AI-based methods for biomolecular structure modeling for Cryo-EM
Farhanaz Farheen, Genki Terashi, Han Zhu, Daisuke Kihara
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. • Cryo-EM structure determination involves numerous computational data processing steps. • Many AI-based methods have been developed for cryo-EM data processing steps. • AI-based methods offer accurate, fast, or often unique capabilities for cryo-EM. • We discuss notable AI-based methods in seven key data processing steps in cryo-EM.