Single particle analysis has become a key structural biology technique. Experimental images are extremely noisy, and during iterative refinement it is possible to stably incorporate noise into the reconstruction. Such “over-fitting” can lead to misinterpretation of the structure and flawed biological results. Several strategies are routinely used to prevent over-fitting, the most common being independent refinement of two sides of a split dataset. In this study, we show that over-fitting remains an issue within regions of low local signal-to-noise, despite independent refinement of half datasets. We propose a modification of the refinement process through the application of a local signal-to-noise filter: SIDESPLITTER. We show that our approach can reduce over-fitting for both idealised and experimental data while maintaining independence between the two sides of a split refinement. SIDESPLITTER refinement leads to improved density, and can also lead to improvement of the final resolution in extreme cases where datasets are prone to severe over-fitting, such as small membrane proteins.

单粒子分析已成为一项关键的结构生物学技术。实验图像非常嘈杂，在迭代细化过程中，可以将噪声稳定地纳入重建。这种“过度拟合”会导致对结构的误解和有缺陷的生物学结果。通常使用几种策略来防止过度拟合，最常见的是对拆分的两侧进行独立细化数据集。在这项研究中，我们表明，尽管对半数据集进行了独立细化，但在低局部信噪比的区域内，过度拟合仍然是一个问题。我们建议通过应用本地信噪比滤波器对细化过程进行修改：SIDESPLITTER。我们表明，我们的方法可以减少理想化数据和实验数据的过度拟合，同时保持拆分细化两侧之间的独立性。SIDESPLITTER 细化可以提高密度，并且在数据集容易出现严重过度拟合的极端情况下（例如小膜蛋白）也可以提高最终分辨率。