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Schmidt, D.

Publications and source records attributed to Schmidt, D..

3 recordsLinked to original sources

Domain Insertion Permissibility is a Measure of Engineerable Allostery in Ion Channels

Allostery is a fundamental principle of protein regulation that remains poorly understood and hard to engineer, in particular in ion channels. Here we use human Inward Rectifier K+ Channel Kir2.1 to establish domain insertion permissibility as a new experimental paradigm to identify engineerable allosteric sites. We find that permissibility is best explained by dynamic protein properties, such as conformational flexibility. Many allosterically regulated sites in Kir2.1 or sites equivalent to those regulated in homologs, such as G-protein-gated inward rectifier K+ channels (GIRK), have differential permissibility; that is, for these sites permissibility depends on the structural properties of the inserted domain. Our data and the well-established link between protein dynamics and allostery led us to propose that differential permissibility is a metric of both existing and latent allostery in Kir2.1. In support of this notion, inserting light-switchable domains into either existing or latent allosteric sites, but not elsewhere, renders Kir2.1 activity sensitive to light.

biochemistry

Ability of known susceptibility SNPs to predict colorectal cancer risk for persons with and without a family history

BackgroundA number of single nucleotide polymorphisms (SNPs), which are common inherited genetic variants, have been identified that are associated with risk of colorectal cancer. The aim of this study was to determine the ability of these SNPs to estimate colorectal cancer (CRC) risk for persons with and without a family history of CRC, and the screening implications.\n\nMethodsWe estimated the association with CRC of a 45 SNP-based risk using 1,181 cases and 999 controls, and its correlation (r) with CRC risk predicted from detailed family history. We estimated the predicted change in the distribution across predefined risk categories, and implications for recommended age to commence screening, from adding SNP-based risk to family history.\n\nResultsThe inter-quintile risk ratio for colorectal cancer risk of the SNP-based risk was 2.46 (95% CI 1.91 - 3.11). SNP-based and family history-based risks were not correlated (r = 0.02). For persons with no first-degree relatives with CRC, recommended screening would commence 2 years earlier for women (4 years for men) in the highest quintile of SNP-based risk, and 12 years later for women (7 years for men) in the lowest quintile. For persons with two first-degree relatives with CRC, recommended screening would commence 15 years earlier for men and women in the highest quintile, and 8 years earlier for men and women in the lowest quintile.\n\nConclusionsRisk reclassification by 45 SNPs could inform targeted screening for CRC prevention, particularly in clinical genetics settings when mutations in high-risk genes cannot be identified.

epidemiology

Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy

Fluorescence microscopy is a key driver of discoveries in the life-sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how image restoration based on deep learning extends the range of biological phenomena observable by microscopy. On seven concrete examples we demonstrate how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how near isotropic resolution can be achieved with up to 10-fold under-sampling along the axial direction, and how tubular and granular structures smaller than the diffraction limit can be resolved at 20-times higher frame-rates compared to state-of-the-art methods. All developed image restoration methods are freely available as open source software in Python, FO_SCPLOWIJIC_SCPLOW, and KO_SCPLOWNIMEC_SCPLOW.

bioinformatics