Protein structure aids predicting functional perturbation of missense variants in SCN5A and KCNQ1.
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ABSTRACT:
SUBMITTER: Kroncke BM
PROVIDER: S-EPMC6383132 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Accurately predicting the impact of rare nonsynonymous variants on disease risk is an important goal in precision medicine. Variants in the cardiac sodium channel SCN5A (protein NaV1.5; voltage-dependent cardiac Na+ channel) are associated with multiple arrhythmia disorders, including Brugada syndrome and long QT syndrome. Rare SCN5A variants also occur in ?1% of unaffected individuals. We hypothesized that in vitro electrophysiological functional parameters explain a statistically significant portion of the variability in disease penetrance. METHODS:From a comprehensive literature review, we quantified the number of carriers presenting with and without disease for 1712 reported SCN5A variants. For 356 variants, data were also available for 5 NaV1.5 electrophysiological parameters: peak current, late/persistent current, steady-state V1/2 of activation and inactivation, and recovery from inactivation. RESULTS:We found that peak and late current significantly associate with Brugada syndrome (P<0.001; ?=-0.44; Spearman rank test) and long QT syndrome disease penetrance (P<0.001; ?=0.37). Steady-state V1/2 activation and recovery from inactivation associate significantly with Brugada syndrome and long QT syndrome penetrance, respectively. Continuous estimates of disease penetrance align with the current American College of Medical Genetics classification paradigm. CONCLUSIONS:NaV1.5 in vitro electrophysiological parameters are correlated with Brugada syndrome and long QT syndrome disease risk. Our data emphasize the value of in vitro electrophysiological characterization and incorporating counts of affected and unaffected carriers to aid variant classification. This quantitative analysis of the electrophysiological literature should aid the interpretation of NaV1.5 variant electrophysiological abnormalities and help improve NaV1.5 variant classification.
| S-EPMC5941942 | biostudies-literature
Project description:PurposeUp to 30% of patients with Brugada syndrome (BrS) carry loss-of-function (LoF) variants in the cardiac sodium channel gene SCN5A encoding for the protein NaV1.5. Recent studies suggested that NaV1.5 can dimerize, and some variants exert dominant negative effects. In this study, we sought to explore the generality of missense variant NaV1.5 dominant negative effects and their clinical severity.MethodsWe identified 35 LoF variants (<10% of wild type [WT] peak current) and 15 partial LoF variants (10%-50% of WT peak current) that we assessed for dominant negative effects. SCN5A variants were studied in HEK293T cells, alone or in heterozygous coexpression with WT SCN5A using automated patch clamp. To assess the clinical risk, we compared the prevalence of dominant negative vs putative haploinsufficient (frameshift, splice, or nonsense) variants in a BrS consortium and the Genome Aggregation Database population database.ResultsIn heterozygous expression with WT, 32 of 35 LoF and 6 of 15 partial LoF variants showed reduction to <75% of WT-alone peak current, showing a dominant negative effect. Individuals with dominant negative LoF variants had an elevated disease burden compared with the individuals with putative haploinsufficient variants (2.7-fold enrichment in BrS cases, P = .019).ConclusionMost SCN5A missense LoF variants exert a dominant negative effect. This class of variant confers an especially high burden of BrS.
| S-EPMC9262418 | biostudies-literature
Project description:BACKGROUND:An emerging standard-of-care for long-QT syndrome uses clinical genetic testing to identify genetic variants of the KCNQ1 potassium channel. However, interpreting results from genetic testing is confounded by the presence of variants of unknown significance for which there is inadequate evidence of pathogenicity. METHODS AND RESULTS:In this study, we curated from the literature a high-quality set of 107 functionally characterized KCNQ1 variants. Based on this data set, we completed a detailed quantitative analysis on the sequence conservation patterns of subdomains of KCNQ1 and the distribution of pathogenic variants therein. We found that conserved subdomains generally are critical for channel function and are enriched with dysfunctional variants. Using this experimentally validated data set, we trained a neural network, designated Q1VarPred, specifically for predicting the functional impact of KCNQ1 variants of unknown significance. The estimated predictive performance of Q1VarPred in terms of Matthew's correlation coefficient and area under the receiver operating characteristic curve were 0.581 and 0.884, respectively, superior to the performance of 8 previous methods tested in parallel. Q1VarPred is publicly available as a web server at http://meilerlab.org/q1varpred. CONCLUSIONS:Although a plethora of tools are available for making pathogenicity predictions over a genome-wide scale, previous tools fail to perform in a robust manner when applied to KCNQ1. The contrasting and favorable results for Q1VarPred suggest a promising approach, where a machine-learning algorithm is tailored to a specific protein target and trained with a functionally validated data set to calibrate informatics tools.
