Ibrahim Abdelbaky, Mohamed Elhakeem, Hilal Tayara, Elsayed Badr, Mustafa Abdul Salam
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Enhanced prediction of hemolytic activity in antimicrobial peptides using deep learning-based sequence analysis.
Antimicrobial peptides (AMPs) are a promising class of antimicrobial drugs due to their broad-spectrum activity against microorganisms. However, their clinical application is limited by their potential to cause hemolysis, the destruction of red blood cells. To address this issue, we propose a deep learning model based on convolutional neural networks (CNNs) for predicting the hemolytic activity of AMPs. Peptide sequences are represented using one-hot encoding, and the CNN architecture consists of multiple convolutional and fully connected layers. The model was trained on six different datasets: HemoPI-1, HemoPI-2, HemoPI-3, RNN-Hem, Hlppredfuse, and AMP-Combined, achieving Matthew's correlation coefficients of 0.9274, 0.5614, 0.6051, 0.6142, 0.8799, and 0.7484, respectively. Our model outperforms previously reported methods and can facilitate the development of novel AMPs with reduced hemolytic activity, which is crucial for their therapeutic use in treating bacterial infections.
期刊介绍:
BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology.
BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.