Limits...
Autoindexing with outlier rejection and identification of superimposed lattices.

Sauter NK, Poon BK - J Appl Crystallogr (2010)

Bottom Line: Constructing a model lattice to fit the observed Bragg diffraction pattern is straightforward for perfect samples, but indexing can be challenging when artifacts are present, such as poorly shaped spots, split crystals giving multiple closely aligned lattices and outright superposition of patterns from aggregated microcrystals.Outliers are identified by assuming a Gaussian error distribution for the best-fitting spots and points diverging from this distribution are culled.The prevalence of outliers provides a potentially useful measure of sample quality.

View Article: PubMed Central - HTML - PubMed

Affiliation: Physical Biosciences Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA.

ABSTRACT
Constructing a model lattice to fit the observed Bragg diffraction pattern is straightforward for perfect samples, but indexing can be challenging when artifacts are present, such as poorly shaped spots, split crystals giving multiple closely aligned lattices and outright superposition of patterns from aggregated microcrystals. To optimize the lattice model against marginal data, refinement can be performed using a subset of the observations from which the poorly fitting spots have been discarded. Outliers are identified by assuming a Gaussian error distribution for the best-fitting spots and points diverging from this distribution are culled. The set of remaining observations produces a superior lattice model, while the rejected observations can be used to identify a second crystal lattice, if one is present. The prevalence of outliers provides a potentially useful measure of sample quality. The described procedures are implemented for macromolecular crystallography within the autoindexing program labelit.index (http://cci.lbl.gov/labelit).

No MeSH data available.


Related in: MedlinePlus

Detail of the 1° rotation image used for analyzing outliers for Protein Data Bank (PDB) entry 1vk8. (a) Red circles are the candidate Bragg spots accepted for the initial lattice refinement, after two rounds of heuristic spot filters. Spot intensity is one criterion for accepting these candidate observations, but other characteristics have disqualifed many of the brightest Bragg spots in this case. Specifically, the heuristic rules select signals for which the centroids are extremely well measured, namely round sharp spot profiles that are baseline-separated from neighboring signals. Thus, the Bragg signals exhibiting satellite spots and streaks oriented along the vertical c*-axis direction are excluded from refinement. (b) Yellow boxes show the predicted Bragg positions on the initially refined lattice model. Observations have been recolored to indicate their status with respect to this predicted lattice. Red dots represent the 40% of spots closest to their predicted positions, used for determining the Rayleigh distribution σ in Fig. 3 ▶(c). Pink spots are those determined to be outliers by the statistical test [equation (6)], very much in agreement with the visual impression. Orange dots are the remaining observations.
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fig2: Detail of the 1° rotation image used for analyzing outliers for Protein Data Bank (PDB) entry 1vk8. (a) Red circles are the candidate Bragg spots accepted for the initial lattice refinement, after two rounds of heuristic spot filters. Spot intensity is one criterion for accepting these candidate observations, but other characteristics have disqualifed many of the brightest Bragg spots in this case. Specifically, the heuristic rules select signals for which the centroids are extremely well measured, namely round sharp spot profiles that are baseline-separated from neighboring signals. Thus, the Bragg signals exhibiting satellite spots and streaks oriented along the vertical c*-axis direction are excluded from refinement. (b) Yellow boxes show the predicted Bragg positions on the initially refined lattice model. Observations have been recolored to indicate their status with respect to this predicted lattice. Red dots represent the 40% of spots closest to their predicted positions, used for determining the Rayleigh distribution σ in Fig. 3 ▶(c). Pink spots are those determined to be outliers by the statistical test [equation (6)], very much in agreement with the visual impression. Orange dots are the remaining observations.

Mentions: This paper focuses only on what is achievable with the observed center-of-mass spot positions from the best-measured spots (Fig. 2 ▶ a). There is good reason to emphasize this initial characterization of raw images (prior to data reduction), since high-throughput crystal screening is relied upon increasingly to identify the best crystalline samples prior to the collection of full data sets. Crystal screening, which is now a standard option at many synchroton beamlines (Soltis et al., 2008 ▶), examines numerous samples sequentially under robotic control. A typical protocol involves the collection of two diffraction patterns spaced 90° apart on the y-rotational axis, which is enough data to gain a general understanding of the quality of the sample. Software server frameworks such as Web-Ice (González et al., 2008 ▶) or EDNA (Incardona et al., 2009 ▶) execute autoindexing trials for each crystal, generating a summary report that lists characteristics such as the signal strength and limiting resolution. One important quality measure is the r.m.s. deviation (in laboratory space) between the observed and predicted positions of the best-measured spots. Even in the absence of post-refinement, the lattice model ought to predict spot positions with sub-pixel accuracy, so in the best cases the r.m.s. spot displacement is expected to be less than the CCD pixel size, typically about 100 µm.


