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腾讯登录张强峰博士Nature：解析全基因组蛋白相互作用
| 导读 | 来自美国哥伦比亚大学，同济大学等处的研究人员发表了题为“Structure-based prediction of protein–protein interactions on a genome-wide scale”的文章，提出了一种基于三维结构信息的全基因组蛋白质相互作用计算预测方法：PrePPI，这将有助于解析蛋白之间无论是近的还是远的几何关系。相关成果公布在Nature杂志上。<!... |
来自美国哥伦比亚大学，同济大学等处的研究人员发表了题为“Structure-based prediction of protein–protein interactions on a genome-wide scale”的文章，提出了一种基于三维结构信息的全基因组蛋白质相互作用计算预测方法：PrePPI，这将有助于解析蛋白之间无论是近的还是远的几何关系。相关成果公布在Nature杂志上。<!--more-->
文章的第一作者为美国哥伦比亚大学的张强峰博士，同济大学电子与信息工程学院计算机科学与技术系博士生邓磊也参与了此项研究。文章的通讯作者为哥伦比亚大学Barry Honig教授等，Honig教授是美国哥伦比亚大学计算生物与生物信息中心主任、教授，美国科学院院士，霍华德•休斯医学研究所（HHMI）研究员，PNAS等著名国际期刊编辑。
蛋白质相互作用研究能够从分子水平上揭示蛋白质的功能，帮助揭示生长发育、新陈代谢、分化和凋亡等细胞活动的规律。在全基因组范围内识别蛋白质相互作用对是解释细胞调控机制的重要一步。
随着蛋白质相互作用实验技术的发展，人们能够获得大量的蛋白质相互作用数据，甚至能够在全基因组范围内对蛋白质相互作用进行分析。然而，由于实验技术的限制，很多高通量实验方法测得的蛋白质相互作用数据的错误率都比较高。并且，由于待测的蛋白质对数目巨大，若要对其逐一验证，需要花费大量的人力和物力。
在这篇文章中，研究人员提出了一种基于三维结构信息的全基因组蛋白质相互作用计算预测方法：PrePPI。这种方法基于蛋白的三维结构，能提供精确，覆盖率高的预测信息。
研究结果表明基于结构信息的相互作用预测方法无论是在准确率还是在覆盖度上均大大优于基于非结构信息的方法。使用三维结构信息在预测相互作用方面所取得的惊人的效果，归功于大量使用了同源模型以及结合了蛋白质之间较近和较远的几何关系。此外，PrePPI还集成了其它多种功能线索，其预测结果甚至可以与高通量实验相媲美。
<br/><strong>原文摘要：</strong><br/>
Structure-based prediction of protein–protein interactions on a genome-wide scale
The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms1, 2. Much of our present knowledge derives from high-throughput techniques such as the yeast two-hybrid assay and affinity purification3, as well as from manual curation of experiments on individual systems4. A variety of computational approaches based, for example, on sequence homology, gene co-expression and phylogenetic profiles, have also been developed for the genome-wide inference of protein–protein interactions (PPIs)5, 6. Yet comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages7, 8, 9. Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, termed PrePPI, which combines structural information with other functional clues, is comparable in accuracy to high-throughput experiments, yielding over 30,000 high-confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of considerable biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins.
<br/>来源：生物通
文章的第一作者为美国哥伦比亚大学的张强峰博士，同济大学电子与信息工程学院计算机科学与技术系博士生邓磊也参与了此项研究。文章的通讯作者为哥伦比亚大学Barry Honig教授等，Honig教授是美国哥伦比亚大学计算生物与生物信息中心主任、教授，美国科学院院士，霍华德•休斯医学研究所（HHMI）研究员，PNAS等著名国际期刊编辑。
蛋白质相互作用研究能够从分子水平上揭示蛋白质的功能，帮助揭示生长发育、新陈代谢、分化和凋亡等细胞活动的规律。在全基因组范围内识别蛋白质相互作用对是解释细胞调控机制的重要一步。
随着蛋白质相互作用实验技术的发展，人们能够获得大量的蛋白质相互作用数据，甚至能够在全基因组范围内对蛋白质相互作用进行分析。然而，由于实验技术的限制，很多高通量实验方法测得的蛋白质相互作用数据的错误率都比较高。并且，由于待测的蛋白质对数目巨大，若要对其逐一验证，需要花费大量的人力和物力。
在这篇文章中，研究人员提出了一种基于三维结构信息的全基因组蛋白质相互作用计算预测方法：PrePPI。这种方法基于蛋白的三维结构，能提供精确，覆盖率高的预测信息。
研究结果表明基于结构信息的相互作用预测方法无论是在准确率还是在覆盖度上均大大优于基于非结构信息的方法。使用三维结构信息在预测相互作用方面所取得的惊人的效果，归功于大量使用了同源模型以及结合了蛋白质之间较近和较远的几何关系。此外，PrePPI还集成了其它多种功能线索，其预测结果甚至可以与高通量实验相媲美。
<br/><strong>原文摘要：</strong><br/>
Structure-based prediction of protein–protein interactions on a genome-wide scale
The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms1, 2. Much of our present knowledge derives from high-throughput techniques such as the yeast two-hybrid assay and affinity purification3, as well as from manual curation of experiments on individual systems4. A variety of computational approaches based, for example, on sequence homology, gene co-expression and phylogenetic profiles, have also been developed for the genome-wide inference of protein–protein interactions (PPIs)5, 6. Yet comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages7, 8, 9. Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, termed PrePPI, which combines structural information with other functional clues, is comparable in accuracy to high-throughput experiments, yielding over 30,000 high-confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of considerable biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins.
<br/>来源：生物通
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