Academic Journal
AlignPCA-2D: PCA-reduced Euclidean vector alignment for 2D classification in cryo-EM.
| Τίτλος: | AlignPCA-2D: PCA-reduced Euclidean vector alignment for 2D classification in cryo-EM. |
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| Συγγραφείς: | Ramírez-Aportela E; Centro Nacional de Biotecnología (CSIC), C/Darwin 3, 28049 Cantoblanco, Madrid, Spain., Zarrabeitia OL; Centro Nacional de Biotecnología (CSIC), C/Darwin 3, 28049 Cantoblanco, Madrid, Spain., Fonseca YC; Centro Nacional de Biotecnología (CSIC), C/Darwin 3, 28049 Cantoblanco, Madrid, Spain., Ceska T; Gandeeva Therapeutics Inc., Vancouver, British Columbia, Canada., Subramaniam S; Gandeeva Therapeutics Inc., Vancouver, British Columbia, Canada.; Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, British Columbia, Canada., Carazo JM; Centro Nacional de Biotecnología (CSIC), C/Darwin 3, 28049 Cantoblanco, Madrid, Spain., Sorzano COS; Centro Nacional de Biotecnología (CSIC), C/Darwin 3, 28049 Cantoblanco, Madrid, Spain. |
| Πηγή: | Acta crystallographica. Section D, Structural biology [Acta Crystallogr D Struct Biol] 2026 Jul 01; Vol. 82 (Pt 7), pp. 727-739. Date of Electronic Publication: 2026 Jun 08. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: John Wiley & Sons Country of Publication: United States NLM ID: 101676043 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2059-7983 (Electronic) Linking ISSN: 20597983 NLM ISO Abbreviation: Acta Crystallogr D Struct Biol Subsets: MEDLINE |
| Imprint Name(s): | Publication: <2018-> : Medford, MA : John Wiley & Sons Original Publication: [Malden, MA] : John Wiley & Sons, [2016]- |
| Ιατρικοί όροι (MeSH): | Cryoelectron Microscopy*/methods , Image Processing, Computer-Assisted*/methods , Macromolecular Substances*/chemistry , Software* , Principal Component Analysis*, Algorithms |
| Περίληψη: | Cryogenic electron microscopy (cryo-EM) has transformed structural biology by enabling the high-resolution reconstruction of macromolecular complexes from noisy projection images. However, the intrinsic heterogeneity and low signal-to-noise ratio of cryo-EM datasets make 2D classification a critical and computationally demanding step in the processing workflow. Here, we introduce AlignPCA-2D, a principal component analysis (PCA)-space Euclidean vector alignment method for fast, interpretable 2D classification in cryo-EM. By projecting particle images and class representations into a compressed latent PCA space, AlignPCA-2D reduces data dimensionality while preserving meaningful structural variability. The image-to-class assignment is then performed using Euclidean distance, enabling efficient and accurate classification. We benchmark AlignPCA-2D against established cryo-EM software, such as RELION and CryoSPARC, and demonstrate that it achieves competitive alignment accuracy while substantially reducing computational cost. This approach provides a lightweight alternative for large-scale 2D classification tasks, and its modular design makes it compatible with existing cryo-EM processing pipelines. (open access.) |
| Grant Information: | BDNS/716450 Ministerio de Ciencia, Innovación y Universidades; S2022/BMD-7232 Comunidad de Madrid; LCF/BQ/DR24/1208003 la Caixa Foundation; 20220678 Gandeeva Therapeutics |
| Contributed Indexing: | Keywords: 3D reconstruction and image processing; AlignPCA-2D; single-particle cryoEM |
| Substance Nomenclature: | 0 (Macromolecular Substances) |
| Entry Date(s): | Date Created: 20260608 Date Completed: 20260630 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13317688 |
| DOI: | 10.1107/S2059798326004572 |
| PMID: | 42257246 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2059-7983 |
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| DOI: | 10.1107/S2059798326004572 |