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AligNet:蛋白质-蛋白质相互作用网络的对齐。

AligNet: alignment of protein-protein interaction networks.

机构信息

Department of Mathematics and Computer Science, University of the Balearic Islands, Palma de Mallorca, E-07122, Spain.

Balearic Islands Health Research Institute (IdISBa), Palma de Mallorca, E-07010, Spain.

出版信息

BMC Bioinformatics. 2020 Nov 18;21(Suppl 6):265. doi: 10.1186/s12859-020-3502-1.

DOI:10.1186/s12859-020-3502-1
PMID:33203353
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7672851/
Abstract

BACKGROUND

All molecular functions and biological processes are carried out by groups of proteins that interact with each other. Metaproteomic data continuously generates new proteins whose molecular functions and relations must be discovered. A widely accepted structure to model functional relations between proteins are protein-protein interaction networks (PPIN), and their analysis and alignment has become a key ingredient in the study and prediction of protein-protein interactions, protein function, and evolutionary conserved assembly pathways of protein complexes. Several PPIN aligners have been proposed, but attaining the right balance between network topology and biological information is one of the most difficult and key points in the design of any PPIN alignment algorithm.

RESULTS

Motivated by the challenge of well-balanced and efficient algorithms, we have designed and implemented AligNet, a parameter-free pairwise PPIN alignment algorithm aimed at bridging the gap between topologically efficient and biologically meaningful matchings. A comparison of the results obtained with AligNet and with the best aligners shows that AligNet achieves indeed a good balance between topological and biological matching.

CONCLUSION

In this paper we present AligNet, a new pairwise global PPIN aligner that produces biologically meaningful alignments, by achieving a good balance between structural matching and protein function conservation, and more efficient computations than state-of-the-art tools.

摘要

背景

所有的分子功能和生物过程都是由相互作用的蛋白质群来执行的。元蛋白质组学数据不断产生新的蛋白质,这些蛋白质的分子功能和关系必须被发现。一个被广泛接受的模型来模拟蛋白质之间的功能关系的结构是蛋白质-蛋白质相互作用网络(PPIN),它们的分析和比对已经成为研究和预测蛋白质-蛋白质相互作用、蛋白质功能以及蛋白质复合物进化保守组装途径的关键组成部分。已经提出了几种 PPIN 比对算法,但在网络拓扑和生物信息之间取得恰当的平衡是任何 PPIN 比对算法设计中最困难和关键的要点之一。

结果

受平衡且高效算法的挑战的启发,我们设计并实现了 AligNet,这是一种无参数的成对 PPIN 比对算法,旨在弥合拓扑有效和生物学有意义的比对之间的差距。将 AligNet 的结果与最佳比对算法进行比较表明,AligNet 确实在拓扑和生物学匹配之间实现了良好的平衡。

结论

在本文中,我们提出了 AligNet,这是一种新的成对全局 PPIN 比对算法,通过在结构匹配和蛋白质功能保守之间取得良好的平衡,以及比最先进的工具更高效的计算,产生有生物学意义的比对。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/96b6812b652c/12859_2020_3502_Fig19_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/df397f30c9e1/12859_2020_3502_Fig10_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/96b6812b652c/12859_2020_3502_Fig19_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/de2e2d40be2a/12859_2020_3502_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/75318bf0f776/12859_2020_3502_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/bdd6609a0888/12859_2020_3502_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/7f9ec2e084b0/12859_2020_3502_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/0e02a3f092da/12859_2020_3502_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/7f4511cfcc47/12859_2020_3502_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/fd82b7199e4b/12859_2020_3502_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/571640fb376b/12859_2020_3502_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/90a3b759f045/12859_2020_3502_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/df397f30c9e1/12859_2020_3502_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/3dd4aa5c1cd9/12859_2020_3502_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/122838746b91/12859_2020_3502_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/d67029305c33/12859_2020_3502_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/f7f4a2663448/12859_2020_3502_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/6171ed33cc97/12859_2020_3502_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/6e084698fb63/12859_2020_3502_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/e01e7baa271e/12859_2020_3502_Fig17_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/01eedb7f8ffb/12859_2020_3502_Fig18_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/426e/7672851/96b6812b652c/12859_2020_3502_Fig19_HTML.jpg

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本文引用的文献

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2
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IEEE/ACM Trans Comput Biol Bioinform. 2016 Jul-Aug;13(4):689-705. doi: 10.1109/TCBB.2015.2474391. Epub 2015 Aug 28.
3
L-GRAAL: Lagrangian graphlet-based network aligner.L-GRAAL:基于拉格朗日图元的网络对齐工具。
基于离散蝙蝠算法的成对生物网络比对。
Comput Math Methods Med. 2021 Nov 3;2021:5548993. doi: 10.1155/2021/5548993. eCollection 2021.
4
Alignment of virus-host protein-protein interaction networks by integer linear programming: SARS-CoV-2.基于整数线性规划的病毒-宿主蛋白-蛋白相互作用网络对齐:SARS-CoV-2。
PLoS One. 2020 Dec 7;15(12):e0236304. doi: 10.1371/journal.pone.0236304. eCollection 2020.
5
Alignment of biological networks by integer linear programming: virus-host protein-protein interaction networks.通过整数线性规划对生物网络进行比对:病毒-宿主蛋白质-蛋白质相互作用网络
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4
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Bioinformatics. 2014 Aug 15;30(16):2351-9. doi: 10.1093/bioinformatics/btu307. Epub 2014 May 2.
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9
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