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Tran NH, Qiao R, Mao Z, Pan S, Zhang Q, Li W, Xin L, Li M, Shan B. NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing. Mol Cell Proteomics 2024; 23:100849. [PMID: 39321875 PMCID: PMC11532909 DOI: 10.1016/j.mcpro.2024.100849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 08/27/2024] [Accepted: 09/18/2024] [Indexed: 09/27/2024] Open
Abstract
De novo peptide sequencing is one of the most fundamental research areas in mass spectrometry-based proteomics. Many methods have often been evaluated using a couple of simple metrics that do not fully reflect their overall performance. Moreover, there has not been an established method to estimate the false discovery rate (FDR) of de novo peptide-spectrum matches. Here we propose NovoBoard, a comprehensive framework to evaluate the performance of de novo peptide-sequencing methods. The framework consists of diverse benchmark datasets (including tryptic, nontryptic, immunopeptidomics, and different species) and a standard set of accuracy metrics to evaluate the fragment ions, amino acids, and peptides of the de novo results. More importantly, a new approach is designed to evaluate de novo peptide-sequencing methods on target-decoy spectra and to estimate and validate their FDRs. Our FDR estimation provides valuable information to assess the reliability of new peptides identified by de novo sequencing tools, especially when no ground-truth information is available to evaluate their accuracy. The FDR estimation can also be used to evaluate the capability of de novo peptide sequencing tools to distinguish between de novo peptide-spectrum matches and random matches. Our results thoroughly reveal the strengths and weaknesses of different de novo peptide-sequencing methods and how their performances depend on specific applications and the types of data.
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Affiliation(s)
| | - Rui Qiao
- Bioinformatics Solutions Inc, Waterloo, Ontario, Canada
| | - Zeping Mao
- Bioinformatics Solutions Inc, Waterloo, Ontario, Canada; David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada
| | - Shengying Pan
- Bioinformatics Solutions Inc, Waterloo, Ontario, Canada
| | - Qing Zhang
- Bioinformatics Solutions Inc, Waterloo, Ontario, Canada
| | - Wenting Li
- Bioinformatics Solutions Inc, Waterloo, Ontario, Canada
| | - Lei Xin
- Bioinformatics Solutions Inc, Waterloo, Ontario, Canada.
| | - Ming Li
- David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada.
| | - Baozhen Shan
- Bioinformatics Solutions Inc, Waterloo, Ontario, Canada.
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2
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Pounraj S, Chen S, Ma L, Mazzieri R, Dolcetti R, Rehm BHA. Targeting Tumor Heterogeneity with Neoantigen-Based Cancer Vaccines. Cancer Res 2024; 84:353-363. [PMID: 38055891 DOI: 10.1158/0008-5472.can-23-2042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 10/24/2023] [Accepted: 12/04/2023] [Indexed: 12/08/2023]
Abstract
Neoantigen-based cancer vaccines have emerged as a promising immunotherapeutic approach to treat cancer. Nevertheless, the high degree of heterogeneity in tumors poses a significant hurdle for developing a vaccine that targets the therapeutically relevant neoantigens capable of effectively stimulating an immune response as each tumor contains numerous unique putative neoantigens. Understanding the complexities of tumor heterogeneity is crucial for the development of personalized neoantigen-based vaccines, which hold the potential to revolutionize cancer treatment and improve patient outcomes. In this review, we discuss recent advancements in the design of neoantigen-based cancer vaccines emphasizing the identification, validation, formulation, and targeting of neoantigens while addressing the challenges posed by tumor heterogeneity. The review highlights the application of cutting-edge approaches, such as single-cell sequencing and artificial intelligence to identify immunogenic neoantigens, while outlining current limitations and proposing future research directions to develop effective neoantigen-based vaccines.
