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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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BiCaps-DBP: Predicting DNA-binding proteins from protein sequences using Bi-LSTM and a 1D-capsule network.

Muhammad K N Mursalim¹, Tati L E R Mengko², Rukman Hertadi³

¹School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, 40132, Indonesia; Department of Informatics Engineering, Universal University, Batam, Indonesia.

Computers in Biology and Medicine

|July 12, 2023

Summary

Predicting DNA-binding proteins (DBPs) is crucial for understanding biological processes and disease research. A new deep learning method, BiCaps-DBP, significantly improves prediction accuracy, offering a faster computational alternative to experimental methods.

Keywords:

Bi-LSTM Capsule network DNA-Binding proteins One-hot encoding

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

DNA-binding proteins (DBPs) are vital for numerous cellular functions, including DNA replication, transcription, repair, and splicing.
Identifying DBPs is critical for pharmaceutical research, particularly in human cancers and autoimmune diseases.
Experimental methods for DBP identification are often time-consuming and expensive, necessitating efficient computational approaches.

Purpose of the Study:

To develop a rapid and accurate computational method for predicting DNA-binding proteins (DBPs) from primary sequences.
To introduce BiCaps-DBP, a novel deep learning model designed to enhance DBP prediction performance.

Main Methods:

The study employed a deep learning approach, combining bidirectional long short-term memory (BiLSTM) with a 1D-capsule network.
The BiCaps-DBP model was trained and evaluated on three distinct training and independent datasets to assess its generalizability and robustness.

Main Results:

BiCaps-DBP demonstrated superior performance compared to an existing predictor across three independent datasets.
The proposed model achieved accuracy improvements of 1.05% on PDB2272, 5.79% on PDB186, and 0.40% on PDB20000.
These results highlight the model's effectiveness and reliability in DBP prediction.

Conclusions:

BiCaps-DBP represents a significant advancement in computational DBP prediction.
The method offers a promising and accurate alternative to traditional experimental techniques.
This deep learning approach has the potential to accelerate genomic annotation and drug discovery efforts.