Quality assessment and biomolecular structure modeling for cryo-EM using deep learning

Abstract

In recent years, an increasing number of protein and nucleotide structures have been modeled from cryo -electron microscopy (cryo-EM) maps. However, even though the EM map resolution has generally improved steadily over the past years, there are still many situations where modeling errors occur in high-resolution EM maps, or modelers face difficulties in modeling biomolecular structures due to locally low resolution in the map. To address such challenges, we have applied deep learning to three tasks: model quality assessment, protein structure modeling, and DNA/RNA structure modeling in cryo-EM maps. 1: Model Quality Assessment Modeling a protein structure into a cryo-EM map is a challenging task. One of the main difficulties is assigning the correct amino acids to their corresponding positions. Moreover, even with high-quality maps, there is always a risk of human error in the modeling process. To ensure the resulting atomic model is as accurate as possible, it's essential to perform rigorous validation using appropriate methods. To validate protein structure models in cryo-EM maps, our group developed a novel method based on the Deep -learning-based Amino-acid-wise model Quality (DAQ) score. In the DAQ score, the neural network detects specific map features for protein amino acid residue types, Cα atoms, and secondary structures, and computes the likelihood that each residue assignment is correct. By quantifying the incompatibilities between the protein model and the EM map at the amino acid level, the DAQ score provides a more accurate and sensitive measure of model quality compared to other methods [1]. Overall, the DAQ score offers a powerful tool for assessing protein structure models in EM maps and advancing cryo-EM research. The DAQ score can be computed on the Google Colab site (https://bit.ly/daq - score) or local machine by installing the code from ( https://github.com/kiharalab/DAQ). Our group has also recently released the DAQ -Score Database [2] (https