PredDBP-Stack: Prediction of DNA-Binding Proteins from HMM Profiles using a Stacked Ensemble Method

Abstract EN

DNA-binding proteins (DBPs) play vital roles in all aspects of genetic activities.

However, the identification of DBPs by using wet-lab experimental approaches is often time-consuming and laborious.

In this study, we develop a novel computational method, called PredDBP-Stack, to predict DBPs solely based on protein sequences.

First, amino acid composition (AAC) and transition probability composition (TPC) extracted from the hidden markov model (HMM) profile are adopted to represent a protein.

Next, we establish a stacked ensemble model to identify DBPs, which involves two stages of learning.

In the first stage, the four base classifiers are trained with the features of HMM-based compositions.

In the second stage, the prediction probabilities of these base classifiers are used as inputs to the meta-classifier to perform the final prediction of DBPs.

Based on the PDB1075 benchmark dataset, we conduct a jackknife cross validation with the proposed PredDBP-Stack predictor and obtain a balanced sensitivity and specificity of 92.47% and 92.36%, respectively.

This outcome outperforms most of the existing classifiers.

Furthermore, our method also achieves superior performance and model robustness on the PDB186 independent dataset.

This demonstrates that the PredDBP-Stack is an effective classifier for accurately identifying DBPs based on protein sequence information alone.