The suite of programs developed by our group for accurately identifying protein secondary structures.
(Emap2Sec), protein nucleic acid (RNA/DNA) (Emap2Sec+), full atom 3D model MAINMAST or
segmentation of individual components (MainMastSeg), improved protein modeling from GAN-modified EM maps (EM-GAN), model quality assessment with (DAQ-Score), and real-time comparison and analysis (EM-Surfer) of cryo-Electron Microscopy (EM) maps.

TOOLS

List of our cryo-EM Modeling Tools

Emap2Sec

Emap2sec uses deep learning to accurately identify secondary structures (alpha helices, beta sheets, coils/turn) in cryo-EM maps of medium to low resolution.

Emap2Sec+

Emap2sec uses deep learning to accurately identify protein secondary structures and nucleic acid (RNA/DNA), in cryo-EM maps of medium to low resolution.

MainMast

MAINchain Model trAcing using Spanning Tree from a EM map, directly traces main-chain connections and C-alpha positions by using Tree-Graph models.

MainMast_Seg

Minimum Spanning Tree based EM map segmentation with Symmetry restraints. MAINMASTseg directly segment out individual components from Electron Microscopy (EM) density map.

EM-GAN

EM-GAN is a computational tool, which enables capturing protein structure information from cryo-EM maps more effectively than raw maps. It is based on 3D deep learning. It is aimed to help protein structure modeling from cryo-EM maps.

DAQ-Score

DAQ-Score (Deep learning-based Amino acid-wise Quality assessment score) evaluates agreement of a protein model with detected amino acid residues and other structural features in a density map by deep learning.

EM-SURFER

A web-based tool for real-time comparison and analysis of Electron Microscopy (EM) density maps. It compares the shape of EM map isosurfaces, generated using author-recommended contour values.