Implementation of a Bayesian secondary structure estimation method for the SESCA circular dichroism analysis package

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.

圆二色光谱学是一种常用于测定可溶性蛋白质二级结构组成的结构生物学技术。我们近期推出的计算分析软件包 SESCA 有助于蛋白质圆二色光谱的解读，并能够验证所提出的相应结构模型。为进一步实现这些目标，我们介绍了 SESCA 中一种新的贝叶斯二级结构估计方法的实现与特性，称之为 SESCA_bayes。SESCA_bayes 通过蒙特卡洛方案采样可能的二级结构，该方案由估计缩放误差和测量光谱的非二级结构贡献的可能性所驱动。SESCA_bayes 提供了估计的二级结构组成，并对每个二级结构类中残基的分数提供了独立的置信度。此外，它通过基于测量光谱的后验二级结构概率分布，辅助高效模型验证。我们的研究指出，与基于光谱去卷积的先导方法 SESCA_deconv 相比，SESCA_bayes 在估计二级结构组成方面具有显著更小的不确定性。进一步地，在我们的分析中，两种方法的平均准确度相当，但 SESCA_bayes 对含有相当非二级结构贡献的圆二色光谱提供了更准确的估计。