Biology subjects

Meiler, J.

Publications and source records attributed to Meiler, J..

4 recordsLinked to original sources

bioRxiv2018

Protein-Ligand Docking with Protein-based and Ligand-based Structure Activity Relationships

Protein-small molecule docking programs predict the interaction interface and energy between a given protein target and a small molecule ligand. The accuracy of docking predictions generally improve with the guidance of experimentally derived restraints. One available source of such restraints is structure-activity relationships (SARs). SARs provide information on changes in binding affinity or biological response corresponding to a chemical change in the protein and/or ligand. These chemical changes frequently refer to amino acid mutations on the protein side and functional group modifications on the ligand side. Theoretically, predicted interaction energies should correlate with SARs though in practice, this is challenging due to the difficulties in scoring protein-ligand interactions. We have previously developed RosettaLigandEnsemble (RLE), a protein-ligand docking method that simultaneously docks a congeneric ligand series to a single protein target. RLE is capable of identifying native-like binding modes for a ligand series that match the available ligand SARs. This work in progress reports on the extension of RLE to factor in SARs derived from protein mutagenesis data. The new method, ProteinLigEnsemble (PLE), is also part of the Rosetta Biomolecular Modeling Suite available at https://www.rosettacommons.org/. We have also developed protein ensemble docking features that allow for docking or screening against multiple receptor variants at the same time. We have included a proof of concept study and a tutorial for interested users.

biophysics↗

bioRxiv2017

Mechanisms of KCNQ1 Channel Dysfunction in Long QT Syndrome Involving Voltage Sensor Domain Mutations

Loss-of-function (LOF) mutations in human KCNQ1 are responsible for susceptibility to a life-threatening heart rhythm disorder, the congenital long-QT syndrome (LQTS). Hundreds of KCNQ1 mutations have been identified, but the molecular mechanisms responsible for impaired function are poorly understood. Here, we investigated the impact of 51 KCNQ1 variants located within the voltage sensor domain (VSD), with an emphasis on elucidating effects on cell surface expression, protein folding and structure. For each variant, the efficiency of trafficking to the plasma membrane, the impact of proteasome inhibition, and protein stability were assayed. The results of these experiments, combined with channel functional data, provided the basis for classifying each mutation into one of 6 mechanistic categories. More than half of the KCNQ1 LOF mutations destabilize the structure of the VSD, resulting in mistrafficking and degradation by the proteasome, an observation that underscores the growing appreciation that mutation-induced destabilization of membrane proteins may be a common human disease mechanism. Finally, we observed that 5 of the folding-defective LQTS mutants are located in the VSD S0 helix, where they interact with a number of other LOF mutation sites in other segments of the VSD. These observations reveal a critical role for the S0 helix as a central scaffold to help organize and stabilize the KCNQ1 VSD and, most likely, the corresponding domain of many other ion channels.\n\nOne Sentence SummaryLong QT syndrome-associated mutations in KCNQ1 most often destabilize the protein, leading to mistrafficking and degradation.

biochemistry↗

bioRxiv2017

High Throughput Functional Evaluation of KCNQ1 Decrypts Variants of Unknown Significance

BackgroundThe explosive growth in known human gene variation presents enormous challenges to current approaches for variant classification that impact diagnosis and treatment of many genetic diseases. For disorders caused by mutations in cardiac ion channels, such as congenital long-QT syndrome (LQTS), in vitro electrophysiological evidence has high value in discriminating pathogenic from benign variants, but these data are often lacking because assays are cost-, time- and labor-intensive.\n\nMethods and ResultsWe implemented a strategy for performing high throughput, functional evaluations of ion channel variants that repurposed an automated electrophysiology platform developed previously for drug discovery. We demonstrated success of this approach by evaluating 78 variants in KCNQ1, a major LQTS gene. We benchmarked our results with traditional electrophysiological approaches and observed a high level of concordance. Our results provided functional data useful for classifying ~70% of previously unstudied KCNQ1 variants annotated with uninformative descriptions in the public database ClinVar. Further, we show that rare and ultra-rare KCNQ1 variants in the general population exhibit functional properties ranging from normal to severe loss-of-function indicating that allele frequency is not a reliable predictor of channel function.\n\nConclusionsOur results illustrate an efficient and high throughput paradigm linking genotype to function for a human cardiac channelopathy that will enable data-driven classification of large numbers of variants and create new opportunities for precision medicine.

genetics↗

bioRxiv2017

Comprehensive Analysis of Constraint on the Spatial Distribution of Missense Variants in Human Protein Structures

The spatial distribution of genetic variation within proteins is shaped by evolutionary constraint and thus can provide insights into the functional importance of protein regions and the potential pathogenicity of protein alterations. Here, we comprehensively evaluate the 3D spatial patterns of constraint on human germline and somatic variation in 4,568 solved protein structures. Different classes of coding variants have significantly different spatial distributions. Neutral missense variants exhibit a range of 3D constraint patterns, with a general trend of spatial dispersion driven by constraint on core residues. In contrast, germline and somatic disease-causing variants are significantly more likely to be clustered in protein structure space. We demonstrate that this difference in the spatial distributions of disease-associated and benign germline variants provides a signature for accurately classifying variants of unknown significance (VUS) that is complementary to current approaches for VUS classification. We further illustrate the clinical utility of our approach by classifying new mutations identified from patients with familial idiopathic pneumonia (FIP) that segregate with disease.

genetics↗