Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America.
PLoS One. 2010 Oct 27;5(10):e15386. doi: 10.1371/journal.pone.0015386.
An accurate potential function is essential to attack protein folding and structure prediction problems. The key to developing efficient knowledge-based potential functions is to design reference states that can appropriately counteract generic interactions. The reference states of many knowledge-based distance-dependent atomic potential functions were derived from non-interacting particles such as ideal gas, however, which ignored the inherent sequence connectivity and entropic elasticity of proteins.
We developed a new pair-wise distance-dependent, atomic statistical potential function (RW), using an ideal random-walk chain as reference state, which was optimized on CASP models and then benchmarked on nine structural decoy sets. Second, we incorporated a new side-chain orientation-dependent energy term into RW (RWplus) and found that the side-chain packing orientation specificity can further improve the decoy recognition ability of the statistical potential.
RW and RWplus demonstrate a significantly better ability than the best performing pair-wise distance-dependent atomic potential functions in both native and near-native model selections. It has higher energy-RMSD and energy-TM-score correlations compared with other potentials of the same type in real-life structure assembly decoys. When benchmarked with a comprehensive list of publicly available potentials, RW and RWplus shows comparable performance to the state-of-the-art scoring functions, including those combining terms from multiple resources. These data demonstrate the usefulness of random-walk chain as reference states which correctly account for sequence connectivity and entropic elasticity of proteins. It shows potential usefulness in structure recognition and protein folding simulations. The RW and RWplus potentials, as well as the newly generated I-TASSER decoys, are freely available in http://zhanglab.ccmb.med.umich.edu/RW.
准确的势能函数对于解决蛋白质折叠和结构预测问题至关重要。开发高效的基于知识的势能函数的关键是设计能够适当抵消通用相互作用的参考状态。许多基于知识的距离相关原子势能函数的参考状态是从非相互作用的粒子（如理想气体）中推导出来的，但忽略了蛋白质固有的序列连接性和熵弹性。
我们使用理想的随机行走链作为参考状态，开发了一种新的对距离相关的原子统计势能函数（RW），对 CASP 模型进行了优化，然后在九个结构模拟集上进行了基准测试。其次，我们将新的侧链取向相关的能量项纳入到 RW 中（RWplus），发现侧链堆积取向的特异性可以进一步提高统计势能对模拟集的识别能力。
RW 和 RWplus 在天然和近天然模型选择中都比表现最好的对距离相关的原子势能函数具有更好的能力。与其他同类势能相比，它在真实结构组装模拟集中具有更高的能量均方根偏差（RMSD）和能量-TM 评分相关性。在与一系列公开可用的势能进行基准测试时，RW 和 RWplus 与最先进的评分函数表现相当，包括那些结合了多种资源的评分函数。这些数据表明，随机行走链作为参考状态是有用的，它正确地考虑了蛋白质的序列连接性和熵弹性。它在结构识别和蛋白质折叠模拟中具有潜在的应用价值。RW 和 RWplus 势能以及新生成的 I-TASSER 模拟集可在 http://zhanglab.ccmb.med.umich.edu/RW 上免费获取。
