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Accounting for partiality in serial crystallography using ray-tracing principles.

Kroon-Batenburg LM, Schreurs AM, Ravelli RB, Gros P - Acta Crystallogr. D Biol. Crystallogr. (2015)

Bottom Line: Still data are compared with a conventional rotation data set collected from a single lysozyme crystal.The R factor of the still data compared with the rotation data decreases from 26% using a Monte Carlo approach to 12% after applying the Lorentz correction, to 5.3% when estimating partialities by EVAL and finally to 4.7% after post-refinement.This suggests that the accuracy of the model parameters could be further improved.

View Article: PubMed Central - HTML - PubMed

Affiliation: Crystal and Structural Chemistry, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.

ABSTRACT
Serial crystallography generates `still' diffraction data sets that are composed of single diffraction images obtained from a large number of crystals arbitrarily oriented in the X-ray beam. Estimation of the reflection partialities, which accounts for the expected observed fractions of diffraction intensities, has so far been problematic. In this paper, a method is derived for modelling the partialities by making use of the ray-tracing diffraction-integration method EVAL. The method estimates partialities based on crystal mosaicity, beam divergence, wavelength dispersion, crystal size and the interference function, accounting for crystallite size. It is shown that modelling of each reflection by a distribution of interference-function weighted rays yields a `still' Lorentz factor. Still data are compared with a conventional rotation data set collected from a single lysozyme crystal. Overall, the presented still integration method improves the data quality markedly. The R factor of the still data compared with the rotation data decreases from 26% using a Monte Carlo approach to 12% after applying the Lorentz correction, to 5.3% when estimating partialities by EVAL and finally to 4.7% after post-refinement. The merging R(int) factor of the still data improves from 105 to 56% but remains high. This suggests that the accuracy of the model parameters could be further improved. However, with a multiplicity of around 40 and an R(int) of ∼50% the merged still data approximate the quality of the rotation data. The presented integration method suitably accounts for the partiality of the observed intensities in still diffraction data, which is a critical step to improve data quality in serial crystallography.

No MeSH data available.


Histogram of I(hkl)/〈I(hkl)〉. (a) Uncorrected, (b) partiality-corrected, (c) partiality and post-refined still data, (d) the same as (c) but omitting weak reflections and (e) rotation data
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fig8: Histogram of I(hkl)/〈I(hkl)〉. (a) Uncorrected, (b) partiality-corrected, (c) partiality and post-refined still data, (d) the same as (c) but omitting weak reflections and (e) rotation data

Mentions: Although the Lorentz and partiality corrections significantly improved the quality of the merged data, the merging Rint value remained high (i.e. 63.8% for all still data). To improve the partialities, we performed post-refinement of the image scale factor, unit-cell parameters and orientations, minimizing the target function of (11). Post-refinement of the ‘all stills’ data gave scale factors of 0.84–1.35 (additional to the scale factor sf used in equation 10) and sharpened the distribution of unit-cell dimensions, with virtually no effect on the variation of crystal orientations (Table 1 ▸). These adjustments resulted in a significant, but modest, reduction of Rint from 63.8 to 55.7% (Table 2 ▸). The progress in the precision of processing the data is reflected by the distributions I(hkl)/〈I(hkl)〉 shown in Fig. 8 ▸. Ideally, I(hkl)/〈I(hkl)〉 values form a sharp distribution around 1 (as a reference, we depict the distribution resulting from the rotation data in Fig. 8 ▸e). Figs. 8 ▸(b) and 8(c) reflect the striking improvement obtained by modelling the partiality in EVAL and subsequent post-refinement. Fig. 8 ▸(d) shows that mainly the weak data do not profit from the post-refinement. Comparison of the merged data sets shows that the improvement in precision is matched by an improvement in accuracy. Post-refinement reduced the Rcomp from 5.3 to 4.7% (Table 3 ▸).


Accounting for partiality in serial crystallography using ray-tracing principles.

Kroon-Batenburg LM, Schreurs AM, Ravelli RB, Gros P - Acta Crystallogr. D Biol. Crystallogr. (2015)

Histogram of I(hkl)/〈I(hkl)〉. (a) Uncorrected, (b) partiality-corrected, (c) partiality and post-refined still data, (d) the same as (c) but omitting weak reflections and (e) rotation data
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4556312&req=5

fig8: Histogram of I(hkl)/〈I(hkl)〉. (a) Uncorrected, (b) partiality-corrected, (c) partiality and post-refined still data, (d) the same as (c) but omitting weak reflections and (e) rotation data
Mentions: Although the Lorentz and partiality corrections significantly improved the quality of the merged data, the merging Rint value remained high (i.e. 63.8% for all still data). To improve the partialities, we performed post-refinement of the image scale factor, unit-cell parameters and orientations, minimizing the target function of (11). Post-refinement of the ‘all stills’ data gave scale factors of 0.84–1.35 (additional to the scale factor sf used in equation 10) and sharpened the distribution of unit-cell dimensions, with virtually no effect on the variation of crystal orientations (Table 1 ▸). These adjustments resulted in a significant, but modest, reduction of Rint from 63.8 to 55.7% (Table 2 ▸). The progress in the precision of processing the data is reflected by the distributions I(hkl)/〈I(hkl)〉 shown in Fig. 8 ▸. Ideally, I(hkl)/〈I(hkl)〉 values form a sharp distribution around 1 (as a reference, we depict the distribution resulting from the rotation data in Fig. 8 ▸e). Figs. 8 ▸(b) and 8(c) reflect the striking improvement obtained by modelling the partiality in EVAL and subsequent post-refinement. Fig. 8 ▸(d) shows that mainly the weak data do not profit from the post-refinement. Comparison of the merged data sets shows that the improvement in precision is matched by an improvement in accuracy. Post-refinement reduced the Rcomp from 5.3 to 4.7% (Table 3 ▸).

Bottom Line: Still data are compared with a conventional rotation data set collected from a single lysozyme crystal.The R factor of the still data compared with the rotation data decreases from 26% using a Monte Carlo approach to 12% after applying the Lorentz correction, to 5.3% when estimating partialities by EVAL and finally to 4.7% after post-refinement.This suggests that the accuracy of the model parameters could be further improved.

View Article: PubMed Central - HTML - PubMed

Affiliation: Crystal and Structural Chemistry, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.

ABSTRACT
Serial crystallography generates `still' diffraction data sets that are composed of single diffraction images obtained from a large number of crystals arbitrarily oriented in the X-ray beam. Estimation of the reflection partialities, which accounts for the expected observed fractions of diffraction intensities, has so far been problematic. In this paper, a method is derived for modelling the partialities by making use of the ray-tracing diffraction-integration method EVAL. The method estimates partialities based on crystal mosaicity, beam divergence, wavelength dispersion, crystal size and the interference function, accounting for crystallite size. It is shown that modelling of each reflection by a distribution of interference-function weighted rays yields a `still' Lorentz factor. Still data are compared with a conventional rotation data set collected from a single lysozyme crystal. Overall, the presented still integration method improves the data quality markedly. The R factor of the still data compared with the rotation data decreases from 26% using a Monte Carlo approach to 12% after applying the Lorentz correction, to 5.3% when estimating partialities by EVAL and finally to 4.7% after post-refinement. The merging R(int) factor of the still data improves from 105 to 56% but remains high. This suggests that the accuracy of the model parameters could be further improved. However, with a multiplicity of around 40 and an R(int) of ∼50% the merged still data approximate the quality of the rotation data. The presented integration method suitably accounts for the partiality of the observed intensities in still diffraction data, which is a critical step to improve data quality in serial crystallography.

No MeSH data available.