ArticleNucleic acids research2015
Computational learning on specificity-determining residue-nucleotide interactions.
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 13 papers.
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Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- Predicting the Specificity- Determining Positions of Receptor Tyrosine Kinase Axl.Frontiers in molecular biosciences · 2021Article
- Use Chou's 5-Step Rule to Predict DNA-Binding Proteins with Evolutionary Information.BioMed research international · 2020Article
- iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks.BMC bioinformatics · 2019Article
- Molecular mechanisms of the protein-protein interaction-regulated binding specificity of basic-region leucine zipper transcription factors.Journal of molecular modeling · 2019Article
- Partner-specific prediction of RNA-binding residues in proteins: A critical assessment.Proteins · 2019Article
- Heterodimeric DNA motif synthesis and validations.Nucleic acids research · 2019Article
- DNA Motif Recognition Modeling from Protein Sequences.iScience · 2018Article
- Identification of DNA-protein Binding Sites through Multi-Scale Local Average Blocks on Sequence Information.Molecules (Basel, Switzerland) · 2017Article
- Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape.Bioinformatics (Oxford, England) · 2017Article
- RPI-Bind: a structure-based method for accurate identification of RNA-protein binding sites.Scientific reports · 2017Article
- Predicting protein-binding regions in RNA using nucleotide profiles and compositions.BMC systems biology · 2017Article
- Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition.International journal of molecular sciences · 2016Review
- A Novel Approach to Predict Core Residues on Cancer-Related DNA-Binding Domains.Cancer informatics · 2016Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The protein-DNA interactions between transcription factors and transcription factor binding sites are essential activities in gene regulation. To decipher the binding codes, it is a long-standing challenge to understand the binding mechanism across different transcription factor DNA binding families. Past computational learning studies usually focus on learning and predicting the DNA binding residues on protein side. Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families. The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance. In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein-DNA interactions across different DNA binding families.
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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.