Cited by 91Machine Learning in BioinformaticsProtein Structure and DynamicsComputational Drug Discovery Methods

An overview of the prediction of protein DNA-binding sites

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Si, Jingna, Zhao, Rui, Wu, Rongling·2015·International Journal of Molecular Sciences

Abstract

Interactions between proteins and DNA play an important role in many essential biological processes such as DNA replication, transcription, splicing, and repair. The identification of amino acid residues involved in DNA-binding sites is critical for understanding the mechanism of these biological activities. In the last decade, numerous computational approaches have been developed to predict protein DNA-binding sites based on protein sequence and/or structural information, which play an important role in complementing experimental strategies. At this time, approaches can be divided into three categories: sequence-based DNA-binding site prediction, structure-based DNA-binding site prediction, and homology modeling and threading. In this article, we review existing research on computational methods to predict protein DNA-binding sites, which includes data sets, various residue sequence/structural features, machine learning methods for comparison and selection, evaluation methods, performance comparison of different tools, and future directions in protein DNA-binding site prediction. In particular, we detail the meta-analysis of protein DNA-binding sites. We also propose specific implications that are likely to result in novel prediction methods, increased performance, or practical applications.

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  • Other types of molecules interacting with proteins, as well as protein-protein interactions in membranes, have been studied extensively through the years.

    “While other types of molecules interacting with proteins, as well as protein-protein interactions in membranes, have been studied extensively through the years [14–18]”

    From Challenges in protein docking (2020) · cites this paper as [15]

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Authors
Si, J., Zhao, R., & Wu, R.
Affiliations
  • Center for Computational Biology, National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
  • Beijing Forestry University (CN)
Venue
International Journal of Molecular Sciences
Publisher
MDPI AG
Published
2015-03-06
Volume
16
Issue
3
Pages
5194-5215
ISSN
1422-0067 (ISSN-L)
OpenAlex
W1977628834
Cited by
91
References
102
Citations by year
2026: 4 · 2025: 12 · 2024: 9 · 2023: 9 · 2022: 6 · 2021: 10 · 2020: 7 · 2019: 10 · 2018: 8 · 2017: 8 · 2016: 6 · 2015: 2
FWCI
0.78
Type
journal article
Language
en
Open access
Locations
  • International Journal of Molecular Sciences (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • National Natural Science Foundation of China
  • Northeast Forestry University
  • Beijing Forestry University
  • Fundamental Research Funds for the Central Universities (31401138, TD2012–04)
  • State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords
protein-DNA binding sites, DNA binding site prediction, sequence-based prediction, structure-based prediction, homology modeling, threading, machine learning, protein-DNA interactions
MeSH terms
Algorithms, Animals, Artificial Intelligence, Binding Sites, DNA, DNA-Binding Proteins, Humans, Protein Binding, Computational Biology

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