Version 1
: Received: 25 August 2024 / Approved: 26 August 2024 / Online: 27 August 2024 (16:38:25 CEST)
How to cite:
Dhakal, A.; Gyawali, R.; Wang, L.; Cheng, J. Artificial Intelligence in Cryo-EM Protein Particle Picking: The Hope, Hype, and Hurdles. Preprints2024, 2024081936. https://doi.org/10.20944/preprints202408.1936.v1
Dhakal, A.; Gyawali, R.; Wang, L.; Cheng, J. Artificial Intelligence in Cryo-EM Protein Particle Picking: The Hope, Hype, and Hurdles. Preprints 2024, 2024081936. https://doi.org/10.20944/preprints202408.1936.v1
Dhakal, A.; Gyawali, R.; Wang, L.; Cheng, J. Artificial Intelligence in Cryo-EM Protein Particle Picking: The Hope, Hype, and Hurdles. Preprints2024, 2024081936. https://doi.org/10.20944/preprints202408.1936.v1
APA Style
Dhakal, A., Gyawali, R., Wang, L., & Cheng, J. (2024). Artificial Intelligence in Cryo-EM Protein Particle Picking: The Hope, Hype, and Hurdles. Preprints. https://doi.org/10.20944/preprints202408.1936.v1
Chicago/Turabian Style
Dhakal, A., Liguo Wang and Jianlin Cheng. 2024 "Artificial Intelligence in Cryo-EM Protein Particle Picking: The Hope, Hype, and Hurdles" Preprints. https://doi.org/10.20944/preprints202408.1936.v1
Abstract
Cryo-electron microscopy (Cryo-EM) has revolutionized structural biology by enabling the determination of high-resolution 3- Dimensional (3D) structures of large biological macromolecules. Protein particle picking, the process of identifying individual protein particles in cryo-EM micrographs for building protein structures, has progressed from manual and template-based methods to sophisticated artificial intelligence (AI)-driven approaches in recent years. This review critically examines the evolution and current state of cryo-EM particle picking methods, with an emphasis on the impact of AI. We conducted a comparative evaluation of popular AI-based particle picking methods, using both general machine learning (ML) metrics and specific cryo-EM structure determination metrics. This analysis involved constructing the 3D density map from the picked protein particles and assessing the obtained resolution and particle orientation diversity, underscoring the significant impact of AI on cryo-EM particle picking. Despite the advancements, we also identified key obstacles, such as handling complex micrographs with small proteins. The analysis provides insights into the future development of more sophisticated and fully automated AI methods in cryo-EM particle recognition.
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
