Department of Computer Science, Old Dominion University, Norfolk, Virginia, USA.
J Chem Inf Model. 2011 Jul 25;51(7):1656-66. doi: 10.1021/ci200143u. Epub 2011 Jul 8.
Accurately predicting loop structures is important for understanding functions of many proteins. In order to obtain loop models with high accuracy, efficiently sampling the loop conformation space to discover reasonable structures is a critical step. In loop conformation sampling, coarse-grain energy (scoring) functions coupling with reduced protein representations are often used to reduce the number of degrees of freedom as well as sampling computational time. However, due to implicitly considering many factors by reduced representations, the coarse-grain scoring functions may have potential insensitivity and inaccuracy, which can mislead the sampling process and consequently ignore important loop conformations. In this paper, we present a new computational sampling approach to obtain reasonable loop backbone models, so-called the Pareto optimal sampling (POS) method. The rationale of the POS method is to sample the function space of multiple, carefully selected scoring functions to discover an ensemble of diversified structures yielding Pareto optimality to all sampled conformations. The POS method can efficiently tolerate insensitivity and inaccuracy in individual scoring functions and thereby lead to significant accuracy improvement in loop structure prediction. We apply the POS method to a set of 4-12-residue loop targets using a function space composed of backbone-only Rosetta and distance-scale finite ideal-gas reference (DFIRE) and a triplet backbone dihedral potential developed in our lab. Our computational results show that in 501 out of 502 targets, the model sets generated by POS contain structure models are within subangstrom resolution. Moreover, the top-ranked models have a root mean square deviation (rmsd) less than 1 A in 96.8, 84.1, and 72.2% of the short (4-6 residues), medium (7-9 residues), and long (10-12 residues) targets, respectively, when the all-atom models are generated by local optimization from the backbone models and are ranked by our recently developed Pareto optimal consensus (POC) method. Similar sampling effectiveness can also be found in a set of 13-residue loop targets.
准确预测环结构对于理解许多蛋白质的功能非常重要。为了获得具有高精度的环模型，高效地采样环构象空间以发现合理的结构是一个关键步骤。在环构象采样中，通常使用粗粒度能量（评分）函数与简化的蛋白质表示相结合，以减少自由度数量和采样计算时间。然而，由于简化表示隐含地考虑了许多因素，粗粒度评分函数可能存在潜在的不敏感性和不准确性，这可能会误导采样过程，从而忽略重要的环构象。在本文中，我们提出了一种新的计算采样方法来获得合理的环骨架模型，称为帕累托最优采样（POS）方法。POS 方法的原理是采样多个精心选择的评分函数的函数空间，以发现一组多样化的结构，这些结构对所有采样构象都具有帕累托最优性。POS 方法可以有效地容忍单个评分函数的不敏感性和不准确性，从而显著提高环结构预测的准确性。我们使用由仅包含骨架的 Rosetta 和距离尺度有限理想气体参考（DFIRE）组成的函数空间以及我们实验室开发的三键骨架二面角势，将 POS 方法应用于一组 4-12 残基环靶标。我们的计算结果表明，在 502 个靶标中有 501 个靶标，POS 生成的模型集中包含的结构模型的分辨率都在亚埃以内。此外，在使用局部优化从骨架模型生成全原子模型并通过我们最近开发的帕累托最优共识（POC）方法进行排名的情况下，在短（4-6 残基）、中（7-9 残基）和长（10-12 残基）靶标中，排名前 10%的模型的均方根偏差（rmsd）小于 1 A 的靶标分别占 96.8%、84.1%和 72.2%。在一组 13 残基环靶标中也可以找到类似的采样效果。
