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Title: The hidden treasure in your data: phasing with unexpected weak anomalous scatterers from routine data sets

Abstract

Single-wavelength anomalous dispersion (SAD) utilizing anomalous signal from native S atoms, or other atoms withZ≤ 20, generally requires highly redundant data collected using relatively long-wavelength X-rays. Here, the results from two proteins are presented where the anomalous signal from serendipitously acquired surface-bound Ca atoms with an anomalous data multiplicity of around 10 was utilized to drivede novostructure determination. In both cases, the Ca atoms were acquired from the crystallization solution, and the data-collection strategy was not optimized to exploit the anomalous signal from these scatterers. The X-ray data were collected at 0.98 Å wavelength in one case and at 1.74 Å in the other (the wavelength was optimized for sulfur, but the anomalous signal from calcium was exploited for structure solution). Similarly, using a test case, it is shown that data collected at ~1.0 Å wavelength, where thef'' value for sulfur is 0.28 e, are sufficient for structure determination using intrinsic S atoms from a strongly diffracting crystal. Interestingly, it was also observed thatSHELXDwas capable of generating a substructure solution from high-exposure data with a completeness of 70% for low-resolution reflections extending to 3.5 Å resolution with relatively low anomalous multiplicity. Considering the fact that many crystallization conditions contain anomalousmore » scatterers such as Cl, Ca, Mnetc., checking for the presence of fortuitous anomalous signal in data from well diffracting crystals could prove useful in either determining the structurede novoor in accurately assigning surface-bound atoms.« less

Authors:
; ; ; ; ; ORCiD logo
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1409626
Report Number(s):
BNL-114678-2017-JA¿¿¿
Journal ID: ISSN 2053-230X; ACSFEN
DOE Contract Number:
SC0012704
Resource Type:
Journal Article
Resource Relation:
Journal Name: Acta Crystallographica. Section F, Structural Biology Communications; Journal Volume: 73; Journal Issue: 4
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES

