Novel convergence-oriented approach for evaluation and optimization of workflow in single-particle two-dimensional averaging of electron microscope images

Three-dimensional (3D) protein structures facilitate the understanding of their biological functions and provide valuable information for developing medicines. Single-particle analysis (SPA) from electron microscopy (EM) is a structure determination method suitable for macromolecules. To achieve a high resolution using combinations of several SPA software packages, 'workflow' optimization and comparative evaluation by scoring results are essential. Two-dimensional (2D) averaging is a key step for 3D reconstruction. The integrated convergence-evaluation oriented system (IC-EOS) proposed here provides an effective tool for customizing 2D averaging. This assesses the behavior and characteristics of workflows and evaluates the convergence of iteration steps without human intervention. We chose five base measurements for quantifying convergence: resolution, variance, similarity, shift-distance and rotation-angle. Curve fitting to history graphs scored their stability. We call this score 'fluctuation'. The number of particle images discarded from the library and the number of classification groups were examined to see their effects on optimization levels and fluctuation of measurements, allowing the IC-EOS to select the most appropriate workflow for the target. A case study using a bacterial sodium channel and a simulation study using GroEL showed that resolution of 2D averaging improved with relatively stricter particle selection. With fewer groups, resolutions of class averages improved, but similarities between class-averages and their constituent particle images degraded. Fluctuation was useful for selecting adequate conditions, even when achieved values alone were not conclusive. The vote method, using fluctuation, was robust against noise and enabled a decision without exhaustive search trials. Thus, the IC-EOS is a step toward full automation of SPA.