Altered spin state equilibrium in the T309V mutant of cytochrome P450 2D6: a spectroscopic and computational study

Cytochrome P450 2D6 (CYP2D6) is one of the most important cytochromes P450 in humans. Resonance Raman data from the T309V mutant of CYP2D6 show that the substitution of the conserved I-helix threonine situated in the enzyme’s active site perturbs the heme spin equilibrium in favor of the six-coordinated low-spin species. A mechanistic hypothesis is introduced to explain the experimental observations, and its compatibility with the available structural and spectroscopic data is tested using quantum-mechanical density functional theory calculations on active-site models for both the CYP2D6 wild type and the T309V mutant

Scalable uncertainty for computer vision with functional variational inference

As Deep Learning continues to yield successful applications in Computer Vision, the ability to quantify all forms of uncertainty is a paramount requirement for its safe and reliable deployment in the real-world. In this work, we leverage the formulation of variational inference in function space, where we associate Gaussian Processes (GPs) to both Bayesian CNN priors and variational family. Since GPs are fully determined by their mean and covariance functions, we are able to obtain predictive uncertainty estimates at the cost of a single forward pass through any chosen CNN architecture and for any supervised learning task. By leveraging the structure of the induced covariance matrices, we propose numerically efficient algorithms which enable fast training in the context of high-dimensional tasks such as depth estimation and semantic segmentation. Additionally, we provide sufficient conditions for constructing regression loss functions whose probabilistic counterparts are compatible with aleatoric uncertainty quantification