ArticleBioinformatics (Oxford, England)2016
Structure-based prediction of transcription factor binding specificity using an integrative energy function.
Article in Bioinformatics (Oxford, England), 2016. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed, 23 citations in OpenAlex.
- The combinatorial binding syntax of transcription factors in forebrain-specific enhancers.Biology open · 2025Article
- Structure-based learning to predict and model protein-DNA interactions and transcription-factor co-operativity inNAR genomics and bioinformatics · 2024Article
- Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation.Nucleic acids research · 2024Article
- MSALigMap-A Tool for Mapping Active-Site Amino Acids in PDB Structures onto Known and Novel Unannotated Homologous Sequences with Similar Function.Life (Basel, Switzerland) · 2022Article
- Insights into protein-DNA interactions from hydrogen bond energy-based comparative protein-ligand analyses.Proteins · 2022Article
- Description of the calf thymus DNA-malathion complex behavior by multi-spectroscopic and molecular modeling techniques: EMF at low and high frequency approaches.Iranian journal of basic medical sciences · 2021Article
- Review
- New insights into protein-DNA binding specificity from hydrogen bond based comparative study.Nucleic acids research · 2019Article
- An SVM-based method for assessment of transcription factor-DNA complex models.BMC bioinformatics · 2018Article
- A novel method for improved accuracy of transcription factor binding site prediction.Nucleic acids research · 2018Article
- Performance evaluation for MOTIFSIM.Biological procedures online · 2018Article
- An efficient algorithm for improving structure-based prediction of transcription factor binding sites.BMC bioinformatics · 2017Article
- Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
unlabelledTranscription factors (TFs) regulate gene expression through binding to specific target DNA sites. Accurate annotation of transcription factor binding sites (TFBSs) at genome scale represents an essential step toward our understanding of gene regulation networks. In this article, we present a structure-based method for computational prediction of TFBSs using a novel, integrative energy (IE) function. The new energy function combines a multibody (MB) knowledge-based potential and two atomic energy terms (hydrogen bond and π interaction) that might not be accurately captured by the knowledge-based potential owing to the mean force nature and low count problem. We applied the new energy function to the TFBS prediction using a non-redundant dataset that consists of TFs from 12 different families. Our results show that the new IE function improves the prediction accuracy over the knowledge-based, statistical potentials, especially for homeodomain TFs, the second largest TF family in mammals. CONTACT: jguo4@uncc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.