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.

中文翻译：

---

琥珀和Rosetta能量函数用于蛋白质结构评估的系统比较

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。比较不同派生的能量函数可以分析每个能量函数的优缺点，并提供独立的基准来评估给定能量函数中的改进。在诱饵判别和回路建模测试中，我们将分子力学的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。在诱饵辨别测试中，在许多情况下，Rosetta和Amber（ff14SBonlySC）能量函数在将原始状态评分为最低能量构象时均表现良好，但在talaris2014和Amber ff14SBonlySC评分函数中均发现了一些错误的最小值。Rosetta能量函数的当前默认版本，REF2015在小分子和大分子基准测试台上都经过参数设置，可以改善诱饵的辨别力，其性能明显优于talaris2014，突出了对Rosetta评分方法的改进。在Rosetta REF2015中没有案例，在Amber中没有8/140案例，在替代环境中没有发现错误的最小值。在循环建模测试中，Amber ff14SBonlySC和REF2015的性能相同，尽管在某些情况下，两者都检测到错误的最小值。二面体，静电，溶剂化和氢键分数之间的平衡有助于错误极小值的存在。要利用Rosetta和Amber能量函数的半正交性质，我们开发了一种结合Amber和Rosetta构象排名的技术来预测给定蛋白质的最接近本地模型。该算法改进了从任一能量函数的预测中得出的结果，应有助于结构评估和回路建模任务的模型选择。