A robust and efficient algorithm for the shape description of protein structures and its application in predicting ligand binding sites.
A robust and efficient algorithm for the shape description of protein structures and its application in predicting ligand binding sites.
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DOI：
10.1186/1471-2105-8-s4-s9
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发表时间：
2007-05-22
影响因子：
3
通讯作者：
Bourne, Philip E
中科院分区：
文献类型：
--
作者：
Xie, Lei;Bourne, Philip E
从蛋白质结构得到的蛋白质形状的准确描述对于建立蛋白质-配体相互作用的理解是必要的，这反过来将导致蛋白质-配体对接和结合位点分析的改进方法。目前大多数形状描述符只使用全原子表示来表征蛋白质结构的局部性质，并且计算缓慢。我们需要新的形状描述符，有能力捕捉本地和全球的结构信息，是强大的应用模型和低质量的结构，计算效率高，允许高通量的蛋白质结构分析。我们引入了一种新的形状描述，只需要Cα原子来表示蛋白质结构，从而使其既快速又适用于模型和低质量结构。几何势的概念被引入到定量描述的结构的形状。这种几何势取决于蛋白质结构的整体形状以及每个残基的周围环境。当应用几何势进行结合位点预测时，大约85%的已知结合位点可以被准确地识别，具有超过50%的残基覆盖率和80%的特异性。此外，该算法是足够快的蛋白质组规模的应用程序。少于500个氨基酸的蛋白质可以在不到两秒的时间内扫描。与几何势相结合的蛋白质结构的减少表示提供了一个快速的，定量的蛋白质-配体结合位点的描述，用于大规模的预测，比较和分析的潜力。
An accurate description of protein shape derived from protein structure is necessary to establish an understanding of protein-ligand interactions, which in turn will lead to improved methods for protein-ligand docking and binding site analysis. Most current shape descriptors characterize only the local properties of protein structure using an all-atom representation and are slow to compute. We need new shape descriptors that have the ability to capture both local and global structural information, are robust for application to models and low quality structures and are computationally efficient to permit high throughput analysis of protein structures. We introduce a new shape description that requires only the Cα atoms to represent the protein structure, thus making it both fast and suitable for use on models and low quality structures. The notion of a geometric potential is introduced to quantitatively describe the shape of the structure. This geometric potential is dependent on both the global shape of the protein structure as well as the surrounding environment of each residue. When applying the geometric potential for binding site prediction, approximately 85% of known binding sites can be accurately identified with above 50% residue coverage and 80% specificity. Moreover, the algorithm is fast enough for proteome-scale applications. Proteins with fewer than 500 amino acids can be scanned in less than two seconds. The reduced representation of the protein structure combined with the geometric potential provides a fast, quantitative description of protein-ligand binding sites with potential for use in large-scale predictions, comparisons and analysis.