ParaDeep: Sequence-Based Deep Learning for Residue-Level Paratope Prediction Using Chain-Aware BiLSTM-CNN Models

Piyachat Udomwong^1*Thanathat Pamonsupornwichit²Kanchanok Kodchakorn^3,4Chatchai Tayapiwatana^2,5*

• ¹Chiang Mai University, International College of Digital Innovation, Chiang Mai, Thailand
• ²Center of Biomolecular Therapy and Diagnostic, Chiang Mai University Faculty of Associated Medical Sciences, Chiang Mai, Thailand
• ³Chiang Mai University, Office of Research Administration, Chiang Mai, Thailand
• ⁴Chiang Mai University Department of Chemistry, Chiang Mai, Thailand
• ⁵Division of Clinical Immunology, Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand

Accurate prediction of antibody paratopes is a critical challenge in structure-limited, high-throughput discovery workflows. We present ParaDeep, a lightweight and interpretable deep learning framework for residue-level paratope prediction directly from amino acid sequences. ParaDeep integrates bidirectional long short-term memory networks with one-dimensional convolutional layers to capture both long-range sequence context and local binding motifs. We systematically evaluated 30 model configurations varying in encoding schemes, convolutional kernel sizes, and antibody chain types. In five-fold cross-validation, heavy (H) chain models achieved the highest performance (F1 = 0.856 ± 0.014, MCC = 0.842 ± 0.015), outperforming light (L) chain models (F1 = 0.774 ± 0.023, MCC = 0.772 ± 0.022). On an independent blind test set, ParaDeep attained F1 = 0.723 and MCC = 0.685 for H chains, and F1 = 0.607 and MCC = 0.587 for L chains, representing a 27% MCC improvement over the sequence-based baseline Parapred. Chain-specific modeling revealed that heavy chains provide stronger sequence-based predictive signals, while light chains benefit more from structural context. ParaDeep approaches the performance of state-of-the-art structure-based methods on heavy chains while requiring only sequence input, enabling faster and broader applicability without the computational cost of 3D modeling. Its efficiency and scalability make it well-suited for early-stage antibody discovery, repertoire profiling, and therapeutic design, particularly in the absence of structural data. The implementation is freely available at https://github.com/PiyachatU/ParaDeep, with Python (PyTorch) code and a Google Colab interface for ease of use.