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

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, i...

Full description

Saved in:

Bibliographic Details
Published in:	BMC bioinformatics Vol. 25; no. 1; p. 111
Main Authors:	Ahmed, Sheikh Hasib, Bose, Dibyendu Brinto, Khandoker, Rafi, Rahman, M Saifur
Format:	Journal Article
Language:	English
Published:	England BioMed Central Ltd 14-03-2024 BioMed Central BMC
Subjects:	Amino acids Analysis Benchmarks Binding proteins Biological activity Classification Data imbalance Datasets Deep learning Deoxyribonucleic acid DNA DNA binding proteins DNA biosynthesis DNA repair DNA replication DNA-binding protein Genetic transcription Genomics Prediction models Protein binding Proteins Recursive feature elimination Sequence identity Source code Support vector machines Training Transcription factors Bangladesh DNA-binding protein Sequence identity Recursive feature elimination Classification Data imbalance
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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. 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. 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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-024-05714-9