MLEM deconvolution of protein X-ray diffraction images based on a multiple-PSF model.

Daan Zhu; Moe Razaz; Andrew Hemmings

doi:10.1109/tnb.2006.875046

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MLEM deconvolution of protein X-ray diffraction images based on a multiple-PSF model.

Daan Zhu·Moe Razaz·Andrew Hemmings

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Abstract

In this paper we analyze the degradation of protein X-ray diffraction images by diffuse light distortion (DLD). In order to correct the degradation, a new multiple point spread function (PSF) model is introduced and used to restore X-ray diffraction image data (XRD). Raw PSFs are collected from isolated spots in high-resolution areas on the diffraction patterns which represent the orientation of DLDs. An adaptive ridge regression (ARR) technique is used to remove noise from the raw PSF data. A target Gaussian function is used to model the raw PSFs. A maximum likelihood expectation maximization (MLEM) algorithm combined with a multi-PSF model is employed to restore high intensity, asymmetrical protein X-ray diffraction data. Experimental results using a single and multiple PSFs are presented and discussed. We show that using a multiple PSF model in the deconvolution algorithm improved the quality of the XRD and as a result the spot integration error (chi-squared) and corresponding electron density mapare improved.

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Algorithms Computer Simulation Models, Chemical Models, Molecular Models, Statistical Protein Conformation Proteins Regression Analysis Scattering, Radiation X-Ray Diffraction Evaluation Study Journal Article

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Daan Zhu, Moe Razaz, Andrew Hemmings. 2006. MLEM deconvolution of protein X-ray diffraction images based on a multiple-PSF model.. https://doi.org/10.1109/tnb.2006.875046

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