CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net.

Autor: Gyawali, Rajan^1,2 (AUTHOR), Dhakal, Ashwin^1,2 (AUTHOR), Wang, Liguo³ (AUTHOR), Cheng, Jianlin^1,2 (AUTHOR) chengji@missouri.edu

Publikováno v: Briefings in Bioinformatics. Jul2024, Vol. 25 Issue 4, p1-13. 13p.

CryoMAE: Few-Shot Cryo-EM Particle Picking with Masked Autoencoders

Autor: Xu, Chentianye, Zhan, Xueying, Xu, Min

Cryo-electron microscopy (cryo-EM) emerges as a pivotal technology for determining the architecture of cells, viruses, and protein assemblies at near-atomic resolution. Traditional particle picking, a key step in cryo-EM, struggles with manual effort

Externí odkaz: http://arxiv.org/abs/2404.10178

TomoNet: A streamlined cryogenic electron tomography software pipeline with automatic particle picking on flexible lattices

Autor: Hui Wang, Shiqing Liao, Xinye Yu, Jiayan Zhang, Z. Hong Zhou

Publikováno v: Biological Imaging, Vol 4 (2024)

Cryogenic electron tomography (cryoET) is capable of determining in situ biological structures of molecular complexes at near-atomic resolution by averaging half a million subtomograms. While abundant complexes/particles are often clustered in arrays

Externí odkaz: https://doaj.org/article/95e0a663843f47308a978da26fdd9b72

TomoPicker: Annotation-Efficient Particle Picking in cryo-electron Tomograms.

Autor: Uddin MR; Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA., Ahmed AY; Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA., Tahmid T; Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh., Alam ZU; Manning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA 01003, USA., Freyberg Z; Department of Developmental and Molecular Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA., Xu M; Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Publikováno v: BioRxiv : the preprint server for biology [bioRxiv] 2024 Nov 06. Date of Electronic Publication: 2024 Nov 06.

Random Conical Tilt Reconstruction without Particle Picking in Cryo-electron Microscopy

Autor: Lan, Ti-Yen, Boumal, Nicolas, Singer, Amit

We propose a method to reconstruct the 3-D molecular structure from micrographs collected at just one sample tilt angle in the random conical tilt scheme in cryo-electron microscopy. Our method uses autocorrelation analysis on the micrographs to esti

Externí odkaz: http://arxiv.org/abs/2101.03500