Machine Learning Methods for X-Ray Scattering Data Analysis from Biomacromolecular Solutions.

ABSTRACT: Small-angle x-ray scattering (SAXS) of biological macromolecules in solutions is a widely employed method in structural biology. SAXS patterns include information about the overall shape and low-resolution structure of dissolved particles. Here, we describe how to transform experimental SAXS patterns to feature vectors and how a simple k-nearest neighbor approach is able to retrieve information on overall particle shape and maximal diameter (D_max) as well as molecular mass directly from experimental scattering data. Based on this transformation, we develop a rapid multiclass shape-classification ranging from compact, extended, and flat categories to hollow and random-chain-like objects. This classification may be employed, e.g., as a decision block in automated data analysis pip

SUBMITTER: Franke D 

PROVIDER: S-EPMC6129182 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature