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腾讯登录Cell：通过基因组测序预测膜蛋白的三维结构
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<div> 膜蛋白的存在使细胞与胞外环境或细胞与细胞之间的“交流”得以实现。超过25%的人类蛋白拥有完整的膜结构域，这些蛋白中许多在医学上非常重要，因为几乎一半的药物靶点都包含一个膜结构域。通过膜蛋白的三维结构可以描述它的分子机制和加速以它为靶点的药物分子的研发。</di... |
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<div> 膜蛋白的存在使细胞与胞外环境或细胞与细胞之间的“交流”得以实现。超过25%的人类蛋白拥有完整的膜结构域，这些蛋白中许多在医学上非常重要，因为几乎一半的药物靶点都包含一个膜结构域。通过膜蛋白的三维结构可以描述它的分子机制和加速以它为靶点的药物分子的研发。</div>
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尽管解析蛋白结构的方法有了很大进步，但大部分膜蛋白的三维结构还是未知的。有效而精确的预测膜蛋白三维结构的计算方法将是现存实验方法的重要补充。
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5月10日， Cell在线发表了哈佛医学院等多家科研机构的一篇题为《Three-Dimensional Structures of Membrane Proteins from Genomic Sequencing》的研究文章，作者指出随着大规模测序技术的快速发展，通过最大熵法，更精确、更全面的来自遗传变异的演化限制的蛋白结构信息将被解译，大大拓宽了用于建模的转膜蛋白的目录表。
作者通过最大熵法仅仅通过氨基酸序列预测了过去未知的11个转膜蛋白的三维结构；还通过重新计算来自23个家族的已知转膜蛋白来测试最大熵法，证明了这种方法应用于大分子转膜蛋白的准确性；最后介绍了这种方法怎么用于预测转膜蛋白的寡聚体、功能性位点、构象改变等。
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<img src="http://www.bioon.com/biology/UploadFiles/201205/2012051318440487.jpg" alt="" width="113" height="149" border="0" hspace="0" />
<a title="" href="http://www.cell.com/abstract/S0092-8674(12)00509-0" target="_blank">doi:10.1016/j.cell.2012.04.012</a>
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<br/><strong>Three-Dimensional Structures of Membrane Proteins from Genomic Sequencing</strong><br/>
Thomas A. Hopf， Lucy J. Colwell， Robert Sheridan， Burkhard Rost， Chris Sander， Debora S. MarksSee Affiliations
Hint： Rollover Authors and Affiliations
Department of Systems Biology， Harvard Medical School， Boston， MA 02115， USA
Department of Informatics， Technische University München， 85748 Garching， Germany
MRC Laboratory of Molecular Biology， Hills Road， CB2 0QH Cambridge， UK
Computational Biology Center， Memorial Sloan-Kettering Cancer Center， New York City， NY 10065， USA
Corresponding author
Summary
We show that amino acid covariation in proteins， extracted from the evolutionary sequence record， can be used to fold transmembrane proteins. We use this technique to predict previously unknown 3D structures for 11 transmembrane proteins (with up to 14 helices) from their sequences alone. The prediction method (EVfold_membrane) applies a maximum entropy approach to infer evolutionary covariation in pairs of sequence positions within a protein family and then generates all-atom models with the derived pairwise distance constraints. We benchmark the approach with blinded de novo computation of known transmembrane protein structures from 23 families， demonstrating unprecedented accuracy of the method for large transmembrane proteins. We show how the method can predict oligomerization， functional sites， and conformational changes in transmembrane proteins. With the rapid rise in large-scale sequencing， more accurate and more comprehensive information on evolutionary constraints can be decoded from genetic variation， greatly expanding the repertoire of transmembrane proteins amenable to modeling by this method.
<br/>来源：生物谷
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<div> 膜蛋白的存在使细胞与胞外环境或细胞与细胞之间的“交流”得以实现。超过25%的人类蛋白拥有完整的膜结构域，这些蛋白中许多在医学上非常重要，因为几乎一半的药物靶点都包含一个膜结构域。通过膜蛋白的三维结构可以描述它的分子机制和加速以它为靶点的药物分子的研发。</div>
</div>
<div id="region-column1and2-layout2">
尽管解析蛋白结构的方法有了很大进步，但大部分膜蛋白的三维结构还是未知的。有效而精确的预测膜蛋白三维结构的计算方法将是现存实验方法的重要补充。
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5月10日， Cell在线发表了哈佛医学院等多家科研机构的一篇题为《Three-Dimensional Structures of Membrane Proteins from Genomic Sequencing》的研究文章，作者指出随着大规模测序技术的快速发展，通过最大熵法，更精确、更全面的来自遗传变异的演化限制的蛋白结构信息将被解译，大大拓宽了用于建模的转膜蛋白的目录表。
作者通过最大熵法仅仅通过氨基酸序列预测了过去未知的11个转膜蛋白的三维结构；还通过重新计算来自23个家族的已知转膜蛋白来测试最大熵法，证明了这种方法应用于大分子转膜蛋白的准确性；最后介绍了这种方法怎么用于预测转膜蛋白的寡聚体、功能性位点、构象改变等。
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<div>
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<img src="http://www.bioon.com/biology/UploadFiles/201205/2012051318440487.jpg" alt="" width="113" height="149" border="0" hspace="0" />
<a title="" href="http://www.cell.com/abstract/S0092-8674(12)00509-0" target="_blank">doi:10.1016/j.cell.2012.04.012</a>
PMC:
PMID:
</div>
<div>
<br/><strong>Three-Dimensional Structures of Membrane Proteins from Genomic Sequencing</strong><br/>
Thomas A. Hopf， Lucy J. Colwell， Robert Sheridan， Burkhard Rost， Chris Sander， Debora S. MarksSee Affiliations
Hint： Rollover Authors and Affiliations
Department of Systems Biology， Harvard Medical School， Boston， MA 02115， USA
Department of Informatics， Technische University München， 85748 Garching， Germany
MRC Laboratory of Molecular Biology， Hills Road， CB2 0QH Cambridge， UK
Computational Biology Center， Memorial Sloan-Kettering Cancer Center， New York City， NY 10065， USA
Corresponding author
Summary
We show that amino acid covariation in proteins， extracted from the evolutionary sequence record， can be used to fold transmembrane proteins. We use this technique to predict previously unknown 3D structures for 11 transmembrane proteins (with up to 14 helices) from their sequences alone. The prediction method (EVfold_membrane) applies a maximum entropy approach to infer evolutionary covariation in pairs of sequence positions within a protein family and then generates all-atom models with the derived pairwise distance constraints. We benchmark the approach with blinded de novo computation of known transmembrane protein structures from 23 families， demonstrating unprecedented accuracy of the method for large transmembrane proteins. We show how the method can predict oligomerization， functional sites， and conformational changes in transmembrane proteins. With the rapid rise in large-scale sequencing， more accurate and more comprehensive information on evolutionary constraints can be decoded from genetic variation， greatly expanding the repertoire of transmembrane proteins amenable to modeling by this method.
<br/>来源：生物谷
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