| S-EPMC5679743 | biostudies-literature
Project description:Recent advances in experimental and computational protein structure determination have provided access to high-quality structures for most human proteins and mutants thereof. However, linking changes in structure in protein mutants to functional impact remains an active area of method development. If successful, such methods can ultimately assist physicians in taking appropriate treatment decisions. This work presents three artificial neural network (ANN)-based predictive models that classify four key functional parameters of KCNQ1 variants as normal or dysfunctional using PSSM-based evolutionary and/or biophysical descriptors. Recent advances in predicting protein structure and variant properties with artificial intelligence (AI) rely heavily on the availability of evolutionary features and thus fail to directly assess the biophysical underpinnings of a change in structure and/or function. The central goal of this work was to develop an ANN model based on structure and physiochemical properties of KCNQ1 potassium channels that performs comparably or better than algorithms using only on PSSM-based evolutionary features. These biophysical features highlight the structure-function relationships that govern protein stability, function, and regulation. The input sensitivity algorithm incorporates the roles of hydrophobicity, polarizability, and functional densities on key functional parameters of the KCNQ1 channel. Inclusion of the biophysical features outperforms exclusive use of PSSM-based evolutionary features in predicting activation voltage dependence and deactivation time. As AI is increasingly applied to problems in biology, biophysical understanding will be critical with respect to 'explainable AI', i.e., understanding the relation of sequence, structure, and function of proteins. Our model is available at www.kcnq1predict.org.
| S-EPMC9060377 | biostudies-literature
Project description:Mutations in SCN5A can alter the cardiac sodium current INa and increase the risk of potentially lethal conditions such as Brugada and long-QT syndromes. The relation between mutations and their clinical phenotypes is complex, and systems to predict clinical severity of unclassified SCN5A variants perform poorly. We investigated if instead we could predict changes to INa, leaving the link from INa to clinical phenotype for mechanistic simulation studies. An exhaustive list of nonsynonymous missense mutations and resulting changes to INa was compiled. We then applied machine-learning methods to this dataset, and found that changes to INa could be predicted with higher sensitivity and specificity than most existing predictors of clinical significance. The substituted residues' location on the protein correlated with channel function and strongly contributed to predictions, while conservedness and physico-chemical properties did not. However, predictions were not sufficiently accurate to form a basis for mechanistic studies. These results show that changes to INa, the mechanism through which SCN5A mutations create cardiac risk, are already difficult to predict using purely in-silico methods. This partly explains the limited success of systems to predict clinical significance of SCN5A variants, and underscores the need for functional studies of INa in risk assessment.
| S-EPMC6109095 | biostudies-literature
Project description:Accurate prediction of damaging missense variants is critically important for interpreting a genome sequence. Although many methods have been developed, their performance has been limited. Recent advances in machine learning and the availability of large-scale population genomic sequencing data provide new opportunities to considerably improve computational predictions. Here we describe the graphical missense variant pathogenicity predictor (gMVP), a new method based on graph attention neural networks. Its main component is a graph with nodes that capture predictive features of amino acids and edges weighted by co-evolution strength, enabling effective pooling of information from the local protein context and functionally correlated distal positions. Evaluation of deep mutational scan data shows that gMVP outperforms other published methods in identifying damaging variants in TP53, PTEN, BRCA1 and MSH2. Furthermore, it achieves the best separation of de novo missense variants in neuro developmental disorder cases from those in controls. Finally, the model supports transfer learning to optimize gain- and loss-of-function predictions in sodium and calcium channels. In summary, we demonstrate that gMVP can improve interpretation of missense variants in clinical testing and genetic studies.
| S-EPMC10361701 | biostudies-literature
Project description:Exome sequencing in diabetes presents a diagnostic challenge because depending on frequency, functional impact, and genomic and environmental contexts, HNF1A variants can cause maturity-onset diabetes of the young (MODY), increase type 2 diabetes risk, or be benign. A correct diagnosis matters as it informs on treatment, progression, and family risk. We describe a multi-dimensional functional dataset of 73 HNF1A missense variants identified in exomes of 12,940 individuals. Our aim was to develop an analytical framework for stratifying variants along the HNF1A phenotypic continuum to facilitate diagnostic interpretation. HNF1A variant function was determined by four different molecular assays. Structure of the multi-dimensional dataset was explored using principal component analysis, k-means, and hierarchical clustering. Weights for tissue-specific isoform expression and functional domain were integrated. Functionally annotated variant subgroups were used to re-evaluate genetic diagnoses in national MODY diagnostic registries. HNF1A variants demonstrated a range of behaviors across the assays. The structure of the multi-parametric data was shaped primarily by transactivation. Using unsupervised learning methods, we obtained high-resolution functional clusters of the variants that separated known causal MODY variants from benign and type 2 diabetes risk variants and led to reclassification of 4% and 9% of HNF1A variants identified in the UK and Norway MODY diagnostic registries, respectively. Our proof-of-principle analyses facilitated informative stratification of HNF1A variants along the continuum, allowing improved evaluation of clinical significance, management, and precision medicine in diabetes clinics. Transcriptional activity appears a superior readout supporting pursuit of transactivation-centric experimental designs for high-throughput functional screens.
