Laboratory for the Structure and Function of Biological Membranes, Center for Structural Biology and Bioinformatics, Université Libre de Bruxelles, Campus Plaine CP206/02, 1050, Brussels, Belgium.
Eur Biophys J. 2021 May;50(3-4):641-651. doi: 10.1007/s00249-021-01507-7. Epub 2021 Feb 8.
Prediction of protein secondary structure from FTIR spectra usually relies on the absorbance in the amide I-amide II region of the spectrum. It assumes that the absorbance in this spectral region, i.e., roughly 1700-1500 cm is solely arising from amide contributions. Yet, it is accepted that, on the average, about 20% of the absorbance is due to amino acid side chains. The present paper evaluates the contribution of amino acid side chains in this spectral region and the potential to improve secondary structure prediction after correcting for their contribution. We show that the β-sheet content prediction is improved upon subtraction of amino acid side chain contributions in the amide I-amide II spectral range. Improvement is relatively important, for instance, the error of prediction of β-sheet content decreases from 5.42 to 4.97% when evaluated by ascending stepwise regression. Other methods tested such as partial least square regression and support vector machine have also improved accuracy for β-sheet content evaluation. The other structures such as α-helix do not significantly benefit from side chain contribution subtraction, in some cases prediction is even degraded. We show that co-linearity between secondary structure content and amino acid composition is not a main limitation for improving secondary structure prediction. We also show that, even though based on different criteria, secondary structures defined by DSSP and XTLSSTR both arrive at the same conclusion: only the β-sheet structure clearly benefits from side chain subtraction. It must be concluded that side chain contribution subtraction benefit for the evaluation of other secondary structure contents is limited by the very rough description of side chain absorbance which does not take into account the variations related to their environment. The study was performed on a large protein set. To deal with the large number of proteins present, we worked on protein microarrays deposited on BaF slides and FTIR spectra were acquired with an imaging system.
从傅里叶变换红外（FTIR）光谱预测蛋白质二级结构通常依赖于光谱酰胺 I-酰胺 II 区域的吸光度。它假定该光谱区域（大致在 1700-1500cm-1 之间）的吸光度仅源自酰胺贡献。然而，人们普遍认为，平均而言，约 20%的吸光度归因于氨基酸侧链。本文评估了在该光谱区域中氨基酸侧链的贡献，以及在纠正其贡献后改善二级结构预测的潜力。我们表明，在酰胺 I-酰胺 II 光谱范围内扣除氨基酸侧链贡献后，β-折叠含量的预测得到改善。这种改进是相对重要的，例如，通过逐步上升回归评估时，β-折叠含量预测的误差从 5.42%降低到 4.97%。测试的其他方法，如偏最小二乘回归和支持向量机，也提高了对β-折叠含量评估的准确性。其他结构，如α-螺旋，从侧链贡献扣除中没有明显受益，在某些情况下预测甚至恶化。我们表明，二级结构含量与氨基酸组成之间的共线性不是提高二级结构预测的主要限制。我们还表明，即使基于不同的标准，由 DSSP 和 XTLSSTR 定义的二级结构都得出相同的结论：只有β-折叠结构明显受益于侧链扣除。必须得出结论，侧链贡献扣除对其他二级结构含量评估的益处受到侧链吸光度的粗略描述的限制，该描述未考虑与其环境相关的变化。该研究是在一个大型蛋白质集上进行的。为了处理存在的大量蛋白质，我们在 BaF 载玻片上处理蛋白质微阵列，并使用成像系统获取 FTIR 光谱。