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Affiliation(s)
- Saranya Pounraj
- Centre for Cell Factories and Biopolymers (CCFB), Griffith Institute for Drug Discovery, Griffith University (Nathan Campus), Brisbane, Queensland, Australia
| | - Shuxiong Chen
- Centre for Cell Factories and Biopolymers (CCFB), Griffith Institute for Drug Discovery, Griffith University (Nathan Campus), Brisbane, Queensland, Australia
| | - Linlin Ma
- Centre for Cell Factories and Biopolymers (CCFB), Griffith Institute for Drug Discovery, Griffith University (Nathan Campus), Brisbane, Queensland, Australia
- School of Environment and Science, Griffith University (Nathan Campus), Brisbane, Queensland, Australia
| | - Roberta Mazzieri
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Riccardo Dolcetti
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Microbiology and Immunology, The University of Melbourne, Melbourne, Victoria, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Bernd H A Rehm
- Centre for Cell Factories and Biopolymers (CCFB), Griffith Institute for Drug Discovery, Griffith University (Nathan Campus), Brisbane, Queensland, Australia
- Menzies Health Institute Queensland (MHIQ), Griffith University (Gold Coast Campus), Queensland, Australia
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3
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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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Bijukumar S, Murugesan T, Dhanapal AR, Mubarak SJ, Vedagiri H, Jayaraman A. Construing recombinant ZFP160 from Aspergillus terreus as pterin deaminase enzyme. Biotechnol Appl Biochem 2023; 70:2150-2162. [PMID: 37766485 DOI: 10.1002/bab.2515] [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: 12/17/2022] [Accepted: 09/09/2023] [Indexed: 09/29/2023]
Abstract
Pterin deaminase stands as a metalloenzyme and exhibits both antitumor and anticancer activities. Therefore, this study aimed to explore the molecular function of zinc finger protein-160 (zfp160) from Aspergillus terreus with its enzyme mechanism in detail. Subsequently, preliminary molecular docking studies on zfp160 from A. terreus were done. Next, the cloning and expression of zfp160 protein were carried out. Following, protein expression was induced and purified through nickel NTA column with imidazole gradient elution. Through the Mascot search engine tool, the expressed protein of MALDI-TOF was confirmed by 32 kDa bands of SDS-PAGE. Furthermore, its enzymatic characterization and biochemical categorization were also explored. The optimum conditions for enzyme were determined to be pH 8, temperature 35°C, km 50 μm with folic acid as substrate, and Vmax of 24.16 (IU/mL). Further, in silico analysis tried to explore the interactions and binding affinity of various substrates to the modeled pterin deaminase from A. terreus. Our results revealed the binding mode of different substrate molecules with pterin deaminase using the approximate scoring functions that possibly correlate with actual experimental binding affinities. Following this, molecular dynamic simulations provided the in-depth knowledge on deciphering functional mechanisms of pterin deaminase over other drugs.
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Affiliation(s)
- Sajitha Bijukumar
- Cancer Therapeutics Laboratory, Department of Microbial Biotechnology, Bharathiar University, Coimbatore, Tamil Nadu, India
| | - Thandeeswaran Murugesan
- Bharathiar Cancer Theranostics Research Centre (BCTRC), RUSA2.0, Bharathiar University, Coimbatore, India
| | - Anand Raj Dhanapal
- Chemistry and Bioprospecting Division, Institute of Forest Genetics and Tree Breeding (IFGTB), Indian Council of Forestry Research and Education (ICFRE), Coimbatore, Tamil Nadu, India
| | - Shoufia Jabeen Mubarak
- Medical Genomics Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, Tamil Nadu, India
| | - Hemamalini Vedagiri
- Medical Genomics Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, Tamil Nadu, India
| | - Angayarkanni Jayaraman
- Cancer Therapeutics Laboratory, Department of Microbial Biotechnology, Bharathiar University, Coimbatore, Tamil Nadu, India
- Bharathiar Cancer Theranostics Research Centre (BCTRC), RUSA2.0, Bharathiar University, Coimbatore, India
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Guarra F, Colombo G. Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens. J Chem Theory Comput 2023; 19:5315-5333. [PMID: 37527403 PMCID: PMC10448727 DOI: 10.1021/acs.jctc.3c00513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Indexed: 08/03/2023]
Abstract
The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.
