PredDRBP-MLP: Prediction of DNA-binding proteins and RNA-binding proteins by multilayer perceptron.

Area of Science:

• Bioinformatics
• Computational Biology
• Machine Learning in Biology

Background:

• Proteins interacting with DNA (DNA-binding proteins, DBP) and RNA (RNA-binding proteins, RBP) are crucial for cellular functions.
• Accurate classification of DBPs, RBPs, and non-nucleic acid-binding proteins (NNABP) is vital for understanding biological processes.
• Existing CNN-BiLSTM models for DBP/RBP classification are computationally intensive and time-consuming.

Purpose of the Study:

• To develop an efficient and accurate artificial learning model for classifying DBPs, RBPs, and NNABPs.
• To introduce PredDRBP-MLP, a multilayer perceptron (MLP) based predictor, as an alternative to existing methods.
• To provide a faster and less resource-intensive solution for protein classification.

Main Methods:

• Development of PredDRBP-MLP, an artificial learning model utilizing a multilayer perceptron (MLP) architecture.
• Implementation of a multi-class classification approach for DBPs, RBPs, and NNABPs.
• Evaluation of PredDRBP-MLP performance on an independent dataset, comparing it with existing predictors.

Main Results:

• PredDRBP-MLP demonstrated successful classification, particularly excelling in the NNABP class compared to other predictors.
• In the NNABP class, PredDRBP-MLP achieved a precision of 0.578, recall of 0.522, and F1-score of 0.549.
• The MLP-based model requires lower processing power and trains significantly faster than CNN-BiLSTM models.
• A desktop application for PredDRBP-MLP was developed and is freely accessible.

Conclusions:

• PredDRBP-MLP offers a computationally efficient and effective method for classifying DNA-binding proteins, RNA-binding proteins, and non-nucleic acid-binding proteins.
• The developed MLP model provides a valuable tool for bioinformatics research, offering improved performance and speed.
• The accessibility of the PredDRBP-MLP desktop application facilitates its use in biological research.