WikiSysop: Created page with "== Citation == Song, Xintao / Bao, Lei / Feng, Chenjie / Huang, Qiang / Zhang, Fa / Gao, Xin / Han, Renmin. Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information. 2024. Nature Communications, Vol. 15, No. 1, p. 5538 == Abstract == The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven chal..."

2024-08-16T05:51:17Z

Created page with "== Citation == Song, Xintao / Bao, Lei / Feng, Chenjie / Huang, Qiang / Zhang, Fa / Gao, Xin / Han, Renmin. Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information. 2024. Nature Communications, Vol. 15, No. 1, p. 5538 == Abstract == The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven chal..."

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== Citation ==

Song, Xintao / Bao, Lei / Feng, Chenjie / Huang, Qiang / Zhang, Fa / Gao, Xin / Han, Renmin. Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information. 2024. Nature Communications, Vol. 15, No. 1, p. 5538

== 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 crossvalidation
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 realtime
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.

== Keywords ==

== Links ==

https://www.nature.com/articles/s41467-024-49858-x

== Related software ==

== Related methods ==

== Comments ==

2024Song RMSFNet - Revision history