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Hong N, Jiang D, Wang Z, Sun H, Luo H, Bao L, Song M, Kang Y, Hou T. TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides. J Chem Inf Model 2024. [PMID: 38920330 DOI: 10.1021/acs.jcim.4c00678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
The intricate interaction between major histocompatibility complexes (MHCs) and antigen peptides with diverse amino acid sequences plays a pivotal role in immune responses and T cell activity. In recent years, deep learning (DL)-based models have emerged as promising tools for accelerating antigen peptide screening. However, most of these models solely rely on one-dimensional amino acid sequences, overlooking crucial information required for the three-dimensional (3-D) space binding process. In this study, we propose TransfIGN, a structure-based DL model that is inspired by our previously developed framework, Interaction Graph Network (IGN), and incorporates sequence information from transformers to predict the interactions between HLA-A*02:01 and antigen peptides. Our model, trained on a comprehensive data set containing 61,816 sequences with 9051 binding affinity labels and 56,848 eluted ligand labels, achieves an area under the curve (AUC) of 0.893 on the binary data set, better than state-of-the-art sequence-based models trained on larger data sets such as NetMHCpan4.1, ANN, and TransPHLA. Furthermore, when evaluated on the IEDB weekly benchmark data sets, our predictions (AUC = 0.816) are better than those of the recommended methods like the IEDB consensus (AUC = 0.795). Notably, the interaction weight matrices generated by our method highlight the strong interactions at specific positions within peptides, emphasizing the model's ability to provide physical interpretability. This capability to unveil binding mechanisms through intricate structural features holds promise for new immunotherapeutic avenues.
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Affiliation(s)
- Nanqi Hong
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang 310027, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Huiyong Sun
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing, Jiangsu 210009, China
| | - Hao Luo
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Lingjie Bao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Mingli Song
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Hangzhou, Zhejiang 310058, China
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Balasco N, Tagliamonte M, Buonaguro L, Vitagliano L, Paladino A. Structural and Dynamic-Based Characterization of the Recognition Patterns of E7 and TRP-2 Epitopes by MHC Class I Receptors through Computational Approaches. Int J Mol Sci 2024; 25:1384. [PMID: 38338663 PMCID: PMC10855917 DOI: 10.3390/ijms25031384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/16/2024] [Accepted: 01/19/2024] [Indexed: 02/12/2024] Open
Abstract
A detailed comprehension of MHC-epitope recognition is essential for the design and development of new antigens that could be effectively used in immunotherapy. Yet, the high variability of the peptide together with the large abundance of MHC variants binding makes the process highly specific and large-scale characterizations extremely challenging by standard experimental techniques. Taking advantage of the striking predictive accuracy of AlphaFold, we report a structural and dynamic-based strategy to gain insights into the molecular basis that drives the recognition and interaction of MHC class I in the immune response triggered by pathogens and/or tumor-derived peptides. Here, we investigated at the atomic level the recognition of E7 and TRP-2 epitopes to their known receptors, thus offering a structural explanation for the different binding preferences of the studied receptors for specific residues in certain positions of the antigen sequences. Moreover, our analysis provides clues on the determinants that dictate the affinity of the same epitope with different receptors. Collectively, the data here presented indicate the reliability of the approach that can be straightforwardly extended to a large number of related systems.
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Affiliation(s)
- Nicole Balasco
- Institute of Molecular Biology and Pathology IBPM-CNR c/o Department Chemistry, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy;
| | - Maria Tagliamonte
- Immunological Models Lab, Istituto Nazionale Tumori—Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS)—“Fond. G. Pascale”, Via Mariano Semmola 53, 80131 Napoli, Italy; (M.T.); (L.B.)
| | - Luigi Buonaguro
- Immunological Models Lab, Istituto Nazionale Tumori—Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS)—“Fond. G. Pascale”, Via Mariano Semmola 53, 80131 Napoli, Italy; (M.T.); (L.B.)
| | - Luigi Vitagliano
- Institute of Biostructures and Bioimaging IBB-CNR, Via Pietro Castellino 111, 80131 Napoli, Italy;
| | - Antonella Paladino
- Institute of Biostructures and Bioimaging IBB-CNR, Via Pietro Castellino 111, 80131 Napoli, Italy;
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Glukhov E, Kalitin D, Stepanenko D, Zhu Y, Nguyen T, Jones G, Simmerling C, Mitchell JC, Vajda S, Dill KA, Padhorny D, Kozakov D. MHC-Fine: Fine-tuned AlphaFold for Precise MHC-Peptide Complex Prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.569310. [PMID: 38077000 PMCID: PMC10705405 DOI: 10.1101/2023.11.29.569310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
The precise prediction of Major Histocompatibility Complex (MHC)-peptide complex structures is pivotal for understanding cellular immune responses and advancing vaccine design. In this study, we enhanced AlphaFold's capabilities by fine-tuning it with a specialized dataset comprised by exclusively high-resolution MHC-peptide crystal structures. This tailored approach aimed to address the generalist nature of AlphaFold's original training, which, while broad-ranging, lacked the granularity necessary for the high-precision demands of MHC-peptide interaction prediction. A comparative analysis was conducted against the homology-modeling-based method Pandora [13], as well as the AlphaFold multimer model [8]. Our results demonstrate that our fine-tuned model outperforms both in terms of RMSD (median value is 0.65 Å) but also provides enhanced predicted lDDT scores, offering a more reliable assessment of the predicted structures. These advances have substantial implications for computational immunology, potentially accelerating the development of novel therapeutics and vaccines by providing a more precise computational lens through which to view MHC-peptide interactions.
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Affiliation(s)
- Ernest Glukhov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, 11794, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, 11794, NY, USA
| | - Dmytro Kalitin
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, 11794, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, 11794, NY, USA
| | - Darya Stepanenko
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, 11794, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, 11794, NY, USA
| | - Yimin Zhu
- Department of Computer Science, Stony Brook University, Stony Brook, 11794, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, 11794, NY, USA
| | - Thu Nguyen
- Department of Computer Science, Stony Brook University, Stony Brook, 11794, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, 11794, NY, USA
| | - George Jones
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, 11794, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, 11794, NY, USA
| | - Carlos Simmerling
- Department of Chemistry, Stony Brook University, Stony Brook, 11794, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, 11794, NY, USA
| | - Julie C. Mitchell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, 02215, MA, USA
| | - Ken A. Dill
- Department of Chemistry, Stony Brook University, Stony Brook, 11794, NY, USA
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, 11794, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, 11794, NY, USA
| | - Dzmitry Padhorny
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, 11794, NY, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, 11794, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, 11794, NY, USA
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