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Affiliation(s)
- Federica Guarra
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
| | - Giorgio Colombo
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
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Zhang Y, Jian X, Xu L, Zhao J, Lu M, Lin Y, Xie L. iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features. Front Genet 2023; 14:1141535. [PMID: 37229205 PMCID: PMC10203616 DOI: 10.3389/fgene.2023.1141535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/20/2023] [Indexed: 05/27/2023] Open
Abstract
Neoantigens recognized by cytotoxic T cells are effective targets for tumor-specific immune responses for personalized cancer immunotherapy. Quite a few neoantigen identification pipelines and computational strategies have been developed to improve the accuracy of the peptide selection process. However, these methods mainly consider the neoantigen end and ignore the interaction between peptide-TCR and the preference of each residue in TCRs, resulting in the filtered peptides often fail to truly elicit an immune response. Here, we propose a novel encoding approach for peptide-TCR representation. Subsequently, a deep learning framework, namely iTCep, was developed to predict the interactions between peptides and TCRs using fusion features derived from a feature-level fusion strategy. The iTCep achieved high predictive performance with AUC up to 0.96 on the testing dataset and above 0.86 on independent datasets, presenting better prediction performance compared with other predictors. Our results provided strong evidence that model iTCep can be a reliable and robust method for predicting TCR binding specificities of given antigen peptides. One can access the iTCep through a user-friendly web server at http://biostatistics.online/iTCep/, which supports prediction modes of peptide-TCR pairs and peptide-only. A stand-alone software program for T cell epitope prediction is also available for convenient installing at https://github.com/kbvstmd/iTCep/.
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Affiliation(s)
- Yu Zhang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Xingxing Jian
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
- Bioinformatics Center, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Linfeng Xu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
- Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Bio-Diversity Science, School of Life Sciences, Fudan University, Shanghai, China
| | - Jingjing Zhao
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Manman Lu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Yong Lin
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Lu Xie
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
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7
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Xu Y, Qian X, Tong Y, Li F, Wang K, Zhang X, Liu T, Wang J. AttnTAP: A Dual-input Framework Incorporating the Attention Mechanism for Accurately Predicting TCR-peptide Binding. Front Genet 2022; 13:942491. [PMID: 36072653 PMCID: PMC9441555 DOI: 10.3389/fgene.2022.942491] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 06/28/2022] [Indexed: 11/30/2022] Open
Abstract
T-cell receptors (TCRs) are formed by random recombination of genomic precursor elements, some of which mediate the recognition of cancer-associated antigens. Due to the complicated process of T-cell immune response and limited biological empirical evidence, the practical strategy for identifying TCRs and their recognized peptides is the computational prediction from population and/or individual TCR repertoires. In recent years, several machine/deep learning-based approaches have been proposed for TCR-peptide binding prediction. However, the predictive performances of these methods can be further improved by overcoming several significant flaws in neural network design. The interrelationship between amino acids in TCRs is critical for TCR antigen recognition, which was not properly considered by the existing methods. They also did not pay more attention to the amino acids that play a significant role in antigen-binding specificity. Moreover, complex networks tended to increase the risk of overfitting and computational costs. In this study, we developed a dual-input deep learning framework, named AttnTAP, to improve the TCR-peptide binding prediction. It used the bi-directional long short-term memory model for robust feature extraction of TCR sequences, which considered the interrelationships between amino acids and their precursors and postcursors. We also introduced the attention mechanism to give amino acids different weights and pay more attention to the contributing ones. In addition, we used the multilayer perceptron model instead of complex networks to extract peptide features to reduce overfitting and computational costs. AttnTAP achieved high areas under the curves (AUCs) in TCR-peptide binding prediction on both balanced and unbalanced datasets (higher than 0.838 on McPAS-TCR and 0.908 on VDJdb). Furthermore, it had the highest average AUCs in TPP-I and TPP-II tasks compared with the other five popular models (TPP-I: 0.84 on McPAS-TCR and 0.894 on VDJdb; TPP-II: 0.837 on McPAS-TCR and 0.893 on VDJdb). In conclusion, AttnTAP is a reasonable and practical framework for predicting TCR-peptide binding, which can accelerate identifying neoantigens and activated T cells for immunotherapy to meet urgent clinical needs.
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Affiliation(s)
- Ying Xu
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Xinyang Qian
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Yao Tong
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Fan Li
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Ke Wang
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China
- Geneplus Beijing Institute, Beijing, China
| | - Xuanping Zhang
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Tao Liu
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China
- Geneplus Beijing Institute, Beijing, China
| | - Jiayin Wang
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China
- *Correspondence: Jiayin Wang,
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