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Su Z, Wu Y, Cao K, Du J, Cao L, Wu Z, Wu X, Wang X, Song Y, Wang X, Duan H. APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules. Methods 2024; 228:38-47. [PMID: 38772499 DOI: 10.1016/j.ymeth.2024.05.013] [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: 01/27/2024] [Revised: 04/28/2024] [Accepted: 05/18/2024] [Indexed: 05/23/2024] Open
Abstract
Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.
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Affiliation(s)
- Zhihao Su
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Yejian Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Kaiqiang Cao
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Jie Du
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Lujing Cao
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Zhipeng Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Xinyi Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Xinqiao Wang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Ying Song
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Xudong Wang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
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Luo H, Ye H, Ng HW, Shi L, Tong W, Mendrick DL, Hong H. Machine Learning Methods for Predicting HLA-Peptide Binding Activity. Bioinform Biol Insights 2015; 9:21-9. [PMID: 26512199 PMCID: PMC4603527 DOI: 10.4137/bbi.s29466] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Revised: 07/30/2015] [Accepted: 08/02/2015] [Indexed: 11/23/2022] Open
Abstract
As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA–peptide binding are important to study T-cell epitopes, immune reactions, and the mechanisms of adverse drug reactions. We review different types of machine learning methods and tools that have been used for HLA–peptide binding prediction. We also summarize the descriptors based on which the HLA–peptide binding prediction models have been constructed and discuss the limitation and challenges of the current methods. Lastly, we give a future perspective on the HLA–peptide binding prediction method based on network analysis.
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Affiliation(s)
- Heng Luo
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA. ; University of Arkansas at Little Rock/University of Arkansas for Medical Sciences Bioinformatics Graduate Program, Little Rock, AR, USA
| | - Hao Ye
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Hui Wen Ng
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Leming Shi
- Center for Pharmacogenomics, School of Pharmacy, Fudan University, Shanghai, China
| | - Weida Tong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Donna L Mendrick
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
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Liao WWP, Arthur JW. Predicting peptide binding affinities to MHC molecules using a modified semi-empirical scoring function. PLoS One 2011; 6:e25055. [PMID: 21966412 PMCID: PMC3178607 DOI: 10.1371/journal.pone.0025055] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2011] [Accepted: 08/23/2011] [Indexed: 12/19/2022] Open
Abstract
The Major Histocompatibility Complex (MHC) plays an important role in the human immune system. The MHC is involved in the antigen presentation system assisting T cells to identify foreign or pathogenic proteins. However, an MHC molecule binding a self-peptide may incorrectly trigger an immune response and cause an autoimmune disease, such as multiple sclerosis. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. In the present study, we have used the Fresno semi-empirical scoring function and modify the approach to the prediction of peptide-MHC binding by using open-source and public domain software. We apply the method to HLA class II alleles DR15, DR1, and DR4, and the HLA class I allele HLA A2. Our analysis shows that using a large set of binding data and multiple crystal structures improves the predictive capability of the method. The performance of the method is also shown to be correlated to the structural similarity of the crystal structures used. We have exposed some of the obstacles faced by structure-based prediction methods and proposed possible solutions to those obstacles. It is envisaged that these obstacles need to be addressed before the performance of structure-based methods can be on par with the sequence-based methods.
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Affiliation(s)
- Webber W. P. Liao
- Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Jonathan W. Arthur
- Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
- Children's Medical Research Institute, Sydney, New South Wales, Australia
- * E-mail:
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Liao WWP, Arthur JW. Predicting peptide binding to Major Histocompatibility Complex molecules. Autoimmun Rev 2011; 10:469-73. [PMID: 21333759 DOI: 10.1016/j.autrev.2011.02.003] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2011] [Accepted: 02/09/2011] [Indexed: 12/29/2022]
Abstract
The Major Histocompatibility Complex (MHC) constitutes an important part of the human immune system. During infection, pathogenic proteins are processed into peptide fragments by the antigen processing machinery. These peptides bind to MHC molecules and the MHC-peptide complex is then transported to the cell membrane where it elicits an immune response via T-cell binding. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. One of the most challenging aspects of this area of research is understanding the specificity and sensitivity of the binding process. An empirical approach to the problem is unfeasible as there are over 512 billion potential binding peptides for each MHC molecule. Computational approaches offer the promise of predicting peptide binding, thus dramatically reducing the number of peptides proceeding to experimental verification. Various bioinformatic approaches have been developed to predict whether or not a particular peptide will bind to a particular MHC allele. Currently, peptide binding prediction methods can be categorised into three major groups: motif- and scoring matrix-based methods, artificial intelligence- (AI-) based methods, and structure-based methods. The first two are sequence-based approaches and are generally based on common sequence motifs in peptides known to bind to MHC molecules. The structure-based approach concerns the structural features and the distribution of energy between the binding peptide and the MHC molecule. Although knowledge of the molecular structure of the MHC molecules is expected to lead to better predictions of peptide binding, the development of structure-based methods has been relatively slow compared to sequence-based methods. Comparisons of various methods showed that the best sequence-based methods significantly outperform structure-based methods. This may be improved by producing more structures and binding data desperately needed by many alleles, especially class II molecules. On the other hand, the large number of verification methods and indicators used by structure-based studies hinders critical evaluation of the methods. Adopting commonly used assessment procedures can demonstrate the relative performance of structure-based methods in a straightforward comparison with other methods. This review provides an overview of current methods for predicting peptide binding to the MHC, with a focus on structure-based methods, and explores the potential for future development in this area.
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Affiliation(s)
- Webber W P Liao
- Discipline of Medicine, Central Clinical School, University of Sydney, NSW, 2006, Australia
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Abstract
INTRODUCTION A frightening increase in the number of isolated multidrug resistant bacterial strains linked to the decline in novel antimicrobial drugs entering the market is a great cause for concern. Cationic antimicrobial peptides (AMPs) have lately been introduced as a potential new class of antimicrobial drugs, and computational methods utilizing molecular descriptors can significantly accelerate the development of new peptide drug candidates. AREAS COVERED This paper gives a broad overview of peptide and amino-acid scale descriptors available for AMP modeling and highlights which of these are currently being used in quantitative structure-activity relationship (QSAR) studies for AMP optimization. Additionally, some key commercial computational tools are discussed, and both successful and less successful studies are referenced, illustrating some of the challenges facing AMP scientists. Through examples of different peptide QSAR studies, this review highlights some of the missing links and illuminates some of the questions that would be interesting to challenge in a more systematic fashion. EXPERT OPINION Computer-aided peptide QSAR using molecular descriptors may provide the necessary edge to peptide drug discovery, enabling successful design of a new generation anti-infective drug molecules. However, if this wonderful scenario is to play out, computational chemists and peptide microbiologists would need to start playing together and not just side by side.
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Affiliation(s)
- Håvard Jenssen
- Roskilde University, Institute of Science, Systems and Models, Universitetsvej 1, Building 17.1, DK-4000 Roskilde, Denmark +45 4674 2877 ; +45 4674 3010 ;
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