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GTalign:基于空间索引的蛋白质结构比对、叠加和搜索。

GTalign: spatial index-driven protein structure alignment, superposition, and search.

机构信息

Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania.

出版信息

Nat Commun. 2024 Aug 24;15(1):7305. doi: 10.1038/s41467-024-51669-z.

DOI:10.1038/s41467-024-51669-z
PMID:39181863
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11344802/
Abstract

With protein databases growing rapidly due to advances in structural and computational biology, the ability to accurately align and rapidly search protein structures has become essential for biological research. In response to the challenge posed by vast protein structure repositories, GTalign offers an innovative solution to protein structure alignment and search-an algorithm that achieves optimal superposition at high speeds. Through the design and implementation of spatial structure indexing, GTalign parallelizes all stages of superposition search across residues and protein structure pairs, yielding rapid identification of optimal superpositions. Rigorous evaluation across diverse datasets reveals GTalign as the most accurate among structure aligners while presenting orders of magnitude in speedup at state-of-the-art accuracy. GTalign's high speed and accuracy make it useful for numerous applications, including functional inference, evolutionary analyses, protein design, and drug discovery, contributing to advancing understanding of protein structure and function.

摘要

随着结构和计算生物学的进步,蛋白质数据库迅速增长,准确对齐和快速搜索蛋白质结构已成为生物研究的关键。针对庞大的蛋白质结构库带来的挑战,GTalign 提供了一种创新的蛋白质结构对齐和搜索解决方案——一种高速实现最佳叠加的算法。通过设计和实现空间结构索引,GTalign 对残基和蛋白质结构对的所有叠加搜索阶段进行并行化,从而快速确定最佳叠加。在各种数据集上的严格评估表明,GTalign 是结构对齐器中最准确的,同时在当前最先进的精度水平上实现了数量级的加速。GTalign 的高速和准确性使其在许多应用中非常有用,包括功能推断、进化分析、蛋白质设计和药物发现,有助于提高对蛋白质结构和功能的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3337/11344802/0a125d61135d/41467_2024_51669_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3337/11344802/4f58fff4245e/41467_2024_51669_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3337/11344802/e022602eaf31/41467_2024_51669_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3337/11344802/e53c4aaaeecc/41467_2024_51669_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3337/11344802/f7a4a8d669da/41467_2024_51669_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3337/11344802/58d7edfdb7d8/41467_2024_51669_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3337/11344802/b07e043527c3/41467_2024_51669_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3337/11344802/0a125d61135d/41467_2024_51669_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3337/11344802/4f58fff4245e/41467_2024_51669_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3337/11344802/e022602eaf31/41467_2024_51669_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3337/11344802/e53c4aaaeecc/41467_2024_51669_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3337/11344802/f7a4a8d669da/41467_2024_51669_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3337/11344802/58d7edfdb7d8/41467_2024_51669_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3337/11344802/b07e043527c3/41467_2024_51669_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3337/11344802/0a125d61135d/41467_2024_51669_Fig7_HTML.jpg

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本文引用的文献

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J Chem Inf Model. 2024 Feb 12;64(3):1043-1049. doi: 10.1021/acs.jcim.3c01455. Epub 2024 Jan 25.
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AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences.2024 年的 AlphaFold 蛋白质结构数据库:为超过 2.14 亿个蛋白质序列提供结构覆盖。
Nucleic Acids Res. 2024 Jan 5;52(D1):D368-D375. doi: 10.1093/nar/gkad1011.
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Unraveling the functional dark matter through global metagenomics.
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Nucleic Acids Res. 2025 Jul 7;53(W1):W291-W296. doi: 10.1093/nar/gkaf398.
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Automated Engineering Protein Dynamics via Loop Grafting: Improving Luciferase Catalysis.通过环移植实现工程蛋白动力学自动化:改善荧光素酶催化作用
ACS Catal. 2025 Feb 11;15(4):3391-3404. doi: 10.1021/acscatal.4c06207. eCollection 2025 Feb 21.
通过全球宏基因组学揭示功能暗物质。
Nature. 2023 Oct;622(7983):594-602. doi: 10.1038/s41586-023-06583-7. Epub 2023 Oct 11.
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Clustering predicted structures at the scale of the known protein universe.对已知蛋白质宇宙尺度的预测结构进行聚类。
Nature. 2023 Oct;622(7983):637-645. doi: 10.1038/s41586-023-06510-w. Epub 2023 Sep 13.
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Uncovering new families and folds in the natural protein universe.揭示自然蛋白质宇宙中的新家族和新折叠。
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