scholarly journals Can I solve my structure by SAD phasing? Planning an experiment, scaling data and evaluating the useful anomalous correlation and anomalous signal

2016 ◽  
Vol 72 (3) ◽  
pp. 359-374 ◽  
Author(s):  
Thomas C. Terwilliger ◽  
Gábor Bunkóczi ◽  
Li-Wei Hung ◽  
Peter H. Zwart ◽  
Janet L. Smith ◽  
...  
Keyword(s):  
Figure Of Merit ◽  
Signal To Noise Ratio ◽  
Theoretical Framework ◽  
Data Sets ◽  
Signal To Noise ◽  
Acta Cryst ◽  
Data Set ◽  
Local Scaling ◽  
Sad Phasing ◽  
Anomalous Signal

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 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.

scholarly journals Quantifying instrument errors in macromolecular X-ray data sets

2010 ◽  
Vol 66 (6) ◽  
pp. 733-740 ◽  
Author(s):  
Kay Diederichs
Keyword(s):  
Data Reduction ◽  
Spindle Speed ◽  
Experimental Setup ◽  
Data Sets ◽  
Signal To Noise ◽  
Instrument Error ◽  
Acta Cryst ◽  
Data Set ◽  
X Ray ◽  
The Stability

An indicator which is calculated after the data reduction of a test data set may be used to estimate the (systematic) instrument error at a macromolecular X-ray source. The numerical value of the indicator is the highest signal-to-noise [I/σ(I)] value that the experimental setup can produce and its reciprocal is related to the lower limit of the mergingRfactor. In the context of this study, the stability of the experimental setup is influenced and characterized by the properties of the X-ray beam, shutter, goniometer, cryostream and detector, and also by the exposure time and spindle speed. Typical values of the indicator are given for data sets from the JCSG archive. Some sources of error are explored with the help of test calculations usingSIM_MX[Diederichs (2009),Acta Cryst.D65, 535–542]. One conclusion is that the accuracy of data at low resolution is usually limited by the experimental setup rather than by the crystal. It is also shown that the influence of vibrations and fluctuations may be mitigated by a reduction in spindle speed accompanied by stronger attenuation.

scholarly journals Can I solve my structure by SAD phasing? Anomalous signal in SAD phasing

2016 ◽  
Vol 72 (3) ◽  
pp. 346-358 ◽  
Author(s):  
Thomas C. Terwilliger ◽  
Gábor Bunkóczi ◽  
Li-Wei Hung ◽  
Peter H. Zwart ◽  
Janet L. Smith ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Atomic Displacement ◽  
Peak Height ◽  
High Ratio ◽  
Data Set ◽  
High Data ◽  
Sad Phasing ◽  
Anomalous Signal ◽  
Displacement Factor ◽  
Difference Fourier

A key challenge in the SAD phasing method is solving a structure when the anomalous signal-to-noise ratio is low. A simple theoretical framework for describing measurements of anomalous differences and the resulting useful anomalous correlation and anomalous signal in a SAD experiment is presented. Here, the useful anomalous correlation is defined as the correlation of anomalous differences with ideal anomalous differences from the anomalous substructure. The useful anomalous correlation reflects the accuracy of the data and the absence of minor sites. The useful anomalous correlation also reflects the information available for estimating crystallographic phases once the substructure has been determined. In contrast, the anomalous signal (the peak height in a model-phased anomalous difference Fourier at the coordinates of atoms in the anomalous substructure) reflects the information available about each site in the substructure and is related to the ability to find the substructure. A theoretical analysis shows that the expected value of the anomalous signal is the product of the useful anomalous correlation, the square root of the ratio of the number of unique reflections in the data set to the number of sites in the substructure, and a function that decreases with increasing values of the atomic displacement factor for the atoms in the substructure. This means that the ability to find the substructure in a SAD experiment is increased by high data quality and by a high ratio of reflections to sites in the substructure, and is decreased by high atomic displacement factors for the substructure.

scholarly journals Efficient merging of data from multiple samples for determination of anomalous substructure

2016 ◽  
Vol 72 (3) ◽  
pp. 296-302 ◽  
Author(s):  
David L. Akey ◽  
Thomas C. Terwilliger ◽  
Janet L. Smith
Keyword(s):  
Data Sets ◽  
Strongly Correlated ◽  
Data Set ◽  
Local Scaling ◽  
Cell Parameters ◽  
Radiation Induced ◽  
Anomalous Signal ◽  
Effects Of Radiation ◽  
Multiple Samples

