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

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.

Keywords: AI; Artificial intelligence; Cryo-EM; Deep learning; Structural heterogeneity; Structure modeling; Structure validation.