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A knowledge-based scale for the analysis and prediction of buried and exposed faces of transmembrane domain proteins

机译:基于知识的量表,用于分析和预测跨膜结构域蛋白的掩埋和暴露面

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Motivation: The dearth of structural data on -helical membrane proteins (MPs) has hampered thus far the development of reliable knowledge-based potentials that can be used for automatic prediction of transmembrane (TM) protein structure. While algorithms for identifying TM segments are available, modeling of the TM domains of -helical MPs involves assembling the segments into a bundle. This requires the correct assignment of the buried and lipid-exposed faces of the TM domains. Results: A recent increase in the number of crystal structures of -helical MPs has enabled an analysis of the lipid-exposed surfaces and the interiors of such molecules on the basis of structure, rather than sequence alone. Together with a conservation criterion that is based on previous observations that conserved residues are mostly found in the interior of MPs, the bias of certain residue types to be preferably buried or exposed is proposed as a criterion for predicting the lipid-exposed and interior faces of TMs. Applications to known structures demonstrates 80% accuracy of this prediction algorithm.
机译:动机:迄今为止,缺乏关于螺旋膜蛋白(MPs)的结构数据的方法阻碍了可用于自动预测跨膜(TM)蛋白质结构的可靠的基于知识的潜能的发展。虽然可以使用用于识别TM片段的算法,但对-型螺旋MP的TM域的建模涉及将这些片段组装成一个束。这要求正确分配TM域的掩埋和脂质暴露面。结果:-螺旋MP的晶体结构数量的最近增加使得能够基于结构而不是单独的序列来分析脂质暴露的表面和此类分子的内部。与基于以前观察到的保守残基主要在MP的内部中发现的保守性标准一起,建议将某些优选掩埋或暴露的残余物类型的偏差作为预测脂质暴露和内表面的标准。 TM。在已知结构上的应用证明了该预测算法的80%准确性。

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