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

Protocol for outlier detection. (a) Data-acquisition geometry, showing librations of the crystal about the incident beam (z axis) and goniometer rotation axis (y axis). (b) Computational procedure showing steps that are executed within the program labelit.index. New outlier detection steps developed in this paper are indicated by an asterisk (*) and the alternative pathway for indexing the second lattice (if one is present) is indicated by a dagger symbol (†).
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fig1: Protocol for outlier detection. (a) Data-acquisition geometry, showing librations of the crystal about the incident beam (z axis) and goniometer rotation axis (y axis). (b) Computational procedure showing steps that are executed within the program labelit.index. New outlier detection steps developed in this paper are indicated by an asterisk (*) and the alternative pathway for indexing the second lattice (if one is present) is indicated by a dagger symbol (†).

Mentions: ‘How good is this sample?’ and ‘How well does the model fit the data?’ are pertinent questions throughout the process of structure solution, driving critical experimental decisions even at the initial step of eliciting the crystal lattice from the raw diffraction image. Algorithms for determining and refining the lattice description are well understood, and are implemented by many data-processing packages such as MOSFLM (Leslie, 1999 ▶), HKL (Otwinowski & Minor, 1997 ▶), XDS (Kabsch, 2010a ▶,b ▶) and d*TREK (Pflugrath, 1999 ▶). Generally, candidate Bragg spots are selected from the diffraction image and their observed laboratory coordinates are converted into reciprocal space by an appropriate geometric construction (Arndt & Wonacott, 1977 ▶; reviewed by Dauter, 1999 ▶). Lattice periodicity is detected by one of several autoindexing procedures (e.g. Steller et al., 1997 ▶), leading to a lattice model with nine degrees of freedom: three unit-cell lengths, three unit-cell angles and three librations of the lattice with respect to the laboratory axes (Fig. 1 ▶ a). The predictive power of the lattice model makes accurate data integration possible; in particular, it is used to deduce image coordinates for all of the reflections (Rossmann & van Beek, 1999 ▶), even those with a low intensity level that would not otherwise be distinguishable from the background level.


Autoindexing with outlier rejection and identification of superimposed lattices.

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

Protocol for outlier detection. (a) Data-acquisition geometry, showing librations of the crystal about the incident beam (z axis) and goniometer rotation axis (y axis). (b) Computational procedure showing steps that are executed within the program labelit.index. New outlier detection steps developed in this paper are indicated by an asterisk (*) and the alternative pathway for indexing the second lattice (if one is present) is indicated by a dagger symbol (†).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: Protocol for outlier detection. (a) Data-acquisition geometry, showing librations of the crystal about the incident beam (z axis) and goniometer rotation axis (y axis). (b) Computational procedure showing steps that are executed within the program labelit.index. New outlier detection steps developed in this paper are indicated by an asterisk (*) and the alternative pathway for indexing the second lattice (if one is present) is indicated by a dagger symbol (†).
Mentions: ‘How good is this sample?’ and ‘How well does the model fit the data?’ are pertinent questions throughout the process of structure solution, driving critical experimental decisions even at the initial step of eliciting the crystal lattice from the raw diffraction image. Algorithms for determining and refining the lattice description are well understood, and are implemented by many data-processing packages such as MOSFLM (Leslie, 1999 ▶), HKL (Otwinowski & Minor, 1997 ▶), XDS (Kabsch, 2010a ▶,b ▶) and d*TREK (Pflugrath, 1999 ▶). Generally, candidate Bragg spots are selected from the diffraction image and their observed laboratory coordinates are converted into reciprocal space by an appropriate geometric construction (Arndt & Wonacott, 1977 ▶; reviewed by Dauter, 1999 ▶). Lattice periodicity is detected by one of several autoindexing procedures (e.g. Steller et al., 1997 ▶), leading to a lattice model with nine degrees of freedom: three unit-cell lengths, three unit-cell angles and three librations of the lattice with respect to the laboratory axes (Fig. 1 ▶ a). The predictive power of the lattice model makes accurate data integration possible; in particular, it is used to deduce image coordinates for all of the reflections (Rossmann & van Beek, 1999 ▶), even those with a low intensity level that would not otherwise be distinguishable from the background level.

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