De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM

Nabin Giri and Jianlin Cheng ()
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Nabin Giri: University of Missouri
Jianlin Cheng: University of Missouri

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Accurately building 3D atomic structures from cryo-EM density maps is a crucial step in cryo-EM-based protein structure determination. Converting density maps into 3D atomic structures for proteins lacking accurate homologous or predicted structures as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated de novo cryo-EM structure modeling method. Cryo2Struct utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps, followed by an innovative Hidden Markov Model (HMM) to connect predicted atoms and build protein backbone structures. Cryo2Struct produces substantially more accurate and complete protein structural models than the widely used ab initio method Phenix. Additionally, its performance in building atomic structural models is robust against changes in the resolution of density maps and the size of protein structures.

Date: 2024
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DOI: 10.1038/s41467-024-49647-6

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