Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network

Resource type
Authors/contributors
Title
Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network
Abstract
Ligand peptides that have high affinity for ion channels are critical for regulating ion flux across the plasma membrane. These peptides are now being considered as potential drug candidates for many diseases, such as cardiovascular disease and cancers. In this work, we developed Multi-Branch-CNN, a CNN method with multiple input branches for identifying three types of ion channel peptide binders (sodium, potassium, and calcium) from intra- and inter-feature types. As for its real-world applications, prediction models that are able to recognize novel sequences having high or low similarities to training sequences are required. To this end, we tested our models on two test sets: a general test set including sequences spanning different similarity levels to those of the training set, and a novel-test set consisting of only sequences that bear little resemblance to sequences from the training set. Our experiments showed that the Multi-Branch-CNN method performs better than thirteen traditional ML algorithms (TML13), yielding an improvement in accuracy of 3.2%, 1.2%, and 2.3% on the test sets as well as 8.8%, 14.3%, and 14.6% on the novel-test sets for sodium, potassium, and calcium ion channels, respectively. We confirmed the effectiveness of Multi-Branch-CNN by comparing it to the standard CNN method with one input branch (Single-Branch-CNN) and an ensemble method (TML13-Stack). The data sets, script files to reproduce the experiments, and the final predictive models are freely available at https://github.com/jieluyan/Multi-Branch-CNN.
Publication
Computers in Biology and Medicine
Volume
147
Pages
105717
Date
2022-08-01
Journal Abbr
Computers in Biology and Medicine
Language
en
DOI
10.1016/j.compbiomed.2022.105717
ISSN
0010-4825
Short Title
Multi-Branch-CNN
Accessed
9/21/22, 5:32 AM
Library Catalog
ScienceDirect
Extra
3 citations (Crossref) [2022-09-21]
Citation
Yan, J., Zhang, B., Zhou, M., Kwok, H. F., & Siu, S. W. I. (2022). Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network. Computers in Biology and Medicine, 147, 105717. https://doi.org/10.1016/j.compbiomed.2022.105717