title: MLEM deconvolution of protein X-ray diffraction images based on a multiple-PSF model.
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.
authors.affiliation: School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK. d.zhu@uea.ac.uk
authors.forename: Daan
authors.initials: D
authors.lastname: Zhu
authors.forename: Moe
authors.initials: M
authors.lastname: Razaz
authors.forename: Andrew
authors.initials: A
authors.lastname: Hemmings
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure*; D012044:Regression Analysis; D012542:Scattering, Radiation; D014961:X-Ray Diffraction / Q000379:methods*
chemical_list: D011506:Proteins