Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning.

Abstract

An increasing number of protein structures have been solved by cryo-electron microscopy (cryo-EM). Although structures determined at near-atomic resolution are now routinely reported, many density maps are still determined at an intermediate resolution, where extracting structure information is still a challenge. We have developed a computational method, Emap2sec, which identifies the secondary structures of proteins (α helices, β sheets, and other structures) in an EM map of 5 to 10 Å resolution. Emap2sec uses a 3D deep convolutional neural network to assign secondary structure to each grid point in an EM map. We tested Emap2sec on 6.0 and 10.0 Å resolution EM maps simulated from 34 structures, as well as on 43 maps determined experimentally at 5.0 to 9.5 Å resolution. Emap2sec was able to clearly identify the secondary structures in many maps tested, and showed substantially better performance than existing methods.