ArticlePatterns (New York, N.Y.)2023
B-factor prediction in proteins using a sequence-based deep learning model.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- From Sequence to Solution: Computational Design of a Multi-Epitope Vaccine Candidate Against Francisella tularensis.Probiotics and antimicrobial proteins · 2026Article
- DeepRMSF: a deep learning-based automated approach for predicting atomic-level flexibility in RNA structure.Briefings in bioinformatics · 2026Article
- Article
- Evolutionary and structural insights into DNMTs and TETs: decoding their functional heterogeneity and oncogenic roles in methylation regulation.BMC molecular and cell biology · 2025Article
- OPUS-BFactor: Predicting Protein B-Factor with Sequence and Structure Information.Molecules (Basel, Switzerland) · 2025Article
- Charting the envelope of mechanical properties of synthetic silk fibers through predictive modeling of the drawing process.Science advances · 2025Article
- Differential Responses in the Core, Active Site and Peripheral Regions of Cytochrome c Peroxidase to Extreme Pressure and Temperature.Journal of molecular biology · 2024Article
- Structural Assessment ofVaccines · 2024Article
- Weak-formulated physics-informed modeling and optimization for heterogeneous digital materials.PNAS nexus · 2024Article
- B-factor prediction in proteins using a sequence-based deep learning model.Patterns (New York, N.Y.) · 2023Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
B factors provide critical insight into protein dynamics. Predicting B factors of an atom in new proteins remains challenging as it is impacted by their neighbors in Euclidean space. Previous learning methods developed have resulted in low Pearson correlation coefficients beyond the training set due to their limited ability to capture the effect of neighboring atoms. With the advances in deep learning methods, we develop a sequence-based model that is tested on 2,442 proteins and outperforms the state-of-the-art models by 30%. We find that the model learns that the B factor of a site is prominently affected by atoms within a 12-15 Å radius, which is in excellent agreement with cutoffs from protein network models. The ablation study revealed that the B factor can largely be predicted from the primary sequence alone. Based on the abovementioned points, our model lays a foundation for predicting other properties that are correlated with the B factor.
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Registered trials
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