Enhanced Bounding Techniques to Reduce the Protein Conformational Search Space.
Enhanced Bounding Techniques to Reduce the Protein Conformational Search Space.
复制标题
增强的边界技术以减少蛋白质构象搜索空间。
DOI：
10.1080/10556780902753486
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发表时间：
2009
影响因子：
2.2
通讯作者：
Floudas,ChristodoulosA
中科院分区：
文献类型：
--
作者：
McAllister,ScottR;Floudas,ChristodoulosA
蛋白质三级结构预测问题必须探索的构象空间的复杂性和巨大尺寸导致了各种算法方法的发展。在这项研究中，我们应用最先进的三级结构预测算法，并专注于开发边界技术以减少构象搜索空间。基于预测的二级结构和对 φ/ψ 空间允许区域的研究，建立了 φ 和 ψ 角的二面角界限。距离界限是根据预测的二级结构信息（包括β-折叠拓扑预测）开发的，以进一步减少搜索空间。这种边界策略完全独立于目标蛋白质和具有实验确定结构的蛋白质数据库之间的同源性程度。所提出的方法作为说明性示例应用于蛋白质 G 的结构预测，产生了显着更高数量的近天然蛋白质三级结构预测。
The complexity and enormous size of the conformational space that must be explored for the protein tertiary structure prediction problem has led to the development of a wide assortment of algorithmic approaches. In this study, we apply state-of-the-art tertiary structure prediction algorithms and instead focus on the development of bounding techniques to reduce the conformational search space. Dihedral angle bounds on the φ and ψ angles are established based on the predicted secondary structure and studies of the allowed regions of φ/ψ space. Distance bounds are developed based on predicted secondary structure information (including β-sheet topology predictions) to further reduce the search space. This bounding strategy is entirely independent of the degree of homology between the target protein and the database of proteins with experimentally-determined structures. The proposed approach is applied to the structure prediction of protein G as an illustrative example, yielding a significantly higher number of near-native protein tertiary structure predictions.