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Cited by 5Machine Learning in BioinformaticsProtein Structure and Dynamicsvaccines and immunoinformatics approaches

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StackDPP: a stacking ensemble based DNA-binding protein prediction model

Open access
Ahmed, Sheikh Hasib, Bose, Dibyendu Brinto, Khandoker, Rafi, Rahman, M Saifur·2024·BMC Bioinformatics
DOI 10.1186/s12859-024-05714-9PMC PMC10941422

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Abstract

Background: DNA-binding proteins (DNA-BPs) are the proteins that bind and interact with DNA. DNA-BPs regulate and affect numerous biological processes, such as, transcription and DNA replication, repair, and organization of the chromosomal DNA. Very few proteins, however, are DNA-binding in nature. Therefore, it is necessary to develop an efficient predictor for identifying DNA-BPs. Result: In this work, we have proposed new benchmark datasets for the DNA-binding protein prediction problem. We discovered several quality concerns with the widely used benchmark datasets, PDB1075 (for training) and PDB186 (for independent testing), which necessitated the preparation of new benchmark datasets. Our proposed datasets UNIPROT1424 and UNIPROT356 can be used for model training and independent testing respectively. We have retrained selected state-of-the-art DNA-BP predictors in the new dataset and reported their performance results. We also trained a novel predictor using the new benchmark dataset. We extracted features from various feature categories, then used a Random Forest classifier and Recursive Feature Elimination with Cross-validation (RFECV) to select the optimal set of 452 features. We then proposed a stacking ensemble architecture as our final prediction model. Named Stacking Ensemble Model for DNA-binding Protein Prediction , or StackDPP in short, our model achieved 0.92, 0.92 and 0.93 accuracy in 10-fold cross-validation, jackknife and independent testing respectively. Conclusion: StackDPP has performed very well in cross-validation testing and has outperformed all the state-of-the-art prediction models in independent testing. Its performance scores in cross-validation testing generalized very well in the independent test set. The source code of the model is publicly available at https://github.com/HasibAhmed1624/StackDPP . Therefore, we expect this generalized model can be adopted by researchers and practitioners to identify novel DNA-binding proteins.

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Metadata

Authors
Ahmed, S. H., Bose, D. B., Khandoker, R., & Rahman, M. S.
Affiliations
Bangladesh University of Engineering and Technology (BD)
Corresponding author
Rahman, M Saifur
ORCID
Venue
BMC Bioinformatics
Publisher
Springer Science and Business Media LLC
Published
2024-03-14
Volume
25
Issue
1
Pages
111-111
ISSN
1471-2105 (ISSN-L)
DOI
10.1186/s12859-024-05714-9
PMC
PMC10941422
OpenAlex
W4392817839
Cited by
5
References
2
Citations by year
2026: 1 · 2025: 1 · 2024: 2
FWCI
0.54
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Bioinformatics (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Greater South Information System
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
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
DNA-binding protein prediction, stacking ensemble, benchmark dataset, feature selection, recursive feature elimination, random forest, protein function prediction
MeSH terms
Algorithms, DNA, DNA-Binding Proteins, Software

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