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Title: PyMDA: Microcrystal Data Assembly Using Python

Abstract

The recent developments at microdiffraction X-ray beamlines are making microcrystals of macromolecules appealing subjects for routine structural analysis. Microcrystal diffraction data collected at synchrotron microdiffraction beamlines may be radiation-damaged with incomplete data per microcrystal and with unit cell variations. We have previously devised a multi-stage data assembly method for microcrystal synchrotron crystallography. Here we have implemented the strategy as a Python program for microcrystal data assembly (PyMDA). PyMDA optimizes microcrystal data quality including weak anomalous signals through iterative crystal and frame rejections. Beyond microcrystals, PyMDA may be applicable for assembling data sets from larger crystals for improved data quality.

Authors:
 [1];  [2];  [1];  [3]; ORCiD logo [2]; ORCiD logo [2]
  1. Brookhaven National Lab. (BNL), Upton, NY (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States). National Synchrotron Light Source II (NSLS-II)
  3. Columbia Univ., New York, NY (United States)
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program; National Institutes of Health (NIH); USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC), Workforce Development for Teachers and Scientists (WDTS)
OSTI Identifier:
1617953
Alternate Identifier(s):
OSTI ID: 1579902; OSTI ID: 1777433
Report Number(s):
BNL-221258-2021-JAAM; BNL-212432-2019-JAAM
Journal ID: ISSN 1600-5767
Grant/Contract Number:  
SC0012704; GM107462; P30GM133893; KP1607010
Resource Type:
Published Article
Journal Name:
Journal of Applied Crystallography (Online)
Additional Journal Information:
Journal Name: Journal of Applied Crystallography (Online); Journal Volume: 53; Journal Issue: 1; Journal ID: ISSN 1600-5767
Publisher:
International Union of Crystallography
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; X-ray crystallography; microcrystals; data assembly; radiation damage; multi-crystal; Python; 59 BASIC BIOLOGICAL SCIENCES; microcrystal; python

Citation Formats

Takemaru, Lina, Guo, Gongrui, Zhu, Ping, Hendrickson, Wayne A., McSweeney, Sean, and Liu, Qun. PyMDA: Microcrystal Data Assembly Using Python. United States: N. p., 2020. Web. doi:10.1107/s160057671901673x.
Takemaru, Lina, Guo, Gongrui, Zhu, Ping, Hendrickson, Wayne A., McSweeney, Sean, & Liu, Qun. PyMDA: Microcrystal Data Assembly Using Python. United States. https://doi.org/10.1107/s160057671901673x
Takemaru, Lina, Guo, Gongrui, Zhu, Ping, Hendrickson, Wayne A., McSweeney, Sean, and Liu, Qun. Sat . "PyMDA: Microcrystal Data Assembly Using Python". United States. https://doi.org/10.1107/s160057671901673x.
@article{osti_1617953,
title = {PyMDA: Microcrystal Data Assembly Using Python},
author = {Takemaru, Lina and Guo, Gongrui and Zhu, Ping and Hendrickson, Wayne A. and McSweeney, Sean and Liu, Qun},
abstractNote = {The recent developments at microdiffraction X-ray beamlines are making microcrystals of macromolecules appealing subjects for routine structural analysis. Microcrystal diffraction data collected at synchrotron microdiffraction beamlines may be radiation-damaged with incomplete data per microcrystal and with unit cell variations. We have previously devised a multi-stage data assembly method for microcrystal synchrotron crystallography. Here we have implemented the strategy as a Python program for microcrystal data assembly (PyMDA). PyMDA optimizes microcrystal data quality including weak anomalous signals through iterative crystal and frame rejections. Beyond microcrystals, PyMDA may be applicable for assembling data sets from larger crystals for improved data quality.},
doi = {10.1107/s160057671901673x},
journal = {Journal of Applied Crystallography (Online)},
number = 1,
volume = 53,
place = {United States},
year = {2020},
month = {2}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1107/s160057671901673x

Citation Metrics:
Cited by: 5 works
Citation information provided by
Web of Science

Figures / Tables:

Figure 1 Figure 1: Multi-step data assembly workflow. (a) Progressive processing of single-crystal data sets as accumulative wedges. (b) Classification based on unit-cell variations. (c) Data assembly for each qualified cluster (completeness > 90%). The data assembly procedure optimizes data quality by iterative crystal and frame rejections. PyMDA produces N optimized datamore » sets, each corresponding to a different set of unit-cell parameters.« less

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