DiIorio Megan C, Kulczyk Arkadiusz W
Institute for Quantitative Biomedicine, Rutgers University, 174 Frelinghuysen Road, Piscataway, NJ 08854, USA.
Department of Biochemistry & Microbiology, Rutgers University, 76 Lipman Drive, New Brunswick, NJ 08901, USA.
Micromachines (Basel). 2023 Aug 27;14(9):1674. doi: 10.3390/mi14091674.
Single particle cryo-electron microscopy (cryo-EM) has emerged as the prevailing method for near-atomic structure determination, shedding light on the important molecular mechanisms of biological macromolecules. However, the inherent dynamics and structural variability of biological complexes coupled with the large number of experimental images generated by a cryo-EM experiment make data processing nontrivial. In particular, ab initio reconstruction and atomic model building remain major bottlenecks that demand substantial computational resources and manual intervention. Approaches utilizing recent innovations in artificial intelligence (AI) technology, particularly deep learning, have the potential to overcome the limitations that cannot be adequately addressed by traditional image processing approaches. Here, we review newly proposed AI-based methods for ab initio volume generation, heterogeneous 3D reconstruction, and atomic model building. We highlight the advancements made by the implementation of AI methods, as well as discuss remaining limitations and areas for future development.
单颗粒冷冻电子显微镜(cryo-EM)已成为近原子结构测定的主流方法,为生物大分子的重要分子机制提供了线索。然而,生物复合物固有的动力学和结构变异性,再加上冷冻电镜实验产生的大量实验图像,使得数据处理并非易事。特别是,从头重建和原子模型构建仍然是主要瓶颈,需要大量的计算资源和人工干预。利用人工智能(AI)技术最新创新,特别是深度学习的方法,有可能克服传统图像处理方法无法充分解决的局限性。在这里,我们回顾了新提出的基于AI的从头生成体积、异质三维重建和原子模型构建方法。我们强调了AI方法实施所取得的进展,并讨论了仍然存在的局限性和未来发展的领域。