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Greisman, J. B.

Publications and source records attributed to Greisman, J. B..

2 recordsLinked to original sources

Native SAD Phasing at Room Temperature

AO_SCPLOWBSTRACTC_SCPLOWSingle-wavelength anomalous diffraction (SAD) is a routine method for overcoming the phase problem when solving a new macromolecular structure. This technique requires the accurate measurement of intensities to sensitively determine differences across Bijvoet pairs, making it a stringent test for the reliability of a data collection method. Although SAD experiments are commonly conducted at cryogenic temperatures to mitigate the effects of radiation damage, such temperatures can alter the conformational ensemble of the protein crystal and may impede the merging of data from multiple crystals due to non-uniform freezing. Here, we propose a data collection strategy to obtain high-quality data from room temperature samples. To illustrate the strengths of this approach we use native SAD phasing at 6.5 keV to solve four structures of three model systems at 295 K. The resulting datasets allow for automatic phasing and model building, and exhibit alternate conformations that are well-supported by the electron density. The high-redundancy data collection method demonstrated here enables the routine collection of high-quality, room-temperature diffraction to improve the study of protein conformational ensembles.

biophysics↗

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↗