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Direct Atomistic Reconstruction in Homogeneous Cryo-EM Using Protein Geometry Regularization
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Numerical Analysis, Optimization and Systems Theory.
Univ Calif Los Angeles, Los Angeles, CA 90095 USA.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Numerical Analysis, Optimization and Systems Theory.ORCID iD: 0000-0002-1118-6483
2025 (English)In: SCALE SPACE AND VARIATIONAL METHODS IN COMPUTER VISION, SSVM 2025, PT I / [ed] Bubba, TA Gaburro, R Gazzola, S Papafitsoros, K Pereyra, M Schonlieb, CB, Springer Nature , 2025, Vol. 15667, p. 173-184Conference paper, Published paper (Refereed)
Abstract [en]

Direct reconstruction of macromolecular structures from cryogenic electron microscopy (Cryo-EM) data has shown to be a challenge both in the homogeneous and heterogeneous setting. In this work we propose a new direct reconstruction method based on a combination of recent developments on protein geometry and orientation estimation. Even though this method is set up for the homogeneous setting, we aim to gain insight into challenges that atomistic methods have been facing for the heterogeneous case. In numerical experiments we observe that the method is able to recover the structure to almost inter-atomic resolution from as few as 100 2D Cryo-EM images due to the strong bias the regularizer gives. We conclude this work with a discussion on how the obtained results indicate possibilities and challenges for the generalization to the heterogeneous case.

Place, publisher, year, edition, pages
Springer Nature , 2025. Vol. 15667, p. 173-184
Series
Lecture Notes in Computer Science, ISSN 0302-9743
Keywords [en]
Cryo-EM, Atomistic reconstruction, Regularization
National Category
Biophysics
Identifiers
URN: urn:nbn:se:kth:diva-373367DOI: 10.1007/978-3-031-92366-1_14ISI: 001539339900014Scopus ID: 2-s2.0-105006846447ISBN: 978-3-031-92365-4 (print)ISBN: 978-3-031-92366-1 (print)OAI: oai:DiVA.org:kth-373367DiVA, id: diva2:2020365
Conference
10th International Conference on Scale Space and Variational Methods in Computer Vision-SSVM, MAY 18-22, 2025, Dartington, ENGLAND
Note

QC 20251210

Available from: 2025-12-10 Created: 2025-12-10 Last updated: 2025-12-10Bibliographically approved

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Krook, JonathanÖktem, Ozan

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