Many proteins are composed of several domains that pack together into a complex tertiary structure. Multidomain proteins can be challenging for protein structure modeling, particularly those for which templates can be found for individual domains but not for the entire sequence. In such cases, homology modeling can generate high quality models of the domains but not for the orientations between domains. Small-angle X-ray scattering (SAXS) reports the structural properties of entire proteins and has the potential for guiding homology modeling of multidomain proteins. In this article, we describe a novel multidomain protein assembly modeling method, SAXSDom that integrates experimental knowledge from SAXS with probabilistic Input-Output Hidden Markov model to assemble the structures of individual domains together. Four SAXS-based scoring functions were developed and tested, and the method was evaluated on multidomain proteins from two public datasets. Incorporation of SAXS information improved the accuracy of domain assembly for 40 out of 46 critical assessment of protein structure prediction multidomain protein targets and 45 out of 73 multidomain protein targets from the ab initio domain assembly dataset. The results demonstrate that SAXS data can provide useful information to improve the accuracy of domain-domain assembly. The source code and tool packages are available at https://github.com/jianlin-cheng/SAXSDom.

中文翻译：

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SAXSDom：使用小角度 X 射线散射数据对多域蛋白质结构进行建模。

许多蛋白质由多个结构域组成，这些结构域组合在一起形成复杂的三级结构。多结构域蛋白质对于蛋白质结构建模来说可能具有挑战性，特别是那些可以为单个结构域找到模板但不能为整个序列找到模板的蛋白质。在这种情况下，同源建模可以生成域的高质量模型，但不能生成域之间的方向模型。小角 X 射线散射 (SAXS) 报告整个蛋白质的结构特性，并具有指导多域蛋白质同源建模的潜力。在本文中，我们描述了一种新颖的多域蛋白质组装建模方法 SAXSDom，它将 SAXS 的实验知识与概率输入输出隐马尔可夫模型相结合，将各个域的结构组装在一起。开发并测试了四种基于 SAXS 的评分函数，并针对来自两个公共数据集的多域蛋白质对该方法进行了评估。SAXS 信息的纳入提高了蛋白质结构预测多域蛋白质目标的 46 个关键评估中的 40 个和从头开始域组装数据集中的 73 个多域蛋白质目标中的 45 个域组装的准确性。结果表明，SAXS 数据可以提供有用的信息，以提高域域组装的准确性。源码和工具包可以在https://github.com/jianlin- Cheng/SAXSDom获取。