Merging of data from multiple crystals has proven to be useful for determination of the anomalously scattering atomic substructure for crystals with weak anomalous scatterers (e.g.S and P) and/or poor diffraction. Strategies for merging data from many samples, which require assessment of sample isomorphism, rely on metrics of variability in unit-cell parameters, anomalous signal correlation and overall data similarity. Local scaling, anomalous signal optimization and data-set weighting, implemented inphenix.scale_and_merge, provide an efficient protocol for merging data from many samples. The protein NS1 was used in a series of trials with data collected from 28 samples for phasing by single-wavelength anomalous diffraction of the native S atoms. The local-scaling, anomalous-optimization protocol produced merged data sets with higher anomalous signal quality indicators than did standard global-scaling protocols. The local-scaled data were also more successful in substructure determination. Merged data quality was assessed for data sets where the multiplicity was reduced in either of two ways: by excluding data from individual crystals (to reduce errors owing to non-isomorphism) or by excluding the last-recorded segments of data from each crystal (to minimize the effects of radiation damage). The anomalous signal was equivalent at equivalent multiplicity for the two procedures, and structure-determination success correlated with anomalous signal metrics. The quality of the anomalous signal was strongly correlated with data multiplicity over a range of 12-fold to 150-fold multiplicity. For the NS1 data, the local-scaling and anomalous-optimization protocol handled sample non-isomorphism and radiation-induced decay equally well.

scholarly journals An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

2019 ◽  
Vol 73 (8) ◽  
pp. 893-901
Author(s):  
Sinead J. Barton ◽  
Bryan M. Hennelly
Keyword(s):  
Cosmic Ray ◽  
Data Sets ◽  
Biological Cells ◽  
Statistical Classification ◽  
Signal To Noise ◽  
Multivariate Statistical ◽  
Data Set ◽  
Artefact Removal ◽  
Single Capture ◽  
Acquisition Method

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

scholarly journals New processing tools for weak and/or spatially overlapped macromolecular diffraction patterns

1999 ◽  
Vol 55 (10) ◽  
pp. 1733-1741 ◽  
Author(s):  
Dominique Bourgeois
Keyword(s):  
Signal To Noise Ratio ◽  
Data Sets ◽  
Signal To Noise ◽  
Weighting Method ◽  
Time Resolved ◽  
Accurate Evaluation ◽  
Profile Fitting ◽  
Integration Program ◽  
Diffraction Patterns ◽  
New Processing

Tools originally developed for the treatment of weak and/or spatially overlapped time-resolved Laue patterns were extended to improve the processing of difficult monochromatic data sets. The integration programPrOWallows deconvolution of spatially overlapped spots which are usually rejected by standard packages. By using dynamically adjusted profile-fitting areas, a carefully built library of reference spots and interpolation of reference profiles, this program also provides a more accurate evaluation of weak spots. In addition, by using Wilson statistics, it allows rejection of non-redundant strong outliers such as zingers, which otherwise may badly corrupt the data. A weighting method for optimizing structure-factor amplitude differences, based on Bayesian statistics and originally applied to low signal-to-noise ratio time-resolved Laue data, is also shown to significantly improve other types of subtle amplitude differences, such as anomalous differences.

scholarly journals Data-collection strategy for challenging native SAD phasing

2016 ◽  
Vol 72 (3) ◽  
pp. 421-429 ◽  
Author(s):  
Vincent Olieric ◽  
Tobias Weinert ◽  
Aaron D. Finke ◽  
Carolin Anders ◽  
Dianfan Li ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Sets ◽  
Yield Data ◽  
Guide Rna ◽  
Multiple Data ◽  
Data Collection Strategy ◽  
Sad Phasing ◽  
Anomalous Signal ◽  
Multiple Data Sets ◽  
Low X

Recent improvements in data-collection strategies have pushed the limits of native SAD (single-wavelength anomalous diffraction) phasing, a method that uses the weak anomalous signal of light elements naturally present in macromolecules. These involve the merging of multiple data sets from either multiple crystals or from a single crystal collected in multiple orientations at a low X-ray dose. Both approaches yield data of high multiplicity while minimizing radiation damage and systematic error, thus ensuring accurate measurements of the anomalous differences. Here, the combined use of these two strategies is described to solve cases of native SAD phasing that were particular challenges: the integral membrane diacylglycerol kinase (DgkA) with a low Bijvoet ratio of 1% and the large 200 kDa complex of the CRISPR-associated endonuclease (Cas9) bound to guide RNA and target DNA crystallized in the low-symmetry space groupC2. The optimal native SAD data-collection strategy based on systematic measurements performed on the 266 kDa multiprotein/multiligand tubulin complex is discussed.

