be independent of the inherent noise in protein interaction data. The effects of the incomplete nature of network data become very noticeable, especially for so-called network motifs. We also consider the effect of incomplete network data on functional and evolutionary inferences.ConclusionCrucially, when only small, partial network data sets are considered, bias is virtually inevitable. Given the scope of effects considered here, previous analyses may have to be carefully reassessed: ignoring the fact that present network data are incomplete will severely affect our ability to understand biological systems.

| S-EPMC1665463 | biostudies-literature

Integrating data-deficient species in analyses of evolutionary history loss.

Project description:There is an increasing interest in measuring loss of phylogenetic diversity and evolutionary distinctiveness which together depict the evolutionary history of conservation interest. Those losses are assessed through the evolutionary relationships between species and species threat status or extinction probabilities. Yet, available information is not always sufficient to quantify the threat status of species that are then classified as data deficient. Data-deficient species are a crucial issue as they cause incomplete assessments of the loss of phylogenetic diversity and evolutionary distinctiveness. We aimed to explore the potential bias caused by data-deficient species in estimating four widely used indices: HEDGE, EDGE, PDloss, and Expected PDloss. Second, we tested four different widely applicable and multitaxa imputation methods and their potential to minimize the bias for those four indices. Two methods are based on a best- vs. worst-case extinction scenarios, one is based on the frequency distribution of threat status within a taxonomic group and one is based on correlates of extinction risks. We showed that data-deficient species led to important bias in predictions of evolutionary history loss (especially high underestimation when they were removed). This issue was particularly important when data-deficient species tended to be clustered in the tree of life. The imputation method based on correlates of extinction risks, especially geographic range size, had the best performance and enabled us to improve risk assessments. Solving threat status of DD species can fundamentally change our understanding of loss of phylogenetic diversity. We found that this loss could be substantially higher than previously found in amphibians, squamate reptiles, and carnivores. We also identified species that are of high priority for the conservation of evolutionary distinctiveness.

| S-EPMC5167052 | 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