Citation Formats

Hegde, Raghurama P., Fedorov, Alexander A., Sauder, J. Michael, Burley, Stephen K., Almo, Steven C., and Ramagopal, Udupi A. The hidden treasure in your data: phasing with unexpected weak anomalous scatterers from routine data sets. United States: N. p., 2017. Web. doi:10.1107/S2053230X17002680.
Hegde, Raghurama P., Fedorov, Alexander A., Sauder, J. Michael, Burley, Stephen K., Almo, Steven C., & Ramagopal, Udupi A. The hidden treasure in your data: phasing with unexpected weak anomalous scatterers from routine data sets. United States. doi:10.1107/S2053230X17002680.
Hegde, Raghurama P., Fedorov, Alexander A., Sauder, J. Michael, Burley, Stephen K., Almo, Steven C., and Ramagopal, Udupi A. Wed . "The hidden treasure in your data: phasing with unexpected weak anomalous scatterers from routine data sets". United States. doi:10.1107/S2053230X17002680.
@article{osti_1409626,
title = {The hidden treasure in your data: phasing with unexpected weak anomalous scatterers from routine data sets},
author = {Hegde, Raghurama P. and Fedorov, Alexander A. and Sauder, J. Michael and Burley, Stephen K. and Almo, Steven C. and Ramagopal, Udupi A.},
abstractNote = {Single-wavelength anomalous dispersion (SAD) utilizing anomalous signal from native S atoms, or other atoms withZ≤ 20, generally requires highly redundant data collected using relatively long-wavelength X-rays. Here, the results from two proteins are presented where the anomalous signal from serendipitously acquired surface-bound Ca atoms with an anomalous data multiplicity of around 10 was utilized to drivede novostructure determination. In both cases, the Ca atoms were acquired from the crystallization solution, and the data-collection strategy was not optimized to exploit the anomalous signal from these scatterers. The X-ray data were collected at 0.98 Å wavelength in one case and at 1.74 Å in the other (the wavelength was optimized for sulfur, but the anomalous signal from calcium was exploited for structure solution). Similarly, using a test case, it is shown that data collected at ~1.0 Å wavelength, where thef'' value for sulfur is 0.28 e, are sufficient for structure determination using intrinsic S atoms from a strongly diffracting crystal. Interestingly, it was also observed thatSHELXDwas capable of generating a substructure solution from high-exposure data with a completeness of 70% for low-resolution reflections extending to 3.5 Å resolution with relatively low anomalous multiplicity. Considering the fact that many crystallization conditions contain anomalous scatterers such as Cl, Ca, Mnetc., checking for the presence of fortuitous anomalous signal in data from well diffracting crystals could prove useful in either determining the structurede novoor in accurately assigning surface-bound atoms.},
doi = {10.1107/S2053230X17002680},
journal = {Acta Crystallographica. Section F, Structural Biology Communications},
number = 4,
volume = 73,
place = {United States},
year = {Wed Mar 22 00:00:00 EDT 2017},
month = {Wed Mar 22 00:00:00 EDT 2017}
}
  • A key challenge in the SAD phasing method is solving a structure when the anomalous signal-to-noise ratio is low. Here, we describe algorithms and tools for evaluating and optimizing the useful anomalous correlation and the anomalous signal in a SAD experiment. A simple theoretical framework [Terwilliger et al.(2016),Acta Cryst.D72, 346–358] is used to develop methods for planning a SAD experiment, scaling SAD data sets and estimating the useful anomalous correlation and anomalous signal in a SAD data set. Thephenix.plan_sad_experimenttool uses a database of solved and unsolved SAD data sets and the expected characteristics of a SAD data set to estimatemore » the probability that the anomalous substructure will be found in the SAD experiment and the expected map quality that would be obtained if the substructure were found. Thephenix.scale_and_mergetool scales unmerged SAD data from one or more crystals using local scaling and optimizes the anomalous signal by identifying the systematic differences among data sets, and thephenix.anomalous_signaltool estimates the useful anomalous correlation and anomalous signal after collecting SAD data and estimates the probability that the data set can be solved and the likely figure of merit of phasing.« less
  • A key challenge in the SAD phasing method is solving a structure when the anomalous signal-to-noise ratio is low. Here, algorithms and tools for evaluating and optimizing the useful anomalous correlation and the anomalous signal in a SAD experiment are described. A simple theoretical framework [Terwilligeret al.(2016),Acta Cryst.D72, 346–358] is used to develop methods for planning a SAD experiment, scaling SAD data sets and estimating the useful anomalous correlation and anomalous signal in a SAD data set. Thephenix.plan_sad_experimenttool uses a database of solved and unsolved SAD data sets and the expected characteristics of a SAD data set to estimate themore » probability that the anomalous substructure will be found in the SAD experiment and the expected map quality that would be obtained if the substructure were found. Thephenix.scale_and_mergetool scales unmerged SAD data from one or more crystals using local scaling and optimizes the anomalous signal by identifying the systematic differences among data sets, and thephenix.anomalous_signaltool estimates the useful anomalous correlation and anomalous signal after collecting SAD data and estimates the probability that the data set can be solved and the likely figure of merit of phasing.« less
  • Highlights: ► US households are storing 84.1 million broken or obsolete (junk) TVs. ► They represent 2.12 million metric tons of scrap. ► The value of these materials is approximately $21 per TV. ► Our count models characterize US households who store junk TVs. ► Our results are useful for designing more effective TV recycling programs. - Abstract: Within the growing stockpile of electronic waste (e-waste), TVs are especially of concern in the US because of their number (which is known imprecisely), their low recycling rate, and their material content: cathode ray tube televisions contain lead, and both rear projectionmore » and flat panel displays contain mercury, in addition to other potentially toxic materials. Based on a unique dataset from a 2010 survey, our count models show that pro-environmental behavior, age, education, household size, marital status, gender of the head of household, dwelling type, and geographic location are statistically significant variables for explaining the number of broken or obsolete (junk) TVs stored by US households. We also estimate that they are storing approximately 84.1 million junk TVs, which represents 40 pounds of scrap per household. Materials in each of these junk TVs are worth $21 on average at January 2012 materials prices, which sets an upper bound on collecting and recycling costs. This information should be helpful for developing more effective recycling strategies for TVs in the e-waste stream.« less
  • No abstract prepared.