Assisted traveltime picking of crosshole GPR data

Geophysics ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. J35-J48 ◽  
Author(s):  
Bernard Giroux ◽  
Abderrezak Bouchedda ◽  
Michel Chouteau
Keyword(s):  
Time Window ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Information Criterion ◽  
High Signal ◽  
Data Sets ◽  
Signal To Noise ◽  
Arrival Times ◽  
Common Time ◽  
Waveform Similarity

We introduce two new traveltime picking schemes developed specifically for crosshole ground-penetrating radar (GPR) applications. The main objective is to automate, at least partially, the traveltime picking procedure and to provide first-arrival times that are closer in quality to those of manual picking approaches. The first scheme is an adaptation of a method based on cross-correlation of radar traces collated in gathers according to their associated transmitter-receiver angle. A detector is added to isolate the first cycle of the radar wave and to suppress secon-dary arrivals that might be mistaken for first arrivals. To improve the accuracy of the arrival times obtained from the crosscorrelation lags, a time-rescaling scheme is implemented to resize the radar wavelets to a common time-window length. The second method is based on the Akaike information criterion(AIC) and continuous wavelet transform (CWT). It is not tied to the restrictive criterion of waveform similarity that underlies crosscorrelation approaches, which is not guaranteed for traces sorted in common ray-angle gathers. It has the advantage of being automated fully. Performances of the new algorithms are tested with synthetic and real data. In all tests, the approach that adds first-cycle isolation to the original crosscorrelation scheme improves the results. In contrast, the time-rescaling approach brings limited benefits, except when strong dispersion is present in the data. In addition, the performance of crosscorrelation picking schemes degrades for data sets with disparate waveforms despite the high signal-to-noise ratio of the data. In general, the AIC-CWT approach is more versatile and performs well on all data sets. Only with data showing low signal-to-noise ratios is the AIC-CWT superseded by the modified crosscorrelation picker.

A low-PDAP and high-PSNR approximate 4:2 compressor cell in CNFET technology

Circuit World ◽  
2019 ◽  
Vol 45 (3) ◽  
pp. 156-168 ◽  
Author(s):  
Yavar Safaei Mehrabani ◽  
Mehdi Bagherizadeh ◽  
Mohammad Hossein Shafiabadi ◽  
Abolghasem Ghasempour
Keyword(s):  
Image Processing ◽  
Design Methodology ◽  
Figure Of Merit ◽  
Signal To Noise Ratio ◽  
Monte Carlo Analysis ◽  
The Other ◽  
Threshold Logic ◽  
Signal To Noise ◽  
Content Type ◽  
Power Delay Product

Purpose This paper aims to present an inexact 4:2 compressor cell using carbon nanotube filed effect transistors (CNFETs). Design/methodology/approach To design this cell, the capacitive threshold logic (CTL) has been used. Findings To evaluate the proposed cell, comprehensive simulations are carried out at two levels of the circuit and image processing. At the circuit level, the HSPICE software has been used and the power consumption, delay, and power-delay product are calculated. Also, the power-delaytransistor count product (PDAP) is used to make a compromise between all metrics. On the other hand, the Monte Carlo analysis has been used to scrutinize the robustness of the proposed cell against the variations in the manufacturing process. The results of simulations at this level of abstraction indicate the superiority of the proposed cell to other circuits. At the application level, the MATLAB software is also used to evaluate the peak signal-to-noise ratio (PSNR) figure of merit. At this level, the two primary images are multiplied by a multiplier circuit consisting of 4:2 compressors. The results of this simulation also show the superiority of the proposed cell to others. Originality/value This cell significantly reduces the number of transistors and only consists of NOT gates.

Combining cross-crystal averaging and MRSAD to phase a 4354-amino-acid structure

2016 ◽  
Vol 72 (2) ◽  
pp. 182-191
Author(s):  
Jason Nicholas Busby ◽  
J. Shaun Lott ◽  
Santosh Panjikar
Keyword(s):  
Radiation Damage ◽  
Model Building ◽  
Data Sets ◽  
Low Resolution ◽  
Data Set ◽  
C Protein ◽  
Large Size ◽  
Anomalous Signal ◽  
Difference Fourier ◽  
Combined Data

The B and C proteins from the ABC toxin complex ofYersinia entomophagaform a large heterodimer that cleaves and encapsulates the C-terminal toxin domain of the C protein. Determining the structure of the complex formed by B and the N-terminal region of C was challenging owing to its large size, the non-isomorphism of different crystals and their sensitivity to radiation damage. A native data set was collected to 2.5 Å resolution and a non-isomorphous Ta6Br12-derivative data set was collected that showed strong anomalous signal at low resolution. The tantalum-cluster sites could be found, but the anomalous signal did not extend to a high enough resolution to allow model building. Selenomethionine (SeMet)-derivatized protein crystals were produced, but the high number (60) of SeMet sites and the sensitivity of the crystals to radiation damage made phasing using the SAD or MAD methods difficult. Multiple SeMet data sets were combined to provide 30-fold multiplicity, and the low-resolution phase information from the Ta6Br12data set was transferred to this combined data set by cross-crystal averaging. This allowed the Se atoms to be located in an anomalous difference Fourier map; they were then used inAuto-Rickshawfor multiple rounds of autobuilding and MRSAD.

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