Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information

Xintao Song, Lei Bao, Chenjie Feng, Qiang Huang, Fa Zhang (), Xin Gao () and Renmin Han ()
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Xintao Song: Shandong University
Lei Bao: Hubei University of Medicine
Chenjie Feng: Ningxia Medical University
Qiang Huang: Shandong University
Fa Zhang: Beijing Institute of Technology
Xin Gao: Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Renmin Han: Shandong University

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and produces the best results in a large-scale protein dynamics dataset; this model can accurately infer the dynamic information of a protein in only a few seconds. By learning effectively from experimental protein structure data and cryo-electron microscopy (cryo-EM) data integration, our approach is able to accurately identify the interactive bidirectional constraints and supervision between cryo-EM maps and PDB models in maximizing the dynamic prediction efficacy. Rigorous 5-fold cross-validation on the dataset demonstrates that RMSF-net achieves test correlation coefficients of 0.746 ± 0.127 at the voxel level and 0.765 ± 0.109 at the residue level, showcasing its ability to deliver dynamic predictions closely approximating molecular dynamics simulations. Additionally, it offers real-time dynamic inference with minimal storage overhead on the order of megabytes. RMSF-net is a freely accessible tool and is anticipated to play an essential role in the study of protein dynamics.

Date: 2024
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DOI: 10.1038/s41467-024-49858-x

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