Simm Stefan, Einloft Jens, Mirus Oliver, Schleiff Enrico
Department of Biosciences, Molecular Cell Biology of Plants, Goethe University, Max von Laue Str. 9, 60438, Frankfurt/Main, Germany.
Molecular Bioinformatics, Cluster of Excellence Frankfurt "Macromolecular Complexes", Institute of Computer Science, Faculty of Computer Science and Mathematics, Goethe-University Frankfurt, Robert-Mayer-Str. 11-15, 60325, Frankfurt/Main, Germany.
Biol Res. 2016 Jul 4;49(1):31. doi: 10.1186/s40659-016-0092-5.
Physicochemical properties are frequently analyzed to characterize protein-sequences of known and unknown function. Especially the hydrophobicity of amino acids is often used for structural prediction or for the detection of membrane associated or embedded β-sheets and α-helices. For this purpose many scales classifying amino acids according to their physicochemical properties have been defined over the past decades. In parallel, several hydrophobicity parameters have been defined for calculation of peptide properties. We analyzed the performance of separating sequence pools using 98 hydrophobicity scales and five different hydrophobicity parameters, namely the overall hydrophobicity, the hydrophobic moment for detection of the α-helical and β-sheet membrane segments, the alternating hydrophobicity and the exact ß-strand score.
Most of the scales are capable of discriminating between transmembrane α-helices and transmembrane β-sheets, but assignment of peptides to pools of soluble peptides of different secondary structures is not achieved at the same quality. The separation capacity as measure of the discrimination between different structural elements is best by using the five different hydrophobicity parameters, but addition of the alternating hydrophobicity does not provide a large benefit. An in silico evolutionary approach shows that scales have limitation in separation capacity with a maximal threshold of 0.6 in general. We observed that scales derived from the evolutionary approach performed best in separating the different peptide pools when values for arginine and tyrosine were largely distinct from the value of glutamate. Finally, the separation of secondary structure pools via hydrophobicity can be supported by specific detectable patterns of four amino acids.
It could be assumed that the quality of separation capacity of a certain scale depends on the spacing of the hydrophobicity value of certain amino acids. Irrespective of the wealth of hydrophobicity scales a scale separating all different kinds of secondary structures or between soluble and transmembrane peptides does not exist reflecting that properties other than hydrophobicity affect secondary structure formation as well. Nevertheless, application of hydrophobicity scales allows distinguishing between peptides with transmembrane α-helices and β-sheets. Furthermore, the overall separation capacity score of 0.6 using different hydrophobicity parameters could be assisted by pattern search on the protein sequence level for specific peptides with a length of four amino acids.
经常分析物理化学性质以表征已知和未知功能的蛋白质序列。尤其是氨基酸的疏水性常用于结构预测或检测与膜相关或嵌入的β折叠和α螺旋。为此,在过去几十年中定义了许多根据氨基酸物理化学性质对其进行分类的量表。同时,还定义了几个疏水性参数来计算肽的性质。我们使用98种疏水性量表和五个不同的疏水性参数分析了分离序列库的性能,这五个参数分别是总体疏水性、用于检测α螺旋和β折叠膜段的疏水矩、交替疏水性和精确的β链得分。
大多数量表能够区分跨膜α螺旋和跨膜β折叠,但将肽分配到不同二级结构的可溶性肽库时,质量并不相同。作为衡量不同结构元件之间区分度的分离能力,使用五个不同的疏水性参数时最佳,但添加交替疏水性并没有带来很大益处。一种计算机模拟进化方法表明,量表在分离能力上存在局限性,一般最大阈值为0.6。我们观察到,当精氨酸和酪氨酸的值与谷氨酸的值有很大差异时,源自进化方法的量表在分离不同肽库方面表现最佳。最后,通过疏水性分离二级结构库可以得到特定的四个氨基酸可检测模式的支持。
可以假设,某一量表的分离能力质量取决于某些氨基酸疏水性值的间距。尽管有大量的疏水性量表,但不存在能分离所有不同类型二级结构或可溶性与跨膜肽之间差异的量表,这反映出除疏水性外的其他性质也会影响二级结构的形成。然而,应用疏水性量表可以区分具有跨膜α螺旋和β折叠的肽。此外,使用不同疏水性参数时0.6的总体分离能力得分可以通过在蛋白质序列水平上搜索特定的四个氨基酸长度的肽模式来辅助。