Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
Biophys J. 2011 Dec 7;101(11):2770-81. doi: 10.1016/j.bpj.2011.10.046.
Small-angle x-ray scattering (SAXS) is able to extract low-resolution protein shape information without requiring a specific crystal formation. However, it has found little use in atomic-level protein structure determination due to the uncertainty of residue-level structural assignment. We developed a new algorithm, SAXSTER, to couple the raw SAXS data with protein-fold-recognition algorithms and thus improve template-based protein-structure predictions. We designed nine different matching scoring functions of template and experimental SAXS profiles. The logarithm of the integrated correlation score showed the best template recognition ability and had the highest correlation with the true template modeling (TM)-score of the target structures. We tested the method in large-scale protein-fold-recognition experiments and achieved significant improvements in prioritizing the best template structures. When SAXSTER was applied to the proteins of asymmetric SAXS profile distributions, the average TM-score of the top-ranking templates increased by 18% after homologous templates were excluded, which corresponds to a p-value < 10(-9) in Student's t-test. These data demonstrate a promising use of SAXS data to facilitate computational protein structure modeling, which is expected to work most efficiently for proteins of irregular global shape and/or multiple-domain protein complexes.
小角 X 射线散射（SAXS）能够在不要求特定晶体形成的情况下提取低分辨率的蛋白质形状信息。然而，由于残基结构分配的不确定性，它在原子水平的蛋白质结构测定中几乎没有得到应用。我们开发了一种新算法 SAXSTER，将原始 SAXS 数据与蛋白质折叠识别算法相结合，从而改进基于模板的蛋白质结构预测。我们设计了九种不同的模板和实验 SAXS 图谱的匹配评分函数。对数积分相关得分显示出最好的模板识别能力，并与目标结构的真实模板建模（TM）得分具有最高的相关性。我们在大规模蛋白质折叠识别实验中测试了该方法，并在优先选择最佳模板结构方面取得了显著的改进。当 SAXSTER 应用于非对称 SAXS 分布的蛋白质时，在排除同源模板后，排名最高的模板的平均 TM 得分增加了 18%，这在学生 t 检验中对应的 p 值<10(-9)。这些数据表明，SAXS 数据有望用于促进计算蛋白质结构建模，对于具有不规则整体形状和/或多结构域蛋白质复合物的蛋白质，预计效果最佳。
