TERPRED: A Dynamic Structural Data Analysis Tool.

Karl Walker, Carole L Cramer, Steven F Jennings, Xiuzhen Huang
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Abstract

Computational protein structure prediction mainly involves the main-chain prediction and the side-chain confirmation determination. In this research, we developed a new structural bioinformatics tool, TERPRED for generating dynamic protein side-chain rotamer libraries. Compared with current various rotamer sampling methods, our work is unique in that it provides a method to generate a rotamer library dynamically based on small sequence fragments of a target protein. The Rotamer Generator provides a means for existing side-chain sampling methods using static pre-existing rotamer libraries, to sample from dynamic target-dependent libraries. Also, existing side-chain packing algorithms that require large rotamer libraries for optimal performance, could possibly utilize smaller, target-relevant libraries for improved speed.

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一个动态结构数据分析工具。
计算蛋白质结构预测主要包括主链预测和侧链确定。在这项研究中,我们开发了一种新的结构生物信息学工具TERPRED,用于生成动态蛋白质侧链旋转体库。与目前的各种旋转体采样方法相比,我们的工作是独特的,因为它提供了一种基于目标蛋白的小序列片段动态生成旋转体库的方法。Rotamer Generator为使用静态预先存在的Rotamer库的现有侧链采样方法提供了一种方法,可以从动态目标相关库中进行采样。此外,现有的侧链打包算法需要大型旋转程序库才能获得最佳性能,因此可以利用更小的、与目标相关的库来提高速度。
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