B0414
Title: Principal nested shape spaces, with applications to molecular dynamics data
Authors: Ian Dryden - University of Nottingham (United Kingdom) [presenting]
Abstract: Molecular dynamics simulations produce large datasets of temporal sequences of molecules, such as flexible proteins. It is of interest to summarize the shape evolution of the molecules in a succinct, low-dimensional representation. However, Euclidean techniques such as principal components analysis (PCA) can be problematic as the data may lie far from a flat manifold. Principal nested spheres can lead to striking insights which may be missed using PCA. We provide some fast fitting algorithms and apply the methodology to a large set of 100 runs of 3D protein simulations, investigating biochemical function in applications in Pharmaceutical Sciences.
