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

IF 6.1 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Current opinion in structural biology Pub Date : 2025-01-25 DOI:10.1016/j.sbi.2025.102989
Farhanaz Farheen, Genki Terashi, Han Zhu, Daisuke Kihara
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引用次数: 0

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.

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Current opinion in structural biology
Current opinion in structural biology 生物-生化与分子生物学
CiteScore
12.20
自引率
2.90%
发文量
179
审稿时长
6-12 weeks
期刊介绍: Current Opinion in Structural Biology (COSB) aims to stimulate scientifically grounded, interdisciplinary, multi-scale debate and exchange of ideas. It contains polished, concise and timely reviews and opinions, with particular emphasis on those articles published in the past two years. In addition to describing recent trends, the authors are encouraged to give their subjective opinion of the topics discussed. In COSB, we help the reader by providing in a systematic manner: 1. The views of experts on current advances in their field in a clear and readable form. 2. Evaluations of the most interesting papers, annotated by experts, from the great wealth of original publications. [...] The subject of Structural Biology is divided into twelve themed sections, each of which is reviewed once a year. Each issue contains two sections, and the amount of space devoted to each section is related to its importance. -Folding and Binding- Nucleic acids and their protein complexes- Macromolecular Machines- Theory and Simulation- Sequences and Topology- New constructs and expression of proteins- Membranes- Engineering and Design- Carbohydrate-protein interactions and glycosylation- Biophysical and molecular biological methods- Multi-protein assemblies in signalling- Catalysis and Regulation
期刊最新文献
Modern machine learning methods for protein property prediction. Major advances in protein function assignment by remote homolog detection with protein language models - A review. AI-based methods for biomolecular structure modeling for Cryo-EM. Advancing protein structure prediction beyond AlphaFold2. Challenges and compromises: Predicting unbound antibody structures with deep learning.
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