ArticleScientific reports2019
DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 65 citations in OpenAlex.
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- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- Deep Neural Networks Based on Sp7 Protein Sequence Prediction in Peri-Implant Bone Formation.International journal of dentistry · 2025Article
- Therapeutic Vaccination in Lung Cancer: Past Attempts, Current Approaches and Future Promises.Journal of respiratory biology and translational medicine · 2025Article
- IMGT/RobustpMHC: robust training for class-I MHC peptide binding prediction.Briefings in bioinformatics · 2024Article
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- TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning.Briefings in bioinformatics · 2024Article
- Article
- DeepHLApan: A Deep Learning Approach for the Prediction of Peptide-HLA Binding and Immunogenicity.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.Frontiers in immunology · 2024Review
- Prioritizing candidate peptides for cancer vaccines through predicting peptide presentation by HLA-I proteins.Biometrics · 2023Article
- CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks.Communications biology · 2023Article
- ProtInteract: A deep learning framework for predicting protein-protein interactions.Computational and structural biotechnology journal · 2023Article
- Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?Nano today · 2022Article
- A comprehensive analysis of the IEDB MHC class-I automated benchmark.Briefings in bioinformatics · 2022Article
- Identification of T cell antigens in the 21st century, as difficult as ever.Seminars in immunology · 2022Review
- Positional SHAP (PoSHAP) for Interpretation of machine learning models trained from biological sequences.PLoS computational biology · 2022Article
- Physicochemical Heuristics for Identifying High Fidelity, Near-Native Structural Models of Peptide/MHC Complexes.Frontiers in immunology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Interactions between human leukocyte antigens (HLAs) and peptides play a critical role in the human immune system. Accurate computational prediction of HLA-binding peptides can be used for peptide drug discovery. Currently, the best prediction algorithms are neural network-based pan-specific models, which take advantage of the large amount of data across HLA alleles. However, current pan-specific models are all based on the pseudo sequence encoding for modeling the binding context, which is based on 34 positions identified from the HLA protein-peptide bound structures in early works. In this work, we proposed a novel deep convolutional neural network model (DCNN) for HLA-peptide binding prediction, in which the encoding of the HLA sequence and the binding context are both learned by the network itself without requiring the HLA-peptide bound structure information. Our DCNN model is also characterized by its binding context extraction layer and dual outputs with both binding affinity output and binding probability outputs. Evaluation on public benchmark datasets shows that our DeepSeqPan model without HLA structural information in training achieves state-of-the-art performance on a large number of HLA alleles with good generalization capability. Since our model only needs raw sequences from the HLA-peptide binding pairs, it can be applied to binding predictions of HLAs without structure information and can also be applied to other protein binding problems such as protein-DNA and protein-RNA bindings. The implementation code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPan .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.