Autoindexing with outlier rejection and identification of superimposed lattices.

Sauter NK, Poon BK - J Appl Crystallogr (2010)

Detail of the 1° rotation image used for analyzing outliers for Protein Data Bank (PDB) entry 1vk8. (a) Red circles are the candidate Bragg spots accepted for the initial lattice refinement, after two rounds of heuristic spot filters. Spot intensity is one criterion for accepting these candidate observations, but other characteristics have disqualifed many of the brightest Bragg spots in this case. Specifically, the heuristic rules select signals for which the centroids are extremely well measured, namely round sharp spot profiles that are baseline-separated from neighboring signals. Thus, the Bragg signals exhibiting satellite spots and streaks oriented along the vertical c*-axis direction are excluded from refinement. (b) Yellow boxes show the predicted Bragg positions on the initially refined lattice model. Observations have been recolored to indicate their status with respect to this predicted lattice. Red dots represent the 40% of spots closest to their predicted positions, used for determining the Rayleigh distribution σ in Fig. 3 ▶(c). Pink spots are those determined to be outliers by the statistical test [equation (6)], very much in agreement with the visual impression. Orange dots are the remaining observations.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2875182&req=5

fig2: Detail of the 1° rotation image used for analyzing outliers for Protein Data Bank (PDB) entry 1vk8. (a) Red circles are the candidate Bragg spots accepted for the initial lattice refinement, after two rounds of heuristic spot filters. Spot intensity is one criterion for accepting these candidate observations, but other characteristics have disqualifed many of the brightest Bragg spots in this case. Specifically, the heuristic rules select signals for which the centroids are extremely well measured, namely round sharp spot profiles that are baseline-separated from neighboring signals. Thus, the Bragg signals exhibiting satellite spots and streaks oriented along the vertical c*-axis direction are excluded from refinement. (b) Yellow boxes show the predicted Bragg positions on the initially refined lattice model. Observations have been recolored to indicate their status with respect to this predicted lattice. Red dots represent the 40% of spots closest to their predicted positions, used for determining the Rayleigh distribution σ in Fig. 3 ▶(c). Pink spots are those determined to be outliers by the statistical test [equation (6)], very much in agreement with the visual impression. Orange dots are the remaining observations.
Mentions: This paper focuses only on what is achievable with the observed center-of-mass spot positions from the best-measured spots (Fig. 2 ▶ a). There is good reason to emphasize this initial characterization of raw images (prior to data reduction), since high-throughput crystal screening is relied upon increasingly to identify the best crystalline samples prior to the collection of full data sets. Crystal screening, which is now a standard option at many synchroton beamlines (Soltis et al., 2008 ▶), examines numerous samples sequentially under robotic control. A typical protocol involves the collection of two diffraction patterns spaced 90° apart on the y-rotational axis, which is enough data to gain a general understanding of the quality of the sample. Software server frameworks such as Web-Ice (González et al., 2008 ▶) or EDNA (Incardona et al., 2009 ▶) execute autoindexing trials for each crystal, generating a summary report that lists characteristics such as the signal strength and limiting resolution. One important quality measure is the r.m.s. deviation (in laboratory space) between the observed and predicted positions of the best-measured spots. Even in the absence of post-refinement, the lattice model ought to predict spot positions with sub-pixel accuracy, so in the best cases the r.m.s. spot displacement is expected to be less than the CCD pixel size, typically about 100 µm.

Bottom Line: Constructing a model lattice to fit the observed Bragg diffraction pattern is straightforward for perfect samples, but indexing can be challenging when artifacts are present, such as poorly shaped spots, split crystals giving multiple closely aligned lattices and outright superposition of patterns from aggregated microcrystals.Outliers are identified by assuming a Gaussian error distribution for the best-fitting spots and points diverging from this distribution are culled.The prevalence of outliers provides a potentially useful measure of sample quality.

View Article: PubMed Central - HTML - PubMed

Affiliation: Physical Biosciences Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA.

ABSTRACT
Constructing a model lattice to fit the observed Bragg diffraction pattern is straightforward for perfect samples, but indexing can be challenging when artifacts are present, such as poorly shaped spots, split crystals giving multiple closely aligned lattices and outright superposition of patterns from aggregated microcrystals. To optimize the lattice model against marginal data, refinement can be performed using a subset of the observations from which the poorly fitting spots have been discarded. Outliers are identified by assuming a Gaussian error distribution for the best-fitting spots and points diverging from this distribution are culled. The set of remaining observations produces a superior lattice model, while the rejected observations can be used to identify a second crystal lattice, if one is present. The prevalence of outliers provides a potentially useful measure of sample quality. The described procedures are implemented for macromolecular crystallography within the autoindexing program labelit.index (http://cci.lbl.gov/labelit).

No MeSH data available.


Related in: MedlinePlus