Joo Keehyoung, Lee Jinwoo, Seo Joo-Hyun, Lee Kyoungrim, Kim Byung-Gee, Lee Jooyoung
School of Computational Sciences, Korea Institute for Advanced Study, Seoul 130-722, Korea.
Proteins. 2009 Jun;75(4):1010-23. doi: 10.1002/prot.22312.
We have investigated the effect of rigorous optimization of the MODELLER energy function for possible improvement in protein all-atom chain-building. For this we applied the global optimization method called conformational space annealing (CSA) to the standard MODELLER procedure to achieve better energy optimization than what MODELLER provides. The method, which we call MODELLERCSA, is tested on two benchmark sets. The first is the 298 proteins taken from the HOMSTRAD multiple alignment set. By simply optimizing the MODELLER energy function, we observe significant improvement in side-chain modeling, where MODELLERCSA provides about 10.7% (14.5%) improvement for chi(1) (chi(1) + chi(2)) accuracy compared to the standard MODELLER modeling. The improvement of backbone accuracy by MODELLERCSA is shown to be less prominent, and a similar improvement can be achieved by simply generating many standard MODELLER models and selecting lowest energy models. However, the level of side-chain modeling accuracy by MODELLERCSA could not be matched either by extensive MODELLER strategies, side-chain remodeling by SCWRL3, or copying unmutated rotamers. The identical procedure was successfully applied to 100 CASP7 template base modeling domains during the prediction season in a blind fashion, and the results are included here for comparison. From this study, we observe a good correlation between the MODELLER energy and the side-chain accuracy. Our findings indicate that, when a good alignment between a target protein and its templates is provided, thorough optimization of the MODELLER energy function leads to accurate all-atom models.
我们研究了严格优化MODELLER能量函数对改善蛋白质全原子链构建的可能效果。为此，我们将名为构象空间退火（CSA）的全局优化方法应用于标准的MODELLER程序，以实现比MODELLER本身更好的能量优化。我们将这种方法称为MODELLERCSA，并在两个基准数据集上进行了测试。第一个数据集是从HOMSTRAD多序列比对集中选取的298个蛋白质。通过简单地优化MODELLER能量函数，我们观察到侧链建模有显著改善，与标准的MODELLER建模相比，MODELLERCSA在χ(1)（χ(1)+χ(2)）准确性方面提高了约10.7%（14.5%）。结果表明，MODELLERCSA对主链准确性的提升不太显著，通过简单地生成多个标准的MODELLER模型并选择能量最低的模型也能实现类似的提升。然而，无论是广泛的MODELLER策略、使用SCWRL3进行侧链重塑，还是复制未突变的旋转异构体，都无法达到MODELLERCSA的侧链建模准确性水平。在预测期间，相同的程序被成功地以盲法应用于100个CASP7模板基础建模域，这里包含结果以供比较。通过这项研究，我们观察到MODELLER能量与侧链准确性之间存在良好的相关性。我们的研究结果表明，当提供目标蛋白质与其模板之间的良好比对时，对MODELLER能量函数进行全面优化可得到准确的全原子模型。
