膜蛋白的存在使細胞與胞外環(huán)境或細胞與細胞之間的“交流”得以實現(xiàn),。超過25%的人類蛋白擁有完整的膜結(jié)構(gòu)域,，這些蛋白中許多在醫(yī)學(xué)上非常重要,，因為幾乎一半的藥物靶點都包含一個膜結(jié)構(gòu)域,。通過膜蛋白的三維結(jié)構(gòu)可以描述它的分子機制和加速以它為靶點的藥物分子的研發(fā),。
盡管解析蛋白結(jié)構(gòu)的方法有了很大進步,，但大部分膜蛋白的三維結(jié)構(gòu)還是未知的,。有效而精確的預(yù)測膜蛋白三維結(jié)構(gòu)的計算方法將是現(xiàn)存實驗方法的重要補充,。
5月10日， Cell在線發(fā)表了哈佛醫(yī)學(xué)院等多家科研機構(gòu)的一篇題為《Three-Dimensional Structures of Membrane Proteins from Genomic Sequencing》的研究文章,，作者指出隨著大規(guī)模測序技術(shù)的快速發(fā)展,，通過最大熵法，更精確,、更全面的來自遺傳變異的演化限制的蛋白結(jié)構(gòu)信息將被解譯,，大大拓寬了用于建模的轉(zhuǎn)膜蛋白的目錄表。
作者通過最大熵法僅僅通過氨基酸序列預(yù)測了過去未知的11個轉(zhuǎn)膜蛋白的三維結(jié)構(gòu),；還通過重新計算來自23個家族的已知轉(zhuǎn)膜蛋白來測試最大熵法,，證明了這種方法應(yīng)用于大分子轉(zhuǎn)膜蛋白的準確性；最后介紹了這種方法怎么用于預(yù)測轉(zhuǎn)膜蛋白的寡聚體,、功能性位點,、構(gòu)象改變等。(生物谷 Bioon.com)
doi:10.1016/j.cell.2012.04.012
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PMID:
Three-Dimensional Structures of Membrane Proteins from Genomic Sequencing
Thomas A. Hopf,， Lucy J. Colwell,， Robert Sheridan， Burkhard Rost， Chris Sander,， Debora S. MarksSee Affiliations
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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.
