In silico analysis of TRPM4 variants of unknown clinical significance.

BACKGROUND: TRPM4 is a calcium-activated channel that selectively permeates monovalent cations. Genetic variants of the channel in cardiomyocytes are associated with various heart disorders, such as progressive familial heart block and Brugada syndrome. About97% of all known TRPM4 missense variants are classified as variants of unknown clinical significance (VUSs). The very large number of VUSs is a serious problem in diagnostics and treatment of inherited heart diseases. METHODS AND RESULTS: We collected 233 benign or pathogenic missense variants in the superfamily of TRP channels from databases ClinVar, Humsavar and Ensembl Variation to compare performance of 22 algorithms that predict damaging variants. We found that ClinPred is the best-performing tool for TRP channels. We also used the paralogue annotation method to identify disease variants across the TRP family. In the set of 565 VUSs of hTRPM4, ClinPred predicted pathogenicity of 299 variants. Among these, 12 variants are also categorized as LP/P variants in at least one paralogue of hTRPM4. We further used the cryo-EM structure of hTRPM4 to find scores of contact pairs between parental (wild type) residues of VUSs for which ClinPred predicts a high probability of pathogenicity of variants for both contact partners. We propose that 68 respective missense VUSs are also likely pathogenic variants. CONCLUSIONS: ClinPred outperformed other in-silico tools in predicting damaging variants of TRP channels. ClinPred, the paralogue annotation method, and analysis of residue contacts the hTRPM4 cryo-EM structure collectively suggest pathogenicity of 80 TRPM4 VUSs.

L-Type Calcium Channel: Predicting Pathogenic/Likely Pathogenic Status for Variants of Uncertain Clinical Significance.

(1) Background: Defects in gene CACNA1C, which encodes the pore-forming subunit of the human Cav1.2 channel (hCav1.2), are associated with cardiac disorders such as atrial fibrillation, long QT syndrome, conduction disorders, cardiomyopathies, and congenital heart defects. Clinical manifestations are known only for 12% of CACNA1C missense variants, which are listed in public databases. Bioinformatics approaches can be used to predict the pathogenic/likely pathogenic status for variants of uncertain clinical significance. Choosing a bioinformatics tool and pathogenicity threshold that are optimal for specific protein families increases the reliability of such predictions. (2) Methods and Results: We used databases ClinVar, Humsavar, gnomAD, and Ensembl to compose a dataset of pathogenic/likely pathogenic and benign variants of hCav1.2 and its 20 paralogues: voltage-gated sodium and calcium channels. We further tested the performance of sixteen in silico tools in predicting pathogenic variants. ClinPred demonstrated the best performance, followed by REVEL and MCap. In the subset of 309 uncharacterized variants of hCav1.2, ClinPred predicted the pathogenicity for 188 variants. Among these, 36 variants were also categorized as pathogenic/likely pathogenic in at least one paralogue of hCav1.2. (3) Conclusions: The bioinformatics tool ClinPred and the paralogue annotation method consensually predicted the pathogenic/likely pathogenic status for 36 uncharacterized variants of hCav1.2. An analogous approach can be used to classify missense variants of other calcium channels and novel variants of hCav1.2.

Predicting novel disease mutations in the cardiac sodium channel.

BACKGROUND: Voltage-gated sodium channels Nav1.x mediate the rising phase of action potential in excitable cells. Variations in gene SCN5A, which encodes the hNav1.5 channel, are associated with arrhythmias and other heart diseases. About 1,400 SCN5A variants are listed in public databases, but for more than 30% of these the clinical significance is unknown and can currently only be derived by bioinformatics approaches. METHODS AND RESULTS: We used the ClinVar, SwissVar, Humsavar, gnomAD, and Ensembl databases to assemble a dataset of 1392 hNav1.5 variants (370 pathogenic variants, 602 benign variants and 420 variants of uncertain significance) as well as a dataset of 1766 damaging variants in 20 human sodium and calcium channel paralogs. Twelve in silico tools were tested for their ability to predict damaging mutations in hNav1.5. The best performing tool, MutPred, correctly predicted 93% of damaging variants in our hNav1.5 dataset. Among the 86 hNav1.5 variants for which electrophysiological data are also available, MutPred correctly predicted 82% of damaging variants. In the subset of 420 uncharacterized hNav1.5 variants MutPred predicted 196 new pathogenic variants. Among these, 74 variants are also annotated as damaging in at least one hNav1.5 paralog. CONCLUSIONS: Using a combination of sequence-based bioinformatics techniques and paralogous annotation we have substantially expanded the knowledge on disease variants in the cardiac sodium channel and assigned a pathogenic status to a number of mutations that so far have been described as variants of uncertain significance. A list of reclassified hNav1.5 variants and their properties is provided.

The interaction of amino acids, peptides, and proteins with DNA.

Amino acids that carry charges on their side groups can bind to double stranded DNA (dsDNA) and change the strength of the double helix. Measurement of the DNA melting temperature (Tm) confirmed that acidic amino acids (Glu, Asp) weaken the H-bonds between DNA strands, whereas basic amino acids (Arg, Lys) strengthen the interaction between the strands. A rank correlation exists between the amino acid isoelectric points and the observed changes in Tm. A similar dependence of the hyperchromic effect on the isoelectric point of a protein (pepsin, insulin, cortexin, and protamine) was observed for DNA-protein complexes at room temperature. Short peptides (KE, AEDG, and KEDP) containing a mixture of acidic and basic amino acid residues also affect Tm and the stability of the double helix. A model for binding Glu and Lys to dsDNA was explored by a docking simulation. The model shows that Glu, in an untwisted shape, binds to dsDNA in its major groove and disrupts three H-bonds between the strands, thereby destabilizing the double helix. Lys, in an untwisted shape, binds to the external side of the dsDNA and forms two bonds with O atoms of neighboring phosphodiester groups, thereby strengthening the DNA helix.