DOE OSTI · 2999004
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.
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Hekstra, Doeke R. [Harvard Univ., Cambridge, MA (United States)] (ORCID:0000000323329223), Wang, Harrison K. [Harvard Univ., Cambridge, MA (United States)] (ORCID:0000000342759472), Klureza, Margaret A. [Harvard Univ., Cambridge, MA (United States)] (ORCID:0000000157741297), Greisman, Jack B. [Harvard Univ., Cambridge, MA (United States); D. E. Shaw Research, New York, NY (United States)] (ORCID:0000000263942658), Dalton, Kevin M. [Harvard Univ., Cambridge, MA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States). Linac Coherent Light Source (LCLS); New York Univ. (NYU), NY (United States)] (ORCID:000000019396085X). 2025-04-10. Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography. https://doi.org/10.1101/2024.07.22.604476
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