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Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography.

ABSTRACT: Chemical and conformational changes underlie the functional cycles of proteins. Comparative crystallography can reveal these changes over time, over ligands, and over chemical and physical perturbations in atomic detail. A key difficulty, however, is that the resulting observations must be placed on the same scale by correcting for experimental factors. We recently introduced a Bayesian framework for correcting (scaling) X-ray diffraction data by combining deep learning with statistical priors informed by crystallographic theory. To scale comparative crystallography data, we here combine this framework with a multivariate statistical theory of comparative crystallography. By doing so, we find strong improvements in the detection of protein dynamics, element-specific anomalous signal, and the binding of drug fragments.

SUBMITTER: Hekstra DR

PROVIDER: S-EPMC11291090 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography.

Hekstra Doeke R DR Wang Harrison K HK Klureza Margaret A MA Greisman Jack B JB Dalton Kevin M KM

bioRxiv : the preprint server for biology 20240723

Chemical and conformational changes underlie the functional cycles of proteins. Comparative crystallography can reveal these changes over time, over ligands, and over chemical and physical perturbations in atomic detail. A key difficulty, however, is that the resulting observations must be placed on the same scale by correcting for experimental factors. We recently introduced a Bayesian framework for correcting (scaling) X-ray diffraction data by combining deep learning with statistical priors i ...[more]

PMID: 39091831

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Dataset Information

Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography.

Publications

Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography.

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