Biology subjects

Bernstein, H. J.

Publications and source records attributed to Bernstein, H. J..

3 recordsLinked to original sources

bioRxiv2024

Massive Compression for High Data Rate Macromolecular Crystallography (HDRMX): Impact on Diffraction Data and Subsequent Structural Analysis

New higher-count-rate, integrating, large area X-ray detectors with framing rates as high as 17,400 images per second are beginning to be available. These will soon be used for specialized MX experiments but will require optimal lossy compression algorithms to enable systems to keep up with data throughput. Some information may be lost. Can we minimize this loss with acceptable impact on structural information? To explore this question, we have considered several approaches: summing short sequences of images, binning to create the effect of larger pixels, use of JPEG-2000 lossy wavelet-based compression, and use of Hcompress, which is a Haar-wavelet-based lossy compression borrowed from astronomy. We also explore the effect of the combination of summing, binning, and Hcompress or JPEG-2000. In each of these last two methods one can specify approximately how much one wants the result to be compressed from the starting file size. These provide particularly effective lossy compressions that retain essential information for structure solution from Bragg reflections. SynopsisNew higher-count-rate, integrating, large area X-ray detectors with framing rates as high as 17,400 images per second are beginning to be available. These will soon be used for specialized MX experiments but will require optimal lossy compression algorithms to enable systems to keep up with data throughput. Some information may be lost. Can we minimize this loss with acceptable impact on structural information?

bioinformatics↗

bioRxiv2020

A simple technique to classify diffraction data from dynamic proteins according to individual polymorphs

One often observes small but measurable differences in diffraction data measured from different crystals of a single protein. These differences might reflect structural differences in the protein and potentially reflect the natural dynamism of the molecule in solution. Partitioning these mixed-state data into single-state clusters is a critical step to extract information about the dynamic behavior of proteins from hundreds or thousands of single-crystal data sets. Mixed-state data can be obtained deliberately (through intentional perturbation) or inadvertently (while attempting to measure highly redundant single-crystal data). State changes may be expressed as changes in morphology, so that a subset of the polystates may be observed as polymorphs. After mixed-state data are deliberately or inadvertently measured, the challenge is to sort the data into clusters that may represent relevant biological polystates. Here we address this problem using a simple multi-factor clustering approach that classifies each data set using independent observables in order to assign each data set to the correct location in conformation space. We illustrate this method using two independent observables (unit cell constants and intensities) to cluster mixed-state data from chymotrypsinogen (ChTg) crystals. We observe that the data populate an arc of the reaction trajectory as ChTg is converted into chymotrypsin.

molecular biology↗

bioRxiv2017

Serial Crystallography with Multi-stage Merging of 1000’s of Images

KAMO and Blend provide particularly effective tools to manage automatically the merging of large numbers of datasets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending Blend to support additional clustering options such as use of more accurate cell distance metrics and use of reflection-intensity correlation coefficients to infer "distances" among sets of reflec- tions. This increases the sensitivity to differences in unit cell parameters and allows for clustering to assemble nearly complete datasets on the basis of intensity or ampli- tude differences. If datasets are already sufficiently complete to permit it, one applies KAMO once and clusters the data using intensities only. If starting from incomplete datasets, one applies KAMO twice, first using cell parameters. In this step we use either the simple cell vector distance of the original Blend, or we use the more sensi- tive NCDist. This step tends to find clusters of sufficient size so that, when merged, each cluster is sufficiently complete to allow reflection intensities or amplitudes to be compared. One then uses KAMO again using the correlation between the reflections having a common hkl to merge clusters in a way sensitive to structural differences that may not have perturbed the cell parameters sufficiently to make meaningful clusters. Many groups have developed effective clustering algorithms that use a measurable physical parameter from each diffraction still or wedge to cluster the data into cate- gories which then can be merged, one hopes, to yield the electron density from a single protein form. Since these physical parameters are often largely independent from one another, it should be possible to greatly improve the efficacy of data clustering software by using a multi-stage partitioning strategy. Here, we have demonstrated one possible approach to multi-stage data clustering. Our strategy is to use unit-cell clustering until merged data is sufficiently complete then to use intensity-based clustering. We have demonstrated that, using this strategy, we are able to accurately cluster datasets from crystals that have subtle differences.

bioinformatics↗