ArticleNucleic acids research2018
Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 17 citations in OpenAlex.
- DRBP-EDP: classification of DNA-binding proteins and RNA-binding proteins using ESM-2 and dual-path neural network.NAR genomics and bioinformatics · 2025Article
- Improving prediction performance of general protein language model by domain-adaptive pretraining on DNA-binding protein.Nature communications · 2024Article
- Identification and validation of a dysregulated TME-related gene signature for predicting prognosis, and immunological properties in bladder cancer.Frontiers in immunology · 2023Article
- Predictive modeling of moonlighting DNA-binding proteins.NAR genomics and bioinformatics · 2022Article
- Host-pathogen protein-nucleic acid interactions: A comprehensive review.Computational and structural biotechnology journal · 2022Review
- The TargetMine Data Warehouse: Enhancement and Updates.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA-binding proteins (DBPs) perform diverse biological functions ranging from transcription to pathogen sensing. Machine learning methods can not only identify DBPs de novo but also provide insights into their DNA-recognition dynamics. However, it remains unclear whether available methods that can accurately predict DNA-binding sites in known DBPs can also identify novel DBPs. Moreover, sequence information is blind to the cellular- and disease-specific contexts of DBP activities, whereas the under-utilized knowledge from public gene expression data offers great promise. To address these issues, we have developed novel methods for predicting DBPs by integrating sequence and gene expression-derived features and applied them to explore human, mouse and Arabidopsis proteomes. While our sequence-based models outperformed the gene expression-based ones, some proteins with weaker DBP-like sequence features were correctly predicted by gene expression-based features, suggesting that these proteins acquire a tangible DBP functionality in a conducive gene expression environment. Analysis of motif enrichment among the co-expressed genes of top 100 candidates DBPs from hitherto unannotated genes provides further avenues to explore their functional associations.
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Registered trials
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