Using sequence similarity networks for visualization of relationships across diverse protein superfamilies.
Using sequence similarity networks for visualization of relationships across diverse protein superfamilies.
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DOI：
10.1371/journal.pone.0004345
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发表时间：
2009
期刊：
影响因子：
3.7
通讯作者：
Babbitt, Patricia C.
中科院分区：
文献类型：
--
作者：
Atkinson, Holly J.;Morris, John H.;Ferrin, Thomas E.;Babbitt, Patricia C.
在异构类型的生物数据的急剧增加，特别是，丰富的新蛋白质序列，需要快速和用户友好的方法来组织这些信息的方式，使功能推断。将序列或结构与功能联系起来的最广泛使用的策略，基于同源性的功能预测，依赖于序列或结构相似性暗示功能相似性的基本假设。扩展这种方法的新工具仍然迫切需要将序列数据与生物信息相关联，以适应问题的真实的复杂性，同时可供实验和计算生物学家使用。为了解决这个问题，我们研究了序列相似性网络的应用，从序列相似性的背景下，可视化蛋白质超家族的功能趋势。使用三个大组的同源蛋白质的不同类型的结构和功能的多样性GPCR和激酶从人类，和巴豆酸酶超家族的酶，我们表明，覆盖网络与正交信息是一个强大的方法，观察功能的主题和揭示离群值。与其他主要方法相比，网络提供了组序列相似性关系的良好表示以及与系统发育树的强视觉和定量相关性，同时能够分析和可视化比树或多序列比对可以容易地容纳的大得多的序列集。我们还定义了这些网络的应用中的重要限制和警告。序列相似性网络作为探索蛋白质超家族的有效工具，在构建可检验的蛋白质结构-功能关系假说方面显示出巨大的潜力。
The dramatic increase in heterogeneous types of biological data—in particular, the abundance of new protein sequences—requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity—GPCRs and kinases from humans, and the crotonase superfamily of enzymes—we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships.
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Pfam：氏族、网络工具和服务。
影响因子：
14.9
作者：
Finn, Robert D.;Mistry, Jaina;Schuster-Bockler, Benjamin;Griffiths-Jones, Sam;Hollich, Volker;Lassmann, Timo;Moxon, Simon;Marshall, Mhairi;Khanna, Ajay;Durbin, Richard;Eddy, Sean R.;Sonnhammer, Erik L. L.;Bateman, Alex
通讯作者：
Bateman, Alex
树突镜：大型系统发育树的互动观众。
影响因子：
3
作者：
Huson, Daniel H.;Richter, Daniel C.;Rausch, Christian;Dezulian, Tobias;Franz, Markus;Rupp, Regula
通讯作者：
Rupp, Regula
影响因子：
5.6
作者：
Goh, CS;Bogan, AA;Cohen, FE
通讯作者：
Cohen, FE
蛋白质同源网络家族揭示了III型和IV型分泌系统的逐步多样化。
影响因子：
4.3
作者：
Medini, Duccio;Covacci, Antonello;Donati, Claudio
通讯作者：
Donati, Claudio
DOI：
10.1126/science.1164772
发表时间：
2008-11-21
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Jaakola VP;Griffith MT;Hanson MA;Cherezov V;Chien EY;Lane JR;Ijzerman AP;Stevens RC
通讯作者：
Stevens RC