| S-EPMC7536579 | biostudies-literature
Project description:PurposeBiallelic pathogenic variants in the gene encoding the ATP-binding cassette transporter ABCA4 are the leading cause of irreversible vision loss in inherited retinal dystrophies (IRDs). Interpretation of ABCA4 variants is challenging, due to cis-modifying and hypomorphic variants. We have previously detected 10 missense variants of unknown significance (VUS) in patients with suspected ABCA4-retinal dystrophies (ABCA4-RDs) in Norway. In this study, we functionally characterized the VUS to aid interpretation of the variants and to determine if they are associated with the disease.MethodsThe ABCA4 VUS were expressed in HEK293T cells and the ABCA4 expression level and ATPase activity were determined and correlated with the patients' phenotype. The functional data further used for reclassification of the VUS following the American College of Medical Genetics and Genomics (ACMG) guidelines.ResultsOf the 10 VUSs, 2 variants, Cys205Phe and Asn415Thr, were categorized as functionally severe. The age at presentation in the 2 patients carrying these variants was divergent and seemed to be driven by the patients' second pathogenic variants Gly1961Glu and c.5461-10T>C, respectively. Three variants, Val643Gly, Pro799Leu, and Val1433Ile were categorized as functionally moderate, and were found in patients with intermediate/late age at presentation. The remaining five variants were categorized as functionally normal/mild. Based on our data, c.614G>T p.(Cys205Phe), c.1244A>C p.(Asn415Thr), and c.2396C>T p.(Pro799Leu) were reclassified to (likely) pathogenic, while 4 of the functionally normal/mild variants could be reclassified to likely benign.ConclusionsFunctional analyses of ABCA4 variants are a helpful tool in variant classification and enable us to better predict the disease severity in patients with ABCA4-RDs.
| S-EPMC11305421 | biostudies-literature
Project description:The elucidation of the complex relationships linking genotypic and phenotypic variations to protein structure is a major challenge in the post-genomic era. We present MSV3d (Database of human MisSense Variants mapped to 3D protein structure), a new database that contains detailed annotation of missense variants of all human proteins (20 199 proteins). The multi-level characterization includes details of the physico-chemical changes induced by amino acid modification, as well as information related to the conservation of the mutated residue and its position relative to functional features in the available or predicted 3D model. Major releases of the database are automatically generated and updated regularly in line with the dbSNP (database of Single Nucleotide Polymorphism) and SwissVar releases, by exploiting the extensive Décrypthon computational grid resources. The database (http://decrypthon.igbmc.fr/msv3d) is easily accessible through a simple web interface coupled to a powerful query engine and a standard web service. The content is completely or partially downloadable in XML or flat file formats. Database URL: http://decrypthon.igbmc.fr/msv3d.
| S-EPMC3317913 | biostudies-literature
Project description:BackgroundVariants in genes encoding voltage-gated potassium channels are associated with a broad spectrum of neurological diseases including epilepsy, ataxia, and intellectual disability. Knowledge of the resulting functional changes, characterized as overall ion channel gain- or loss-of-function, is essential to guide clinical management including precision medicine therapies. However, for an increasing number of variants, little to no experimental data is available. New tools are needed to evaluate variant functional effects.MethodsWe catalogued a comprehensive dataset of 959 functional experiments across 19 voltage-gated potassium channels, leveraging data from 782 unique disease-associated and synthetic variants. We used these data to train a taxonomy-based multi-task learning support vector machine (MTL-SVM), and compared performance to several baseline methods.FindingsMTL-SVM maintains channel family structure during model training, improving overall predictive performance (mean balanced accuracy 0·718 ± 0·041, AU-ROC 0·761 ± 0·063) over baseline (mean balanced accuracy 0·620 ± 0·045, AU-ROC 0·711 ± 0·022). We can obtain meaningful predictions even for channels with few known variants (KCNC1, KCNQ5).InterpretationOur model enables functional variant prediction for voltage-gated potassium channels. It may assist in tailoring current and future precision therapies for the increasing number of patients with ion channel disorders.FundingThis work was supported by intramural funding of the Medical Faculty, University of Tuebingen (PATE F.1315137.1), the Federal Ministry for Education and Research (Treat-ION, 01GM1907A/B/G/H) and the German Research Foundation (FOR-2715, Le1030/16-2, He8155/1-2).
| S-EPMC9250003 | biostudies-literature