Title: Selecting XFEL single-particle snapshots by geometric machine learning

Abstract

A promising new route for structural biology is single-particle imaging with an X-ray Free-Electron Laser (XFEL). This method has the advantage that the samples do not require crystallization and can be examined at room temperature. However, high-resolution structures can only be obtained from a sufficiently large number of diffraction patterns of individual molecules, so-called single particles. Here, we present a method that allows for efficient identification of single particles in very large XFEL datasets, operates at low signal levels, and is tolerant to background. This method uses supervised Geometric Machine Learning (GML) to extract low-dimensional feature vectors from a training dataset, fuse test datasets into the feature space of training datasets, and separate the data into binary distributions of “single particles” and “non-single particles.” As a proof of principle, we tested simulated and experimental datasets of the Coliphage PR772 virus. We created a training dataset and classified three types of test datasets: First, a noise-free simulated test dataset, which gave near perfect separation. Second, simulated test datasets that were modified to reflect different levels of photon counts and background noise. These modified datasets were used to quantify the predictive limits of our approach. Third, an experimental dataset collected at themore » Stanford Linear Accelerator Center. The single-particle identification for this experimental dataset was compared with previously published results and it was found that GML covers a wide photon-count range, outperforming other single-particle identification methods. Moreover, a major advantage of GML is its ability to retrieve single particles in the presence of structural variability.« less

Authors:

Cruz-Chú, Eduardo R.  ^[1];

• Search DOE PAGES for author "Cruz-Chú, Eduardo R."
• Search DOE PAGES for ORCID "0000-0001-5797-9238"

Hosseinizadeh, Ahmad ^[1]; Mashayekhi, Ghoncheh ^[1]; Fung, Russell ^[1]; Ourmazd, Abbas ^[1]; Schwander, Peter  ^[1]

• Search DOE PAGES for author "Schwander, Peter"
• Search DOE PAGES for ORCID "0000-0003-2350-8838"

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+ Show Author Affiliations

1. Univ. of Wisconsin, Milwaukee, WI (United States). Dept. of Physics

Publication Date:

Thu Feb 18 00:00:00 EST 2021

Research Org.:

Univ. of Wisconsin, Milwaukee, WI (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)

OSTI Identifier:

1817031

Alternate Identifier(s):

OSTI ID: 1766112

Grant/Contract Number:  

SC0002164; STC 1231306; DBI-2029533

Resource Type:

Accepted Manuscript

Journal Name:

Structural Dynamics

Additional Journal Information:

Journal Volume: 8; Journal Issue: 1; Journal ID: ISSN 2329-7778

Publisher:

American Crystallographic Association/AIP

Country of Publication:

United States

Language:

English

Subject:

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS