Easymode: general pretrained networks for cellular cryo-ET enable flexible approaches to subtomogram averaging

Abstract

Cellular cryo-electron tomography (cryo-ET) reveals high-resolution details of macromolecules within their native cellular environment. However, in situ cryo-ET datasets are large and highly heterogeneous, which makes comprehensive identification and extraction of the many different elements of cellular architecture for high-resolution analysis a challenging, time-consuming and often tedious task. Here we present easymode, a library of pretrained general segmentation networks for cryo-ET, trained on over 4,000 tilt series spanning a large and diverse variety of sources. Easymode enables in situ structural determination workflows by rendering tomogram content computationally accessible, without requiring any per-dataset training. Beyond directly facilitating high-resolution subtomogram averaging of a selection of widely prevalent complexes, we show how easymode can be used to leverage cellular context in subtomogram averaging workflows, helping identify, align, or filter particle sets, and enabling automated mapping of the cellular landscape surrounding target proteins. We use easymode to determine the in situ structure of rare inosine monophosphate dehydrogenase (IMPDH) filaments at 4.0 A resolution, and to map and visualize the surrounding cellular environment.