configuring and utilizing cryosieve for enhanced particle sieving in cryo-electron microscopy

Enhancing Cryo-EM Map Quality with CryoSieve Particle Selection

Cryogenic electron microscopy (cryo-EM) single-particle analysis (SPA) has become a dominant method to determine high-resolution three-dimensional density maps of biological macromolecules. Due to a series of technological innovations1,2,3,4,5,6, named resolution revolution7, cryo-EM has the capability to determine the structures of biological macromolecules with up to atomic resolution at an unprecedented rate. This breakthrough marks the beginning of a new era in molecular insights, overtaking X-ray crystallography as the predominant technique and answering longstanding biological questions.
Cryo-EM SPA diverges from X-ray crystallography by not requiring the crystallization of biological macromolecules. Instead, a solution containing the target biological macromolecules is rapidly frozen in vitreous ice. It is then imaged with an electron beam to produce a series of micrographs, bypassing the need for crystallization8. Subsequently, particle-picking algorithms are utilized to extract individual raw particles from these micrographs4,9,10,11,12. As cryo-EM does not depend on crystallization, it is natural that extracted particles are predominantly damaged or in undesired conformational states, necessitating multiple rounds of particle selection to achieve a high-resolution density map. In cryo-EM SPA image processing, particle selection is therefore crucial for obtaining high-resolution density maps13.
In cryo-EM SPA, standard particle selection methods include two-dimensional (2D) and three-dimensional (3D) classification14. 2D classification categorizes particles into a predefined number of groups, yielding an average image and an estimated 2D resolution for each class. Researchers can then visually inspect these classes, removing particles from lower resolution groups to use the remaining ones in reconstructions aimed at achieving higher resolution. Once particle poses are established using refinement algorithms, researchers will proceed with 3D classification, clustering particles into multiple classes. This enables visual inspection of the reconstructed density map for each class, allowing for the exclusion of undesirable particles, such as those from undesired conformations. Following multiple rounds of classification, a final stack comprising relatively high-quality particles is obtained. These final stacks are instrumental in producing atomic or near-atomic resolution density maps.
Zhu and her colleagues have demonstrated that further particle selection can be conducted on these final stacks15. CryoSieve15, an innovative iterative method for particle selection, can be applied to enhance the quality of the final density map by significantly reducing the number of particles. While other particle sorting criteria and software, such as the normalized cross-correlation (NCC) method16, the angular graph consistency (AGC) approach17, and non-alignment classification5, are currently in use within the field, this method has been shown to outperform these algorithms in terms of effectiveness.
In this study, we present a detailed guide to the entire process. As a case study, we applied this new method to the dataset of the influenza hemagglutinin trimer (EMPIAR entry: 10097)18, which includes 130,000 particles in its final stack. Our procedure successfully discarded about 73.8% of the particles from the final stack of this dataset, improving the resolution of the reconstructed density map from 4.11 &#197; to 3.62 &#197;. In addition to the influenza hemagglutinin trimer, results from multiple datasets are presented in earlier publication15, showcasing a variety of resolutions and molecular weights of biomolecules.

Author Spotlight: Optimizing Cryo-EM Analysis with CryoSieve for Enhanced Particle Selection Efficiency

Enhancing Density Maps by Removing the Majority of Particles in Single Particle Cryogenic Electron Microscopy Final Stacks

Cryogenic electron microscopy is essential for determining near-atomic structures of biological macromolecules. Despite its reliance on averaging numerous low-signal-to-noise images, the optimum number of particles needed for specific resolution remains unknown. This limitation hinders progress in sample analysis and preparation methods.To tackle this, we introduce an iterative sorting method, CryoSieve. Standard protocol selection includes two-dimensional and three-dimensional classification, other protocol sorting criteria such as the normalized cross-correlation method, the angular graph consistency approach, and the non-alignment classification are currently in use. Extensive experiments demonstrate that CryoSieve outperforms other cryo-EM particle sorting algorithms, revealing that most particles are unnecessary in final stacks.The minority of particles remaining in the final stacks yields very high-resolution amplitude in reconstructive density maps. For some data sets, the size of the finest subset approaches the theoretical limit. In cryo-EM, sample preparation hinders workflow.Due to the lack of standard metrics for protocol comparison, the ratio of selected to collected particles could serve as a quality metric. Examining their spatial and temporal distribution may also highlight key physical factors in preparation effectiveness.

Configuring and Utilizing CryoSieve for Enhanced Particle Sieving in Cryo-Electron Microscopy

configuring-utilizing-cryosieve-for-enhanced-particle-sieving-cryo

Research

JoVE Journal

Biology

186 조회수.  Shenzhen Medical Academy of Research and Translation. GPU 가속 환경을 구성하려면 터미널을 열고 nvidia-smi 명령을 입력합니다. 명령이 GPU 카드에 대한 모든 정보를 성공적으로 표시하고 CUDA 버전이 10.2보다 높은지 확인합니다. 그런 다음 conda V 명령을 실행하여 Conda 설치를 확인합니다.다음 명령을 입력합니다. 가상 환경을 설정하고 환경이 성공적으로 구성될 때까지 몇 분 동안 기다립니다. conda activate CRYOSIEVE_ENV 명령을 실행?...