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本文引用的文献

1
ACMG/AMP interpretation of BRCA1 missense variants: Structure-informed scores add evidence strength granularity to the PP3/BP4 computational evidence.ACMG/AMP对BRCA1错义变异的解读:基于结构的评分增加了PP3/BP4计算证据的证据强度粒度。
Am J Hum Genet. 2025 May 1;112(5):993-1002. doi: 10.1016/j.ajhg.2024.12.011. Epub 2025 Apr 14.
2
Integration of protein stability and AlphaMissense scores improves bioinformatic impact prediction for p53 missense and in-frame amino acid deletion variants.蛋白质稳定性与AlphaMissense评分的整合改善了对p53错义及框内氨基酸缺失变体的生物信息学影响预测。
Am J Hum Genet. 2025 May 1;112(5):1003-1014. doi: 10.1016/j.ajhg.2025.01.012. Epub 2025 Apr 14.
3
Leveraging protein structural information to improve variant effect prediction.利用蛋白质结构信息改进变异效应预测。
Curr Opin Struct Biol. 2025 Jun;92:103023. doi: 10.1016/j.sbi.2025.103023. Epub 2025 Feb 22.
4
MaveDB 2024: a curated community database with over seven million variant effects from multiplexed functional assays.MaveDB 2024:一个经过整理的社区数据库,包含来自多重功能测定的超过700万个变异效应。
Genome Biol. 2025 Jan 21;26(1):13. doi: 10.1186/s13059-025-03476-y.
5
Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3.附录:使用AlphaFold 3对生物分子相互作用进行准确的结构预测。
Nature. 2024 Dec;636(8042):E4. doi: 10.1038/s41586-024-08416-7.
6
Calibration of variant effect predictors on genome-wide data masks heterogeneous performance across genes.在全基因组数据上对变异效应预测因子进行校准会掩盖基因间异质性的性能。
Am J Hum Genet. 2024 Sep 5;111(9):2031-2043. doi: 10.1016/j.ajhg.2024.07.018. Epub 2024 Aug 21.
7
Evidence-based recommendations for gene-specific ACMG/AMP variant classification from the ClinGen ENIGMA BRCA1 and BRCA2 Variant Curation Expert Panel.基于证据的基因特异性 ACMG/AMP 变异分类推荐意见,来自 ClinGen ENIGMA BRCA1 和 BRCA2 变异 curation 专家小组。
Am J Hum Genet. 2024 Sep 5;111(9):2044-2058. doi: 10.1016/j.ajhg.2024.07.013. Epub 2024 Aug 13.
8
Structural and functional prediction, evaluation, and validation in the post-sequencing era.测序后时代的结构与功能预测、评估及验证
Comput Struct Biotechnol J. 2023 Dec 25;23:446-451. doi: 10.1016/j.csbj.2023.12.031. eCollection 2024 Dec.
9
Accurate proteome-wide missense variant effect prediction with AlphaMissense.使用 AlphaMissense 进行精确的全蛋白质错义变异效应预测。
Science. 2023 Sep 22;381(6664):eadg7492. doi: 10.1126/science.adg7492.
10
Correspondence between functional scores from deep mutational scans and predicted effects on protein stability.深突变扫描的功能评分与预测对蛋白质稳定性影响之间的对应关系。
Protein Sci. 2023 Jul;32(7):e4688. doi: 10.1002/pro.4688.

变异解读中的结构生物学:两项研究的观点与实践

Structural biology in variant interpretation: Perspectives and practices from two studies.

作者信息

Varga Matthew J, Richardson Marcy E, Chamberlin Adam

机构信息

Ambry Genetics, Aliso Viejo, CA, USA.

Ambry Genetics, Aliso Viejo, CA, USA.

出版信息

Am J Hum Genet. 2025 May 1;112(5):984-992. doi: 10.1016/j.ajhg.2025.03.010. Epub 2025 Apr 14.

DOI:10.1016/j.ajhg.2025.03.010
PMID:40233741
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12120175/
Abstract

Structural biology offers a powerful lens through which to assess genetic variants by providing insights into their impact on clinically relevant protein structure and function. Due to the availability of new, user-friendly, web-based tools, structural analyses by wider audiences have become more mainstream. These new tools, including AlphaMissense and AlphaFold, have recently been in the limelight due to their initial success and projected future promise; however, the intricacies and limitations of using these tools still need to be disseminated to the more general audience that is likely to use them in variant analysis. Here, we expound on frameworks applying structural biology to variant interpretation by examining two accompanying articles. To this end, we explore the nuances of choosing the correct protein model, compare and contrast various structural approaches, and highlight both the advantages and limitations of employing structural biology in variant interpretation. Using two articles published in this issue of The American Journal of Human Genetics as a baseline, we focus on case studies in TP53 and BRCA1 to illuminate gene-specific differences in the applications of structural information, which illustrate the complexities inherent in this field. Additionally, we discuss the implications of recent advancements, such as AlphaFold, and provide practical guidance for researchers navigating variant interpretation using structural biology.

摘要

结构生物学提供了一个强大的视角,通过深入了解基因变异对临床相关蛋白质结构和功能的影响来评估这些变异。由于新的、用户友好的基于网络的工具的出现,更广泛的受众进行结构分析已变得更加主流。这些新工具,包括AlphaMissense和AlphaFold,由于其初步成功和预期的未来前景,最近备受关注;然而,使用这些工具的复杂性和局限性仍需要向更可能在变异分析中使用它们的普通受众进行传播。在此,我们通过审视两篇配套文章阐述了将结构生物学应用于变异解读的框架。为此,我们探讨选择正确蛋白质模型的细微差别,比较和对比各种结构方法,并强调在变异解读中采用结构生物学的优势和局限性。以本期《美国人类遗传学杂志》发表 的两篇文章为基线,我们重点关注TP53和BRCA1的案例研究,以阐明结构信息应用中的基因特异性差异,这说明了该领域固有的复杂性。此外,我们讨论了诸如AlphaFold等最新进展的影响,并为使用结构生物学进行变异解读的研究人员提供实用指导。