ArticleThe Journal of chemical physics2018
Blind prediction of protein B-factor and flexibility.
Article in The Journal of chemical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- DeepRMSF: a deep learning-based automated approach for predicting atomic-level flexibility in RNA structure.Briefings in bioinformatics · 2026Article
- OPUS-BFactor: Predicting Protein B-Factor with Sequence and Structure Information.Molecules (Basel, Switzerland) · 2025Article
- One bead per residue can describe all-atom protein structures.Structure (London, England : 1993) · 2024Article
- Using graphlet degree vectors to predict atomic displacement parameters in protein structures.Acta crystallographica. Section D, Structural biology · 2023Article
- B-factor prediction in proteins using a sequence-based deep learning model.Patterns (New York, N.Y.) · 2023Article
- Analysis of conformational stability of interacting residues in protein binding interfaces.Protein engineering, design & selection : PEDS · 2023Article
- Persistent Homology for RNA Data Analysis.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- Crystal structure of potato 14-3-3 protein St14f revealed the importance of helix I in StFDL1 recognition.Scientific reports · 2022Article
- BioS2Net: Holistic Structural and Sequential Analysis of Biomolecules Using a Deep Neural Network.International journal of molecular sciences · 2022Article
- Persistent spectral graph.International journal for numerical methods in biomedical engineering · 2020Article
- Atom-specific persistent homology and its application to protein flexibility analysis.Computational and mathematical biophysics · 2020Article
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Authors and funding
2 authors.
Funding
Abstract
The Debye-Waller factor, a measure of X-ray attenuation, can be experimentally observed in protein X-ray crystallography. Previous theoretical models have made strong inroads in the analysis of beta (B)-factors by linearly fitting protein B-factors from experimental data. However, the blind prediction of B-factors for unknown proteins is an unsolved problem. This work integrates machine learning and advanced graph theory, namely, multiscale weighted colored graphs (MWCGs), to blindly predict B-factors of unknown proteins. MWCGs are local features that measure the intrinsic flexibility due to a protein structure. Global features that connect the B-factors of different proteins, e.g., the resolution of X-ray crystallography, are introduced to enable the cross-protein B-factor predictions. Several machine learning approaches, including ensemble methods and deep learning, are considered in the present work. The proposed method is validated with hundreds of thousands of experimental B-factors. Extensive numerical results indicate that the blind B-factor predictions obtained from the present method are more accurate than the least squares fittings using traditional methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.