SL-PseAAC: A super learner based DNA-binding protein prediction model

Abstract

Abstract Background: DNA-binding proteins (DNA-BPs) are the proteins that bind and interact with DNA. DNA-BPs play a critical role in all aspects of genetic activity. DNA-BPs regulate and affect the processes of transcription and DNA replication, repair, and recombination by organizing the chromosomal DNA as well as various cellular processes. 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 datasets and reported their performance results. We also trained a novel predictor using the new benchmark datasets. 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 super learner architecture as our final prediction model. Named Super Learner Using Pseudo Amino Acid Composition, or SL-PseAAC in short, our model achieved 91.86%, 92.14% and 92.70% accuracy in 10-fold cross-validation, jackknife and independent testing respectively. Conclusion: SL-PseAAC 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/SL-PseAAC. Therefore, we expect this generalized model can be adopted by researchers and practitioners to identify novel DNA-binding proteins.