AMP-CapsNet: a multi-view feature fusion approach for antimicrobial peptide prediction using capsule networks

TL;DR

This paper introduces a new deep learning model called AMP-CapsNet that improves the prediction of antimicrobial peptides, which could help in developing new antibiotics.

Contribution

The novel contribution is the development of AMP-CapsNet, a capsule neural network approach that outperforms existing methods in predicting antimicrobial peptides.

Findings

AMP-CapsNet achieved 97.29% accuracy and 98.91% AUC using dipeptide composition features.

The model outperformed other deep learning and baseline models in predicting antimicrobial peptides.

The method shows promise for enhancing AMP drug discovery through improved prediction accuracy.

Abstract

Antimicrobial peptides (AMPs) are universally found in both intracellular and extracellular settings and have significant antibiotic-resistant bacteria are becoming a bigger problem. In medical laboratories, it has shown notable anti-bacterial effectiveness in treating diabetic foot infections and related issues. New medication development frequently targets (AMPs), which are certainly ensuing components of adaptive immune system. The findings of this research employs deep learning to identify antibiotic activity. Numerous computational methods have been established to detect antimicrobial peptides via deep learning algorithms. We introduced a novel deep learning approach called antimicrobial peptides using Capsule Neural Network (AMP-CapsNet) to precisely forecast them and evaluated its efficacy against deep learning and baseline models. AMPs prediction using capsule neural networks, a type of next generation neural network, to build prediction models. Additionally, we utilized Amino Acid Composition (AAC) for effective features encoded method and as well as dipeptide composition (DPC). Every model underwent independent cross-validation and external testing. The findings indicate that the enhanced AMP-CapsNet deep learning model surpassed its counterparts, achieving an accuracy of 97.29% and an AUC score of 98.91% on the test set using with dipeptide Composition (DPC). The proposed AMP-CapsNet demonstrates superior performance of the testing set achieved accuracy 97.29% score with DPC and accuracy 84.42% score with AAC approach. Consequently, the technique we advocate is anticipated to enhance the accuracy of antimicrobial peptide predictions in the future. By producing powerful peptides for medication development and application, this study advances deep learning-based AMP drug discovery approaches. This finding has important ramifications for how biological data is processed and how pharmacology is calculated.

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