J Chem Theory Comput. 2018 Nov 13;14(11):6015-6025. doi: 10.1021/acs.jctc.8b00303. Epub 2018 Oct 12.
An accurate energy function is an essential component of biomolecular structural modeling and design. The comparison of differently derived energy functions enables analysis of the strengths and weaknesses of each energy function and provides independent benchmarks for evaluating improvements within a given energy function. We compared the molecular mechanics Amber empirical energy function to two versions of the Rosetta energy function (talaris2014 and REF2015) in decoy discrimination and loop modeling tests. In decoy discrimination tests, both Rosetta and Amber (ff14SBonlySC) energy functions performed well in scoring the native state as the lowest energy conformation in many cases, but several false minima were found in with both talaris2014 and Amber ff14SBonlySC scoring functions. The current default version of the Rosetta energy function, REF2015, which is parametrized on both small molecule and macromolecular benchmark sets to improve decoy discrimination, performs significantly better than talaris2014, highlighting the improvements made to the Rosetta scoring approach. There are no cases in Rosetta REF2015, and 8/140 cases in Amber, where a false minimum is found that is absent in the alternative landscape. In loop modeling tests, Amber ff14SBonlySC and REF2015 perform equivalently, although false minima are detected in several cases for both. The balance between dihedral, electrostatic, solvation and hydrogen bonding scores contribute to the existence of false minima. To take advantage of the semi-orthogonal nature of the Rosetta and Amber energy functions, we developed a technique that combines Amber and Rosetta conformational rankings to predict the most near-native model for a given protein. This algorithm improves upon predictions from either energy function in isolation and should aid in model selection for structure evaluation and loop modeling tasks.
准确的能量函数是生物分子结构建模和设计的重要组成部分。比较不同的能量函数可以分析每个能量函数的优缺点，并为评估给定能量函数内的改进提供独立的基准。我们将分子力学 Amber 经验能量函数与 Rosetta 能量函数的两个版本（talaris2014 和 REF2015）进行了比较，在诱饵区分和环建模测试中进行了比较。在诱饵区分测试中，Rosetta 和 Amber（ff14SBonlySC）能量函数在许多情况下都能很好地将天然状态评分最低，但是在 talaris2014 和 Amber ff14SBonlySC 评分函数中都发现了几个假最小。当前 Rosetta 能量函数的默认版本 REF2015 是在小分子和大分子基准集上进行参数化的，以提高诱饵区分能力，其性能明显优于 talaris2014，突出了 Rosetta 评分方法的改进。在 Rosetta REF2015 中没有出现，在 Amber 中出现了 8/140 个情况，其中发现了一个假最小，而在另一个景观中不存在。在环建模测试中，Amber ff14SBonlySC 和 REF2015 的性能相当，尽管在这两种情况下都检测到了假最小。二面角、静电、溶剂化和氢键评分的平衡导致了假最小的存在。为了利用 Rosetta 和 Amber 能量函数的半正交性质，我们开发了一种将 Amber 和 Rosetta 构象排名相结合的技术，以预测给定蛋白质最接近天然的模型。该算法改进了单独使用任何一种能量函数的预测，应该有助于结构评估和环建模任务中的模型选择。
