Deep learning assisted phase retrieval and computational methods in coherent diffractive imaging

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Deep learning assisted phase retrieval and computational methods in coherent diffractive imaging

Bellisario, Alfredo (author): Uppsala universitet,Molekylär biofysik

Ekeberg, Tomas, Dr. 1983- (thesis advisor): Uppsala universitet,Molekylär biofysik

Maia, Filipe, Professor (thesis advisor): Uppsala universitet,Molekylär biofysik

Barty, Anton, Dr. (opponent): Deutsches Elektronen-Synchrotron (DESY) Notkestrasse 85, 22607 Hamburg, Germany

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(creator_code:org_t)

ISBN 9789151321202
Uppsala : Acta Universitatis Upsaliensis, 2024
English 94 s.
Series: Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, 1651-6214 ; 2400

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Doctoral thesis (other academic/artistic)

Abstract Subject headings

In recent years, advances in Artificial Intelligence and experimental techniques have revolutionized the field of structural biology. X-ray crystallography and Cryo-EM have provided unprecedented insights into the structures of biomolecules, while the unexpected success of AlphaFold has opened up new avenues of investigation. However, studying the dynamics of proteins at high resolution remains a significant obstacle, especially for fast dynamics. Single-particle imaging (SPI) or Flash X-ray Imaging (FXI) is an emerging technique that may enable the mapping of the conformational landscape of biological molecules at high resolution and fast time scale. This thesis discusses the potential of SPI/FXI, its challenges, recent experimental successes, and the advancements driving its development. In particular, machine learning and neural networks could play a vital role in fostering data analysis and improving SPI/FXI data processing. In Paper I, we discuss the problem of noise and detector masks in collecting FXI data. I simulated a dataset of diffraction patterns and used it to train a Convolutional Neural Network (U-Net) to restore data by denoising and filling in detector masks. As a natural continuation of this work, I trained another machine learning model in Paper II to estimate 2D protein densities from diffraction intensities. In the final chapter, corresponding to Paper III, we discuss another experimental method, time-resolved Small Angle X-ray Scattering (SAXS), and a new algorithm recently developed for SAXS data, the DENsity from Solution Scattering (DENSS) algorithm. I discuss the potential of DENSS in time-resolved SAXS and its application for structural fitting of AsLOV2, a Light-Oxygen-Voltage (LOV) protein domain from Avena sativa.

Subject headings

NATURVETENSKAP -- Biologi -- Biofysik (hsv//swe)
NATURAL SCIENCES -- Biological Sciences -- Biophysics (hsv//eng)
NATURVETENSKAP -- Fysik -- Atom- och molekylfysik och optik (hsv//swe)
NATURAL SCIENCES -- Physical Sciences -- Atom and Molecular Physics and Optics (hsv//eng)

Keyword

coherent diffractive X-ray imaging
lensless imaging
coherent X-ray diffractive imaging
flash diffractive imaging
single particle imaging
phase retrieval
X-ray free-electron laser
XFEL
FEL
CXI
CDI
CXDI
FXI
Machine learning
Deep learning
Reinforcement learning
Neural networks
Convolutional Neural Networks
Molekylär biovetenskap
Molecular Life Sciences

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