Protein Structural Modeling for Electron Microscopy Maps Using VESPER and MAINMAST

Abstract

An increasing number of protein structures are determined by cryo-electron microscopy (cryo-EM) and stored in the Electron Microscopy Data Bank (EMDB). To interpret determined cryo-EM maps, several methods have been developed that model the tertiary structure of biomolecules, particularly proteins. Here we show how to use two such methods, VESPER and MAINMAST, which were developed in our group. VESPER is a method mainly for two purposes: fitting protein structure models into an EM map and aligning two EM maps locally or globally to capture their similarity. VESPER represents each EM map as a set of vectors pointing toward denser points. By considering matching the directions of vectors, in general, VESPER aligns maps better than conventional methods that only consider local densities of maps. MAINMAST is a de novo protein modeling tool designed for EM maps with resolution of 3-5 Å or better. MAINMAST builds a protein main chain directly from a density map by tracing dense points in an EM map and connecting them using a tree-graph structure. This article describes how to use these two tools using three illustrative modeling examples.

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures

“…Early methods to build atomic models from cryo-EM density maps utilized protein structures in the Protein Data Bank 12 to generate theoretical density maps at different resolution, usually referred to as simulated density maps for training and testing. For instance, Cascaded-CNN 14 utilized pdb2mrc from the EMAN2 package 15 , and VESPER 16 utilized pdb2vol from the Situs package 17 to generate simulated density maps. However, simulated density maps lack the complexity of real-world density maps such as high noise, missing density values, and experimental artifacts that arise from protein particle picking 18 errors, or atom movement during image capturing in the cryo-EM data collection.…”

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures

“…For instance, Cascaded-CNN 13 utilized pdb2mrc from the EMAN2 package 14 , and VESPER 15 utilized pdb2vol from the Situs package 16 to generate simulated density maps. However, simulated density maps lack the complexity of real-world density maps such as high noise, missing density values, and experimental artifacts that arise from protein particle picking 17 errors, or atom movement during image capturing in the cryo-EM data collection.…”

“…Early methods for predicting structures from cryo-EM density maps utilized protein structures in the PDB to generate theoretical density maps at different resolution, usually referred to as simulated density maps for training and testing. For instance, Cascaded-CNN [9] utilized pdb2mrc from the EMAN2 package [10], and VESPER [11] utilized pdb2vol from the Situs package [12] to generate simulated density maps. However, simulated density maps lack the complexity of real-world density maps such as high noise, missing density values, and experimental artifacts that arise from protein particle picking [13] errors, electron beam interaction with atoms, or atom movement during image capture in the cryo-EM data collection and preprocessing.…”