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Enhancing MAD F(A) data for substructure determination.

ABSTRACT: Heavy-atom substructure determination is a critical step in phasing an unknown macromolecular structure. Dual-space (Shake-and-Bake) recycling is a very effective procedure for locating the substructure (heavy) atoms using F(A) data estimated from multiple-wavelength anomalous diffraction. However, the estimated F(A) are susceptible to the accumulation of errors in the individual intensity measurements at several wavelengths and from inaccurate estimation of the anomalous atomic scattering corrections f' and f''. In this paper, a new statistical and computational procedure which merges multiple F(A) estimates into an averaged data set is used to further improve the quality of the estimated anomalous amplitudes. The results of 18 Se-atom substructure determinations provide convincing evidence in favor of using such a procedure to locate anomalous scatterers.

SUBMITTER: Xu H

PROVIDER: S-EPMC2917277 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Enhancing MAD F(A) data for substructure determination.

Xu Hongliang H

Acta crystallographica. Section D, Biological crystallography 20100714 Pt 8

Heavy-atom substructure determination is a critical step in phasing an unknown macromolecular structure. Dual-space (Shake-and-Bake) recycling is a very effective procedure for locating the substructure (heavy) atoms using F(A) data estimated from multiple-wavelength anomalous diffraction. However, the estimated F(A) are susceptible to the accumulation of errors in the individual intensity measurements at several wavelengths and from inaccurate estimation of the anomalous atomic scattering corre ...[more]

PMID: 20693694

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Project description:Objectives(1) To develop and internally-validate Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) thresholds for separating sedentary behaviours from common light-intensity physical activities using raw acceleration data collected from both hip- and wrist-worn tri-axial accelerometers; and (2) to compare and evaluate the performances between the ENMO and MAD metrics.MethodsThirty-three adults [mean age (standard deviation (SD)) = 27.4 (5.9) years; mean BMI (SD) = 23.9 (3.7) kg/m2; 20 females (60.6%)] wore four accelerometers; an ActiGraph GT3X+ and a GENEActiv on the right hip; and an ActiGraph GT3X+ and a GENEActiv on the non-dominant wrist. Under laboratory-conditions, participants performed 16 different activities (11 sedentary behaviours and 5 light-intensity physical activities) for 5 minutes each. ENMO and MAD were computed from the raw acceleration data, and logistic regression and receiver-operating-characteristic (ROC) analyses were implemented to derive thresholds for activity discrimination. Areas under ROC curves (AUROC) were calculated to summarise performances and thresholds were assessed via executing leave-one-out-cross-validations.ResultsFor both hip and wrist monitor placements, in comparison to the ActiGraph GT3X+ monitors, the ENMO and MAD values derived from the GENEActiv devices were observed to be slightly higher, particularly for the lower-intensity activities. Monitor-specific hip and wrist ENMO and MAD thresholds showed excellent ability for separating sedentary behaviours from motion-based light-intensity physical activities (in general, AUROCs >0.95), with validation indicating robustness. However, poor classification was experienced when attempting to isolate standing still from sedentary behaviours (in general, AUROCs <0.65). The ENMO and MAD metrics tended to perform similarly across activities and accelerometer brands.ConclusionsResearchers can utilise these robust monitor-specific hip and wrist ENMO and MAD thresholds, in order to accurately separate sedentary behaviours from common motion-based light-intensity physical activities. However, caution should be taken if isolating sedentary behaviours from standing is of particular interest.

Dataset Information

Enhancing MAD F(A) data for substructure determination.

Publications

Enhancing MAD F(A) data for substructure determination.

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