ArticleNucleic acids research2015
DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 33 citations in OpenAlex.
- PLM-DBPs: enhancing plant DNA-binding protein prediction by integrating sequence-based and structure-aware protein language models.Briefings in bioinformatics · 2025Article
- Accurate prediction of nucleic acid binding proteins using protein language model.Bioinformatics advances · 2025Article
- RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes.Computational and structural biotechnology journal · 2024Article
- ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins.Protein science : a publication of the Protein Society · 2024Article
- Improved prediction of DNA and RNA binding proteins with deep learning models.Briefings in bioinformatics · 2024Article
- Single-Stranded DNA Binding Proteins and Their Identification Using Machine Learning-Based Approaches.Biomolecules · 2022Review
- PredDBP-Stack: Prediction of DNA-Binding Proteins from HMM Profiles using a Stacked Ensemble Method.BioMed research international · 2020Article
- HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection.Computational and mathematical methods in medicine · 2020Article
- Genomic insights into HSFs as candidate genes for high-temperature stress adaptation and gene editing with minimal off-target effects in flax.Scientific reports · 2019Article
- An improved deep learning method for predicting DNA-binding proteins based on contextual features in amino acid sequences.PloS one · 2019Article
- Detection of nucleic acid-protein interactions in plant leaves using fluorescence lifetime imaging microscopy.Nature protocols · 2017Article
- Use of Natural Diversity and Biotechnology to Increase the Quality and Nutritional Content of Tomato and Grape.Frontiers in plant science · 2017Review
- On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach.PloS one · 2017Article
- Improved detection of DNA-binding proteins via compression technology on PSSM information.PloS one · 2017Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
There are currently 151 plants with draft genomes available but levels of functional annotation for putative protein products are low. Therefore, accurate computational predictions are essential to annotate genomes in the first instance, and to provide focus for the more costly and time consuming functional assays that follow. DNA-binding proteins are an important class of proteins that require annotation, but current computational methods are not applicable for genome wide predictions in plant species. Here, we explore the use of species and lineage specific models for the prediction of DNA-binding proteins in plants. We show that a species specific support vector machine model based on Arabidopsis sequence data is more accurate (accuracy 81%) than a generic model (74%), and based on this we develop a plant specific model for predicting DNA-binding proteins. We apply this model to the tomato proteome and demonstrate its ability to perform accurate high-throughput prediction of DNA-binding proteins. In doing so, we have annotated 36 currently uncharacterised proteins by assigning a putative DNA-binding function. Our model is publically available and we propose it be used in combination with existing tools to help increase annotation levels of DNA-binding proteins encoded in plant genomes.
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Registered trials
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.