Circular dichroism spectroscopy is a structural biology technique frequently applied to determine the secondary structure composition of soluble proteins. Our recently introduced computational analysis package SESCA aids the interpretation of protein circular dichroism spectra and enables the validation of proposed corresponding structural models. To further these aims, we present the implementation and characterization of a new Bayesian secondary structure estimation method in SESCA, termed SESCA_bayes. SESCA_bayes samples possible secondary structures using a Monte Carlo scheme, driven by the likelihood of estimated scaling errors and non-secondary-structure contributions of the measured spectrum. SESCA_bayes provides an estimated secondary structure composition and separate uncertainties on the fraction of residues in each secondary structure class. It also assists efficient model validation by providing a posterior secondary structure probability distribution based on the measured spectrum. Our presented study indicates that SESCA_bayes estimates the secondary structure composition with a significantly smaller uncertainty than its predecessor, SESCA_deconv, which is based on spectrum deconvolution. Further, the mean accuracy of the two methods in our analysis is comparable, but SESCA_bayes provides more accurate estimates for circular dichroism spectra that contain considerable non-SS contributions.

中文翻译：

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SESCA圆二色性分析包的贝叶斯二级结构估计方法的实现。

圆二色性光谱学是一种结构生物学技术，经常用于确定可溶性蛋白质的二级结构组成。我们最近推出的计算分析软件包SESCA有助于蛋白质圆二色性光谱的解释，并能够验证提出的相应结构模型。为了实现这些目标，我们介绍了一种称为SESCA_bayes的SESCA中新的贝叶斯二级结构估计方法的实现和特征。SESCA_bayes使用蒙特卡洛（Monte Carlo）方案对可能的二级结构进行采样，这由估计的比例误差和测量频谱的非二级结构贡献的可能性驱动。SESCA_bayes提供了估计的二级结构组成以及每个二级结构类别中残基分数的单独不确定性。它还通过提供基于所测频谱的后继二级结构概率分布来帮助进行有效的模型验证。我们提出的研究表明，与基于频谱反卷积的前身SESCA_deconv相比，SESCA_bayes估计的二级结构组成具有明显较小的不确定性。此外，在我们的分析中这两种方法的平均准确度相当，但是SESCA_bayes对包含大量非SS贡献的圆二色性光谱提供了更准确的估计。它还通过提供基于所测频谱的后继二级结构概率分布来帮助进行有效的模型验证。我们提出的研究表明，与基于频谱反卷积的前身SESCA_deconv相比，SESCA_bayes估计的二级结构组成具有明显较小的不确定性。此外，在我们的分析中，这两种方法的平均准确度相当，但是SESCA_bayes对包含大量非SS贡献的圆二色性光谱提供了更准确的估计。它还通过提供基于所测频谱的后继二级结构概率分布来帮助进行有效的模型验证。我们提出的研究表明，与基于频谱反卷积的前身SESCA_deconv相比，SESCA_bayes估计的二级结构组成具有明显较小的不确定性。此外，在我们的分析中这两种方法的平均准确度相当，但是SESCA_bayes对包含大量非SS贡献的圆二色性光谱提供了更准确的估计。