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Direct Atomistic Reconstruction in Homogeneous Cryo-EM Using Protein Geometry Regularization
KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Numerisk analys, optimeringslära och systemteori.
Univ Calif Los Angeles, Los Angeles, CA 90095 USA.
KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Numerisk analys, optimeringslära och systemteori.ORCID-id: 0000-0002-1118-6483
2025 (engelsk)Inngår i: 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, s. 173-184Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer Nature , 2025. Vol. 15667, s. 173-184
Serie
Lecture Notes in Computer Science, ISSN 0302-9743
Emneord [en]
Cryo-EM, Atomistic reconstruction, Regularization
HSV kategori
Identifikatorer
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 (tryckt)ISBN: 978-3-031-92366-1 (tryckt)OAI: oai:DiVA.org:kth-373367DiVA, id: diva2:2020365
Konferanse
10th International Conference on Scale Space and Variational Methods in Computer Vision-SSVM, MAY 18-22, 2025, Dartington, ENGLAND
Merknad

QC 20251210

Tilgjengelig fra: 2025-12-10 Laget: 2025-12-10 Sist oppdatert: 2025-12-10bibliografisk kontrollert

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