Approximating deformation fields for the analysis of continuous heterogeneity of biological macromolecules by 3D Zernike polynomials
Status PubMed-not-MEDLINE Jazyk angličtina Země Anglie, Velká Británie Médium electronic-ecollection
Typ dokumentu časopisecké články
Grantová podpora
P41 GM103712
NIGMS NIH HHS - United States
R01 GM136780
NIGMS NIH HHS - United States
PubMed
34804551
PubMed Central
PMC8562670
DOI
10.1107/s2052252521008903
PII: S2052252521008903
Knihovny.cz E-zdroje
- Klíčová slova
- 3D reconstruction and image processing, Zernike polynomials, conformations, multi-dimensional scaling (MDS), single-particle cryo-EM, spherical harmonics,
- Publikační typ
- časopisecké články MeSH
Structural biology has evolved greatly due to the advances introduced in fields like electron microscopy. This image-capturing technique, combined with improved algorithms and current data processing software, allows the recovery of different conformational states of a macromolecule, opening new possibilities for the study of its flexibility and dynamic events. However, the ensemble analysis of these different conformations, and in particular their placement into a common variable space in which the differences and similarities can be easily recognized, is not an easy matter. To simplify the analysis of continuous heterogeneity data, this work proposes a new automatic algorithm that relies on a mathematical basis defined over the sphere to estimate the deformation fields describing conformational transitions among different structures. Thanks to the approximation of these deformation fields, it is possible to describe the forces acting on the molecules due to the presence of different motions. It is also possible to represent and compare several structures in a low-dimensional mapping, which summarizes the structural characteristics of different states. All these analyses are integrated into a common framework, providing the user with the ability to combine them seamlessly. In addition, this new approach is a significant step forward compared with principal component analysis and normal mode analysis of cryo-electron microscopy maps, avoiding the need to select components or modes and producing localized analysis.
Centro Nacional de Biotecnologia CSIC C Darwin 3 Cantoblanco Madrid 28049 Spain
Department of Computational and Systems Biology University of Pittsburgh Pennsylvania USA
Department of Statistics and Data Science Yale University New Haven Connecticut USA
Faculty of Informatics Masaryk University Botanická 68a 60200 Brno Czech Republic
Institute of Computer Science Masaryk University Botanická 68a 60200 Brno Czech Republic
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