Mathematical and Computer Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955, KSA.
BMC Bioinformatics. 2011 Dec 14;12 Suppl 14(Suppl 14):S10. doi: 10.1186/1471-2105-12-S14-S10.
Protein side-chain packing problem has remained one of the key open problems in bioinformatics. The three main components of protein side-chain prediction methods are a rotamer library, an energy function and a search algorithm. Rotamer libraries summarize the existing knowledge of the experimentally determined structures quantitatively. Depending on how much contextual information is encoded, there are backbone-independent rotamer libraries and backbone-dependent rotamer libraries. Backbone-independent libraries only encode sequential information, whereas backbone-dependent libraries encode both sequential and locally structural information. However, side-chain conformations are determined by spatially local information, rather than sequentially local information. Since in the side-chain prediction problem, the backbone structure is given, spatially local information should ideally be encoded into the rotamer libraries.
In this paper, we propose a new type of backbone-dependent rotamer library, which encodes structural information of all the spatially neighboring residues. We call it protein-dependent rotamer libraries. Given any rotamer library and a protein backbone structure, we first model the protein structure as a Markov random field. Then the marginal distributions are estimated by the inference algorithms, without doing global optimization or search. The rotamers from the given library are then re-ranked and associated with the updated probabilities.
Experimental results demonstrate that the proposed protein-dependent libraries significantly outperform the widely used backbone-dependent libraries in terms of the side-chain prediction accuracy and the rotamer ranking ability. Furthermore, without global optimization/search, the side-chain prediction power of the protein-dependent library is still comparable to the global-search-based side-chain prediction methods.
蛋白质侧链堆积问题一直是生物信息学中的一个关键难题。蛋白质侧链预测方法的三个主要组成部分是旋转体库、能量函数和搜索算法。旋转体库定量总结了实验确定结构的现有知识。根据编码的上下文信息量，有骨架独立的旋转体库和骨架依赖的旋转体库。骨架独立的库仅编码顺序信息，而骨架依赖的库则同时编码顺序和局部结构信息。然而，侧链构象是由空间局部信息决定的，而不是顺序局部信息。由于在侧链预测问题中，骨架结构是给定的，因此理想情况下应将空间局部信息编码到旋转体库中。
在本文中，我们提出了一种新的骨架依赖的旋转体库，它编码了所有空间相邻残基的结构信息。我们称之为蛋白质依赖的旋转体库。对于任何旋转体库和蛋白质骨架结构，我们首先将蛋白质结构建模为马尔可夫随机场。然后通过推理算法估计边缘分布，而无需进行全局优化或搜索。然后对来自给定库的旋转体进行重新排序，并与更新的概率相关联。
实验结果表明，在所提出的蛋白质依赖的库在侧链预测准确性和旋转体排序能力方面明显优于广泛使用的骨架依赖的库。此外，无需全局优化/搜索，蛋白质依赖的库的侧链预测能力仍然可与基于全局搜索的侧链预测方法相媲美。
