Physics-based computational approaches to predicting the structure of macromolecules such as proteins are gaining increased use, but there are remaining challenges. In the current work, it is demonstrated that in energy-based prediction methods, the degree of optimization of the sampled structures can influence the prediction results. In particular, discrepancies in the degree of local sampling can bias the predictions in favor of the oversampled structures by shifting the local probability distributions of the minimum sampled energies. In simple systems, it is shown that the magnitude of the errors can be calculated from the energy surface, and for certain model systems, derived analytically. Further, it is shown that for energy wells whose forms differ only by a randomly assigned energy shift, the optimal accuracy of prediction is achieved when the sampling around each structure is equal. Energy correction terms can be used in cases of unequal sampling to reproduce the total probabilities that would occur under equal sampling, but optimal corrections only partially restore the prediction accuracy lost to unequal sampling. For multiwell systems, the determination of the correction terms is a multibody problem; it is shown that the involved cross-correlation multiple integrals can be reduced to simpler integrals. The possible implications of the current analysis for macromolecular structure prediction are discussed.

中文翻译：

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基于能量的结构预测中的优化偏差


基于物理的计算方法来预测蛋白质等大分子的结构正在得到越来越多的使用，但仍然存在挑战。目前的工作表明，在基于能量的预测方法中，采样结构的优化程度会影响预测结果。特别是，局部采样程度的差异可能会通过改变最小采样能量的局部概率分布来使预测偏向于过采样结构。在简单系统中，误差的大小可以从能量面计算出来，对于某些模型系统，可以通过分析导出。此外，结果表明，对于其形式仅因随机分配的能量偏移而不同的能量井，当每个结构周围的采样相等时，可以实现最佳的预测精度。在不等采样的情况下可以使用能量校正项来重现等采样下发生的总概率，但最佳校正只能部分恢复因不等采样而损失的预测精度。对于多孔系统，修正项的确定是一个多体问题；结果表明，所涉及的互相关多重积分可以简化为更简单的积分。讨论了当前分析对大分子结构预测的可能影响。