Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy

Megan C. DiIorio, Arkadiusz W. Kulczyk

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number1674
JournalMicromachines
Volume14
Issue number9
DOIs
StatePublished - Sep 2023

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Mechanical Engineering
  • Electrical and Electronic Engineering

Keywords

  • AI
  • AlphaFold2
  • artificial intelligence
  • cryo-EM
  • cryo-electron microscopy
  • deep learning
  • machine learning
  • neural networks

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