Center for Theoretical Biological Physics, Rice University , Houston , Texas 77030 , United States.
Department of Bioengineering , Rice University , Houston , Texas 77005 , United States.
J Chem Theory Comput. 2018 Nov 13;14(11):6102-6116. doi: 10.1021/acs.jctc.8b00683. Epub 2018 Oct 8.
When good structural templates can be identified, template-based modeling is the most reliable way to predict the tertiary structure of proteins. In this study, we combine template-based modeling with a realistic coarse-grained force field, AWSEM, that has been optimized using the principles of energy landscape theory. The Associative memory, Water mediated, Structure and Energy Model (AWSEM) is a coarse-grained force field having both transferable tertiary interactions and knowledge-based local-in-sequence interaction terms. We incorporate template information into AWSEM by introducing soft collective biases to the template structures, resulting in a model that we call AWSEM-Template. Structure prediction tests on eight targets, four of which are in the low sequence identity "twilight zone" of homology modeling, show that AWSEM-Template can achieve high-resolution structure prediction. Our results also confirm that using a combination of AWSEM and a template-guided potential leads to more accurate prediction of protein structures than simply using a template-guided potential alone. Free energy profile analyses demonstrate that the soft collective biases to the template effectively increase funneling toward native-like structures while still allowing significant flexibility so as to allow for correction of discrepancies between the target structure and the template. A further stage of refinement using all-atom molecular dynamics augmented with soft collective biases to the structures predicted by AWSEM-Template leads to a further improvement of both backbone and side-chain accuracy by maintaining sufficient flexibility but at the same time discouraging unproductive unfolding events often seen in unrestrained all-atom refinement simulations. The all-atom refinement simulations also reduce patches of frustration of the initial predictions. Some of the backbones found among the structures produced during the initial coarse-grained prediction step already have CE-RMSD values of less than 3 Å with 90% or more of the residues aligned to the experimentally solved structure for all targets. All-atom structures generated during the following all-atom refinement simulations, which started from coarse-grained structures that were chosen without reference to any knowledge about the native structure, have CE-RMSD values of less than 2.5 Å with 90% or more of the residues aligned for 6 out of 8 targets. Clustering low energy structures generated during the initial coarse-grained annealing picks out reliably structures that are within 1 Å of the best sampled structures in 5 out of 8 cases. After the all-atom refinement, structures that are within 1 Å of the best sampled structures can be selected using a simple algorithm based on energetic features alone in 7 out of 8 cases.
当可以识别出良好的结构模板时，基于模板的建模是预测蛋白质三级结构最可靠的方法。在这项研究中，我们将基于模板的建模与一种经过优化的现实的粗粒力场 AWSEM 相结合，该力场是根据能量景观理论的原理进行优化的。关联记忆、水介导、结构和能量模型（AWSEM）是一种具有可转移三级相互作用和基于知识的局部序列相互作用项的粗粒力场。我们通过向模板结构引入软集体偏差将模板信息纳入 AWSEM，从而得到一个我们称之为 AWSEM-Template 的模型。对八个目标进行结构预测测试，其中四个目标处于同源建模的低序列同一性“黄昏区”，结果表明 AWSEM-Template 可以实现高分辨率结构预测。我们的结果还证实，使用 AWSEM 和模板引导势的组合比仅使用模板引导势更能准确预测蛋白质结构。自由能谱分析表明，对模板的软集体偏差有效地增加了向天然样结构的漏斗效应，同时仍然保持了足够的灵活性，从而允许纠正目标结构与模板之间的差异。使用全原子分子动力学对 AWSEM-Template 预测的结构进行进一步的细化，并结合对结构的软集体偏差，通过保持足够的灵活性，同时阻止不受约束的全原子细化模拟中经常出现的无生产性展开事件，进一步提高了骨架和侧链的准确性。全原子细化模拟还减少了初始预测中的挫折区域。在初始粗粒预测步骤中产生的结构中，有些骨架已经具有小于 3Å 的 CE-RMSD 值，并且 90%或更多的残基与所有目标的实验确定结构对齐。从没有参考任何关于天然结构的知识而选择的粗粒结构开始的全原子细化模拟产生的全原子结构的 CE-RMSD 值小于 2.5Å，其中 6 个目标中的 90%或更多的残基对齐。在初始粗粒退火过程中生成的低能结构聚类可以可靠地选择出与 5 个目标中的 8 个最佳采样结构相差 1Å 以内的结构。在全原子细化之后，可以使用基于能量特征的简单算法从 8 个目标中的 7 个目标中选择与最佳采样结构相差 1Å 以内的结构。
