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

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Bellisario, AlfredoUppsala universitet,Molekylär biofysik (author)

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

BookEnglish2024

Publisher, publication year, extent ...

Uppsala :Acta Universitatis Upsaliensis,2024
94 s.
electronicrdacarrier

Numbers

LIBRIS-ID:oai:DiVA.org:uu-526527
ISBN:9789151321202
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-526527URI

Supplementary language notes

Language:English
Summary in:English &language:-1_t

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SwePubSwePub

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Subject category:vet swepub-contenttype
Subject category:dok swepub-publicationtype

Series

Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology,1651-6214 ;2400

Notes

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 and genre

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
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

Added entries (persons, corporate bodies, meetings, titles ...)

Ekeberg, Tomas,Dr.1983-Uppsala universitet,Molekylär biofysik(Swepub:uu)tomek611 (thesis advisor)
Maia, Filipe,ProfessorUppsala universitet,Molekylär biofysik(Swepub:uu)fimai103 (thesis advisor)
Barty, Anton,Dr.Deutsches Elektronen-Synchrotron (DESY) Notkestrasse 85, 22607 Hamburg, Germany (opponent)
Uppsala universitetMolekylär biofysik (creator_code:org_t)

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