DeepHIV: A Sequence-Based Deep Learning Model for Predicting HIV-1 Protease Cleavage Sites

Dongxu Li

doi:10.1109/TCBBIO.2025.3610881

DeepHIV: A Sequence-Based Deep Learning Model for Predicting HIV-1 Protease Cleavage Sites

2025-09-16

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DOI：10.1109/TCBBIO.2025.3610881

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Human immunodeficiency virus type 1 (HIV-1) is one of the main causative agents of acquired immunodeficiency syndrome (AIDS), and effectively identifying HIV-1 protease cleavage sites (PCSs) is of great importance for the design of new anti-AIDS inhibitors. Computational prediction of HIV-1 PCSs can be used to discover new cleavable substrates, and further facilitates the understanding of substrate specificity. A novel deep learning model, namely DeepHIV, is designed to predict HIV-1 PCSs from substrate sequence information alone. In particular, DeepHIV first applies a convolutional neural network combined with an attention mechanism to capture the rich contextual information of position-specific amino acids in the substrate sequences, thus improving the quality of features learned for substrates. Considering the imbalance observed between cleavable and uncleavable substrates, a biased support vector machine is adopted as the classifier of DeepHIV to complete the prediction task. Experimental results demonstrate that DeepHIV outperforms several state-of-the-art prediction methods across all benchmark datasets and evaluation metrics. Hence, DeepHIV is an accurate and robust tool to predict HIV-1 PCSs. Moreover, the promising predictive performance of DeepHIV also reveals that our deep learning model is capable of fully leveraging the sequence information to effectively learn the latent features of substrates.

Keywords:

HIV-1 protease

cleavage site prediction

deep learning

protein sequence

substrate specificity

DeepHIV: A Sequence-Based Deep Learning Model for Predicting HIV-1 Protease Cleavage Sites

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