Biology subjects

Hekstra, D. R.

Publications and source records attributed to Hekstra, D. R..

2 recordsLinked to original sources

bioRxiv2021

Reciprocalspaceship: A Python Library for Crystallographic Data Analysis

AO_SCPLOWBSTRACTC_SCPLOWX-ray crystallography is an invaluable technique for studying the atomic structure of macromolecules. Much of crystallographys success is due to the software packages developed to enable the automated processing of diffraction data. However, the analysis of unconventional diffraction experiments can still pose significant challenges--many existing programs are closed-source, sparsely documented, or are challenging to integrate with modern libraries for scientific computing and machine learning. Here we describe reciprocalspaceship, a Python library for exploring reciprocal space. It provides a tabular representation for reflection data from diffraction experiments that extends the widely-used pandas library with built-in methods for handling space group, unit cell, and symmetry-based operations. As we illustrate, this library facilitates new modes of exploratory data analysis while supporting the prototyping, development, and release of new methods.

biophysics↗

bioRxiv2021

Careless: A Variational Bayesian Model for Merging X-ray Diffraction Data

AO_SCPLOWBSTRACTC_SCPLOWNovel X-ray methods are transforming the study of the functional dynamics of biomolecules. Key to this revolution is detection of often subtle conformational changes from diffraction data. Diffraction data contain patterns of bright spots known as reflections. To compute the electron density of a molecule, the intensity of each reflection must be estimated, and redundant observations reduced to consensus intensities. Systematic effects, however, lead to the measurement of equivalent reflections on different scales, corrupting observation of changes in electron density. Here, we present a modern Bayesian solution to this problem, which uses deep learning and variational inference to simultaneously rescale and merge reflection observations. We successfully apply this method to monochromatic and polychromatic single-crystal diffraction data, as well as serial femtosecond crystallography data. We find that this approach is applicable to the analysis of many types of diffraction experiments, while accurately and sensitively detecting subtle dynamics and anomalous scattering.

biophysics↗