Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA.
Int J Mol Sci. 2013 Oct 15;14(10):20635-57. doi: 10.3390/ijms141020635.
With the accumulation of next generation sequencing data, there is increasing interest in the study of intra-species difference in molecular biology, especially in relation to disease analysis. Furthermore, the dynamics of the protein is being identified as a critical factor in its function. Although accuracy of protein structure prediction methods is high, provided there are structural templates, most methods are still insensitive to amino-acid differences at critical points that may change the overall structure. Also, predicted structures are inherently static and do not provide information about structural change over time. It is challenging to address the sensitivity and the dynamics by computational structure predictions alone. However, with the fast development of diverse mass spectrometry coupled experiments, low-resolution but fast and sensitive structural information can be obtained. This information can then be integrated into the structure prediction process to further improve the sensitivity and address the dynamics of the protein structures. For this purpose, this article focuses on reviewing two aspects: the types of mass spectrometry coupled experiments and structural data that are obtainable through those experiments; and the structure prediction methods that can utilize these data as constraints. Also, short review of current efforts in integrating experimental data in the structural modeling is provided.
随着下一代测序数据的积累，人们对研究物种内分子生物学差异越来越感兴趣，特别是在疾病分析方面。此外，蛋白质的动力学被认为是其功能的关键因素。尽管蛋白质结构预测方法的准确性很高，但在有结构模板的情况下，大多数方法仍然对可能改变整体结构的关键位置的氨基酸差异不敏感。此外，预测的结构本质上是静态的，不能提供关于随时间变化的结构变化的信息。仅通过计算结构预测来解决敏感性和动力学问题具有挑战性。然而，随着各种质谱联用实验的快速发展，可以获得低分辨率但快速和敏感的结构信息。然后可以将这些信息集成到结构预测过程中，以进一步提高敏感性并解决蛋白质结构的动力学问题。为此，本文重点关注两个方面：可通过这些实验获得的质谱联用实验的类型和结构数据；以及可以利用这些数据作为约束的结构预测方法。此外，还提供了对当前将实验数据集成到结构建模中的努力的简要回顾。
