利用SELCON算法从独立和集成的红外吸光度和圆二色性数据确定蛋白质二级结构。

Q3 Biochemistry, Genetics and Molecular Biology
QRB Discovery Pub Date : 2025-02-03 eCollection Date: 2025-01-01 DOI:10.1017/qrd.2025.4
Søren Vrønning Hoffmann, Nykola C Jones, Alison Rodger
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引用次数: 0

摘要

蛋白质的圆二色性(CD)和红外吸收光谱(IR)被广泛用于估计溶液中蛋白质的二级结构含量。CD分析使用了一系列算法(SELCON, CONTIN, CDsstr, SOMSpec),其中一些算法已应用于IR数据,尽管IR更常用带拟合或统计方法进行分析。在这项工作中,我们提供了一个Python版本的SELCON3,并探索如何将CD和IR数据结合起来以获得最佳效果。我们使用了Δε/氨基酸残基的CD数据,并将红外光谱缩放到相似的量级。将红外酰胺I光谱归一化至最大吸光度为15,可获得最佳的一般性能。结合CD和IR可将螺旋蛋白和薄片蛋白的预测提高约2%,并有助于识别单独使用IR数据时高螺旋蛋白(如血红蛋白)和单独使用CD数据时高薄片蛋白的异常大误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Protein secondary structure determined from independent and integrated infra-red absorbance and circular dichroism data using the algorithm SELCON.

Protein circular dichroism (CD) and infrared absorbance (IR) spectra are widely used to estimate the secondary structure content of proteins in solution. A range of algorithms have been used for CD analysis (SELCON, CONTIN, CDsstr, SOMSpec) and some of these have been applied to IR data, though IR is more commonly analysed by bandfitting or statistical approaches. In this work we provide a Python version of SELCON3 and explore how to combine CD and IR data to best effect. We used CD data in Δε/amino acid residue and scaled the IR spectra to similar magnitudes. Normalising the IR amide I spectra scaled to a maximum absorbance of 15 gives best general performance. Combining CD and IR improves predictions for both helix and sheet by ~2% and helps identify anomalously large errors for high helix proteins such as haemoglobin when using IR data alone and high sheet proteins when using CD data alone.

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来源期刊
QRB Discovery
QRB Discovery Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
3.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
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