Cross-validation tests for cryo-EM maps using an independent particle set
Sebastian Ortiz, Luka Stanisic, Boris A Rodriguez, Markus Rampp, Gerhard Hummer and Pilar Cossio
Received Date: 10th October 19
Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by providing 3D density maps of proteins and nucleic acids at near-atomic resolution. However, map validation is still an open issue in the field. Despite several efforts from the community, it is possible to overfit the 3D density reconstructions to noisy data. Here, inspired by modern statistics, we develop a novel methodology that uses a small independent particle set to validate the 3D maps. The main idea is to monitor how the map probability evolves over the control set during the refinement. The method is complementary to the gold-standard procedure, which generates two reconstructions at each iteration. We low-pass filter the two reconstructions for different frequency cutoffs, and we calculate the probability of each filtered map given the control set. For high-quality maps, the map probability should increase as a function of the frequency cutoff and of the refinement iteration. We also compute the similarity between the map probability distributions of the two reconstructions. As higher frequencies are included, the map distributions become more dissimilar. We optimized the BioEM software package to perform these calculations, and tested the method over systems ranging from quality data to pure noise. Our results show that cross-validation makes it possible to recognize overfitting. We conclude that cross-validation against a control particle set provides a powerful tool to assess the quality of 3D cryo-EM maps.
Read in full at arXiv.
This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.