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Evaluating crystallographic likelihood functions using numerical quadratures

Acta Crystallographica Section D: Structural Biology, ISSN: 2059-7983, Vol: 76, Issue: Pt 8, Page: 736-750
2020
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Chasing Their Tails but Getting Somewhere: Reimagining the Shape of Noise Leads to Improved Molecular Models

Univ. of Tennessee undergrad Elliot Perryman (on right) worked with Biosciences staff scientist Peter Zwart during his fall 2019 Berkeley Lab Undergraduate Research (BLUR) internship.

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Reimagining the shape of noise leads to improved molecular models

Tenacity comes naturally to a guy who hails from the "mule capital of the world." That trait has stood Columbia, Tennessee, native Elliot Perryman in good stead as an intern at Lawrence Berkeley National Laboratory (Berkeley Lab). Last fall, he began working with staff scientist Peter Zwart in the Center for Advanced Mathematics for Energy Research Applications (CAMERA) through the Berkeley Lab Un

Article Description

Intensity-based likelihood functions In crystallographic applications have the potential to enhance the quality of structures derived from marginal diffraction data. Their usage, however, Is complicated by the ability to efficiently compute these target functions. Here, a numerical quadrature Is developed that allows the rapid evaluation of Intensity-based likelihood functions In crystallographic applications. By using a sequence of change-of-variable transformations, Including a nonlinear domain-compression operation, an accurate, robust and efficient quadrature Is constructed. The approach Is flexible and can Incorporate different noise models with relative ease.

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