Pose Estimation, Temperature, Noise, and Biological Temperature Sensors via Cryo-EM

Explore a comprehensive lecture on cryo-electron microscopy (cryo-EM) and its applications in understanding biological temperature sensors, particularly TRP channels. Delve into advanced topics such as pose estimation using quaternions, noise analysis, and methods for resolving continuous heterogeneity in single-particle cryo-EM. Gain insights into the challenges of structural characterization of TRP channels and the potential implications for understanding their temperature-sensing properties. Examine the use of machine learning in particle orientation estimation and the application of manifold embedding techniques to analyze conformational distributions. Learn about the DRAM approach for 3D pose estimation and its significance in improving cryo-EM analysis. Discover how these advancements in cryo-EM techniques contribute to the broader field of structural biology and their potential impact on understanding complex biological systems.

Intro
What is a biological temperature sensor?
A THERMODYNAMIC FRAMEWORK FOR TRP CHANNEL TEMPERATURE SENSING
3D point cloud matching vs. 3D point cloud to 2D projection matching
Why quaternions?
A discontinuity when going from rotation matrix to quaternion space!
Part II: Analytical solution to the 3D pose estimation problem.
The Determinant Ratio Matrix (DRAM) Approach
Summary: Improved framework for quaternion pose estimation.
An interlude about noise.
Cryo-EM allows access to a conformational distribution.
A quick introduction to Manifold Embedding.
Belief propagation in Manifold Embedding.
Manifold Embedding: Apo TRPV1 dataset
An apoferritin for heterogeneity?