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- W4386468560 abstract "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, and pre-trained transformer 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 ." @default.
- W4386468560 created "2023-09-07" @default.
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- W4386468560 date "2023-09-06" @default.
- W4386468560 modified "2023-10-02" @default.
- W4386468560 title "Deep Learning for Protein Peptide binding Prediction: Incorporating Sequence, Structural and Language Model Features" @default.
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- W4386468560 doi "https://doi.org/10.1101/2023.09.02.556055" @default.
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