AI-based methods for biomolecular structure modeling for Cryo-EM.

Journal: Current opinion in structural biology
PMID: 39864242

Abstract

Cryo-electron microscopy (Cryo-EM) has revolutionized structural biology by enabling the determination of macromolecular structures that were challenging to study with conventional methods. Processing cryo-EM data involves several computational steps to derive three-dimensional structures from raw projections. Recent advancements in artificial intelligence (AI) including deep learning have significantly improved the performance of these processes. In this review, we discuss state-of-the-art AI-based techniques used in key steps of cryo-EM data processing, including macromolecular structure modeling and heterogeneity analysis.

Authors

  • Farhanaz Farheen
    Department of Computer Science, Purdue University, West Lafayette, IN, USA.
  • Genki Terashi
    Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
  • Han Zhu
    College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China.
  • Daisuke Kihara
    Department of Computer Science and Department of Biological Science, Purdue University, West Lafayette, IN 47907, USA Department of Computer Science and Department of Biological Science, Purdue University, West Lafayette, IN 47907, USA.

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