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AlphaFold2 用于蛋白质结构预测:最佳实践和关键分析。

AlphaFold2 for Protein Structure Prediction: Best Practices and Critical Analyses.

机构信息

Université Paris Cité and Université des Antilles and Université de la Réunion, BIGR, UMR_S1134, DSIMB Team, Inserm, Paris, France.

出版信息

Methods Mol Biol. 2024;2836:235-252. doi: 10.1007/978-1-0716-4007-4_13.


DOI:10.1007/978-1-0716-4007-4_13
PMID:38995544
Abstract

AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design, and the elucidation of disease mechanisms. Many scientists now use AF2 on a daily basis, including non-specialist users. This chapter is aimed at the latter. Tips and tricks for getting the most out of AF2 to produce a high-quality biological model are discussed here. We suggest to non-specialist users how to maintain a critical perspective when working with AF2 models and provide guidelines on how to properly evaluate them. After showing how to perform our own structure prediction using ColabFold, we list several ways to improve AF2 models by adding information that is missing from the original AF2 model. By using software such as AlphaFill to add cofactors and ligands to the models, or MODELLER to add disulfide bridges between cysteines, we guide users to build a high-quality biological model suitable for applications such as drug design, protein interaction, or molecular dynamics studies.

摘要

近年来,AlphaFold2(AF2)作为一项突破性的创新技术出现,彻底改变了多个科学领域,特别是结构生物学、药物设计和疾病机制的阐明。现在,许多科学家包括非专业用户都在日常使用 AF2。本章针对的是后者。这里讨论了一些技巧和窍门,可帮助非专业用户充分利用 AF2 来生成高质量的生物学模型。我们建议非专业用户在使用 AF2 模型时保持批判性思维,并提供关于如何正确评估它们的指导方针。在展示如何使用 ColabFold 进行我们自己的结构预测之后,我们列出了通过添加原始 AF2 模型中缺失的信息来改进 AF2 模型的几种方法。通过使用 AlphaFill 等软件向模型中添加辅助因子和配体,或者使用 MODELLER 添加半胱氨酸之间的二硫键,我们指导用户构建适用于药物设计、蛋白质相互作用或分子动力学研究等应用的高质量生物学模型。

相似文献

[1]
AlphaFold2 for Protein Structure Prediction: Best Practices and Critical Analyses.

Methods Mol Biol. 2024

[2]
Overview of AlphaFold2 and breakthroughs in overcoming its limitations.

Comput Biol Med. 2024-6

[3]
AlphaFold2 and its applications in the fields of biology and medicine.

Signal Transduct Target Ther. 2023-3-14

[4]
AlphaFold2 as a replacement for solution NMR structure determination of small proteins: Not so fast!

J Magn Reson. 2024-7

[5]
Easy and accurate protein structure prediction using ColabFold.

Nat Protoc. 2025-3

[6]
Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data.

J Magn Reson. 2023-7

[7]
Ins and outs of AlphaFold2 transmembrane protein structure predictions.

Cell Mol Life Sci. 2022-1-15

[8]
OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization.

Nat Methods. 2024-8

[9]
High-accuracy protein structure prediction in CASP14.

Proteins. 2021-12

[10]
Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR Data.

bioRxiv. 2023-1-22

引用本文的文献

[1]
New Insights into Bioactive Peptides: Design, Synthesis, Structure-Activity Relationship.

Int J Mol Sci. 2024-12-1

[2]
Special Issue: "Molecular Dynamics Simulations and Structural Analysis of Protein Domains".

Int J Mol Sci. 2024-10-8

本文引用的文献

[1]
Accurate proteome-wide missense variant effect prediction with AlphaMissense.

Science. 2023-9-22

[2]
Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes.

Bioinformatics. 2023-7-1

[3]
Evolutionary-scale prediction of atomic-level protein structure with a language model.

Science. 2023-3-17

[4]
AlphaFold2 protein structure prediction: Implications for drug discovery.

Curr Opin Struct Biol. 2023-2

[5]
AlphaFill: enriching AlphaFold models with ligands and cofactors.

Nat Methods. 2023-2

[6]
ColabFold: making protein folding accessible to all.

Nat Methods. 2022-6

[7]
Improved prediction of protein-protein interactions using AlphaFold2.

Nat Commun. 2022-3-10

[8]
Database resources of the national center for biotechnology information.

Nucleic Acids Res. 2022-1-7

[9]
Critical assessment of methods of protein structure prediction (CASP)-Round XIV.

Proteins. 2021-12

[10]
Highly accurate protein structure prediction for the human proteome.

Nature. 2021-8

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