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Cited by 5Protein Structure and DynamicsMachine Learning in BioinformaticsEnzyme Structure and Function

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Prediction of Protein B-Factor Profiles Based on Bidirectional Long Short-Term Memory Network

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Wang, Qianqian, Xiao, Xiongjie, Miao, Zhiwei, Zhang, Xu, Yang, Daiwen, Jiang, Bin, Liu, Maili·2025·IEEE Transactions on Computational Biology and Bioinformatics
DOI 10.1109/tcbbio.2025.3564284

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Abstract

B-factor is a measure of ray attenuation or scattering caused by atomic thermal motion during X-ray diffraction of protein crystal structure. B-factor reflects the vibration of atoms; hence, it is the most common experimental descriptor of protein flexibility and has been extensively applied in the studies of protein dynamics, screening of bioactive small molecules, and protein engineering. The prediction of B-factor profiles has considerable significance for analyzing the dynamic properties of unknown proteins. Deep learning technology has developed rapidly in recent years and has been widely implemented in many research fields, especially structural biology. In this paper, a deep neural network model based on bidirectional long short-term memory (biLSTM) network is proposed to predict the B-factor profile of a protein by combining its sequence-based features and structure-based features. Based on a large dataset of high-resolution proteins, our method predicts the B-factor profiles with an average Pearson correlation coefficient (PCC) of 0.71, and 85% of the B-factor profiles have a PCC greater than 0.6, which indicates a strong correlation between predicted and experimental values. In addition, our method remarkably outperforms the existing methods on four test datasets with different protein sizes.

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Metadata

Authors
Wang, Q., Xiao, X., Miao, Z., Zhang, X., Yang, D., Jiang, B., & Liu, M.
Affiliations
  • Wuhan Institute of Physics and Mathematics (CN)
  • National University of Singapore (SG)
ORCID
Venue
IEEE Transactions on Computational Biology and Bioinformatics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2025-07
Volume
22
Issue
4
Pages
1674-1681
ISSN
  • 1545-5963 (ISSN-L)
  • 2998-4165
DOI
10.1109/tcbbio.2025.3564284
OpenAlex
W4409762847
Cited by
5
References
4
Citations by year
2025: 4
FWCI
1.21
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Computational Biology and Bioinformatics (publishedVersion)
  • ChemRxiv (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • Strategic Priority Research Program of the Chinese Academy of Sciences (XDB0540301)
  • National Key R&D Program of China (2018YFA0704002, 2018YFE0202300)
  • National Natural Science Foundation of China (21991080, 21921004, 21974149, 22327901)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
Keywords
protein flexibility, bidirectional LSTM, protein dynamics, X-ray diffraction, sequence-based features, protein crystal structure

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