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Deepstack-ACE: A deep stacking-based ensemble learning framework for the accelerated discovery of ACE inhibitory

Phasit Charoenkwan¹, Pramote Chumnanpuen², Nalini Schaduangrat³

¹Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand.

Methods (San Diego, Calif.)

|December 21, 2024

Summary

Deepstack-ACE, a novel deep learning framework, accurately identifies angiotensin-I-converting enzyme (ACE) inhibitory peptides. This computational approach aids in drug development by efficiently characterizing potential ACE inhibitors.

Keywords:

ACE inhibitory peptide Bioinformatics Deep learning Machine learning Stacking strategy Word2vec

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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Area of Science:

Biochemistry and Bioinformatics
Computational Drug Discovery
Machine Learning in Pharmacology

Background:

Accurate identification of angiotensin-I-converting enzyme (ACE) inhibitory peptides is vital for understanding the renin-angiotensin system and developing new pharmaceuticals.
Experimental methods for peptide identification are complex and time-consuming, necessitating efficient computational approaches.
In silico methods offer a high-throughput solution for characterizing ACE inhibitory peptides.

Purpose of the Study:

To propose and evaluate Deepstack-ACE, a novel deep stacking-based ensemble learning framework for precise identification of ACE inhibitory peptides.
To leverage deep learning techniques for enhanced accuracy and robustness in predicting peptide-ACE inhibitory activity.
To provide a user-friendly computational tool for researchers in drug discovery.

Main Methods:

Utilized word2vec embedding to generate sequence representations from peptide sequences.
Trained five deep learning models (LSTM, CNN, MLP, GRU, RNN) as base-classifiers.
Constructed an optimized stacked ensemble model by combining the best base-classifiers.
Benchmarked Deepstack-ACE against base-classifiers and conventional machine learning methods.

Main Results:

Deepstack-ACE demonstrated superior accuracy and robustness in identifying ACE inhibitory peptides compared to individual base-classifiers and conventional methods.
Achieved high performance in independent testing with a balanced accuracy of 0.916, sensitivity of 0.911, and MCC of 0.826.
Outperformed current state-of-the-art methods in predicting ACE inhibitory peptides.

Conclusions:

Deepstack-ACE offers a faster and highly accurate computational method for identifying ACE inhibitory peptides.
The developed web server provides accessible and efficient in silico analysis for drug discovery.
This framework has the potential to significantly accelerate the characterization of novel ACE inhibitors.