Chair of Bioinformatics, Matthias Schleiden Institute, University of Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
Westernacher Solutions, Columbiadamm 37, 10965, Berlin, Germany.
Sci Rep. 2020 Sep 18;10(1):15321. doi: 10.1038/s41598-020-72174-5.
The classification of proteinogenic amino acids is crucial for understanding their commonalities as well as their differences to provide a hint for why life settled on the usage of precisely those amino acids. It is also crucial for predicting electrostatic, hydrophobic, stacking and other interactions, for assessing conservation in multiple alignments and many other applications. While several methods have been proposed to find "the" optimal classification, they have several shortcomings, such as the lack of efficiency and interpretability or an unnecessarily high number of discriminating features. In this study, we propose a novel method involving a repeated binary separation via a minimum amount of five features (such as hydrophobicity or volume) expressed by numerical values for amino acid characteristics. The features are extracted from the AAindex database. By simple separation at the medians, we successfully derive the five properties volume, electron-ion-interaction potential, hydrophobicity, α-helix propensity, and π-helix propensity. We extend our analysis to separations other than by the median. We further score our combinations based on how natural the separations are.
蛋白质氨基酸的分类对于理解它们的共性以及它们之间的差异至关重要，这为生命为何选择特定的氨基酸提供了线索。它对于预测静电、疏水性、堆积和其他相互作用，评估多重比对中的保守性以及许多其他应用也至关重要。虽然已经提出了几种方法来寻找“最佳”分类，但它们存在一些缺点，例如效率和可解释性低，或者区分特征数量过多。在这项研究中，我们提出了一种新的方法，涉及通过最小数量的五个特征（如疏水性或体积）进行重复的二进制分离，这些特征由氨基酸特征的数值表示。这些特征是从 AAindex 数据库中提取的。通过简单的中位数分离，我们成功地得到了五个特性：体积、电子-离子相互作用势、疏水性、α-螺旋倾向和π-螺旋倾向。我们将分析扩展到中位数以外的分离。我们进一步根据分离的自然程度对我们的组合进行评分。
