PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features
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Sharma, Alok
Dehzangi, Iman
Tsunoda, Tatsuhiko
Sattar, Abdul
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Abstract
Protein-peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein-peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available at https://github.com/abelavit/PepCNN.git .
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Scientific Reports
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13
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© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Medical biochemistry - proteins and peptides (incl. medical proteomics)
Proteomics and intermolecular interactions (excl. medical proteomics)
Oncology and carcinogenesis
Microbiology
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Chandra, A; Sharma, A; Dehzangi, I; Tsunoda, T; Sattar, A, PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features, Scientific Reports, 2023, 13, pp. 20882