1
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Madsen AV, Mejias-Gomez O, Pedersen LE, Preben Morth J, Kristensen P, Jenkins TP, Goletz S. Structural trends in antibody-antigen binding interfaces: a computational analysis of 1833 experimentally determined 3D structures. Comput Struct Biotechnol J 2024; 23:199-211. [PMID: 38161735 PMCID: PMC10755492 DOI: 10.1016/j.csbj.2023.11.056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 11/27/2023] [Accepted: 11/28/2023] [Indexed: 01/03/2024] Open
Abstract
Antibodies are attractive therapeutic candidates due to their ability to bind cognate antigens with high affinity and specificity. Still, the underlying molecular rules governing the antibody-antigen interface remain poorly understood, making in silico antibody design inherently difficult and keeping the discovery and design of novel antibodies a costly and laborious process. This study investigates the characteristics of antibody-antigen binding interfaces through a computational analysis of more than 850,000 atom-atom contacts from the largest reported set of antibody-antigen complexes with 1833 nonredundant, experimentally determined structures. The analysis compares binding characteristics of conventional antibodies and single-domain antibodies (sdAbs) targeting both protein- and peptide antigens. We find clear patterns in the number antibody-antigen contacts and amino acid frequencies in the paratope. The direct comparison of sdAbs and conventional antibodies helps elucidate the mechanisms employed by sdAbs to compensate for their smaller size and the fact that they harbor only half the number of complementarity-determining regions compared to conventional antibodies. Furthermore, we pinpoint antibody interface hotspot residues that are often found at the binding interface and the amino acid frequencies at these positions. These findings have direct potential applications in antibody engineering and the design of improved antibody libraries.
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Affiliation(s)
- Andreas V. Madsen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Oscar Mejias-Gomez
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Lasse E. Pedersen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - J. Preben Morth
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Peter Kristensen
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Timothy P. Jenkins
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Steffen Goletz
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
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2
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Wang C, Wang J, Song W, Luo G, Jiang T. EpiScan: accurate high-throughput mapping of antibody-specific epitopes using sequence information. NPJ Syst Biol Appl 2024; 10:101. [PMID: 39251627 DOI: 10.1038/s41540-024-00432-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 08/27/2024] [Indexed: 09/11/2024] Open
Abstract
The identification of antibody-specific epitopes on virus proteins is crucial for vaccine development and drug design. Nonetheless, traditional wet-lab approaches for the identification of epitopes are both costly and labor-intensive, underscoring the need for the development of efficient and cost-effective computational tools. Here, EpiScan, an attention-based deep learning framework for predicting antibody-specific epitopes, is presented. EpiScan adopts a multi-input and single-output strategy by designing independent blocks for different parts of antibodies, including variable heavy chain (VH), variable light chain (VL), complementary determining regions (CDRs), and framework regions (FRs). The block predictions are weighted and integrated for the prediction of potential epitopes. Using multiple experimental data samples, we show that EpiScan, which only uses antibody sequence information, can accurately map epitopes on specific antigen structures. The antibody-specific epitopes on the receptor binding domain (RBD) of SARS coronavirus 2 (SARS-CoV-2) were located by EpiScan, and the potentially valuable vaccine epitope was identified. EpiScan can expedite the epitope mapping process for high-throughput antibody sequencing data, supporting vaccine design and drug development. Availability: For the convenience of related wet-experimental researchers, the source code and web server of EpiScan are publicly available at https://github.com/gzBiomedical/EpiScan .
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Affiliation(s)
- Chuan Wang
- School of Life Sciences, Sun Yat-sen University, Guangzhou, China
- Guangzhou National Laboratory, Guangzhou, China
| | | | - Wenjun Song
- Guangzhou National Laboratory, Guangzhou, China
- Institute of Integration of Traditional and Western Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Guanzheng Luo
- School of Life Sciences, Sun Yat-sen University, Guangzhou, China.
| | - Taijiao Jiang
- Guangzhou National Laboratory, Guangzhou, China.
- State Key Laboratory of Respiratory Disease, The Key laboratory of Advanced Interdisciplinary Studies Center, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
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3
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Carroll M, Rosenbaum E, Viswanathan R. Computational Methods to Predict Conformational B-Cell Epitopes. Biomolecules 2024; 14:983. [PMID: 39199371 PMCID: PMC11352882 DOI: 10.3390/biom14080983] [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: 07/09/2024] [Revised: 08/04/2024] [Accepted: 08/08/2024] [Indexed: 09/01/2024] Open
Abstract
Accurate computational prediction of B-cell epitopes can greatly enhance biomedical research and rapidly advance efforts to develop therapeutics, monoclonal antibodies, vaccines, and immunodiagnostic reagents. Previous research efforts have primarily focused on the development of computational methods to predict linear epitopes rather than conformational epitopes; however, the latter is much more biologically predominant. Several conformational B-cell epitope prediction methods have recently been published, but their predictive performances are weak. Here, we present a review of the latest computational methods and assess their performances on a diverse test set of 29 non-redundant unbound antigen structures. Our results demonstrate that ISPIPab performs better than most methods and compares favorably with other recent antigen-specific methods. Finally, we suggest new strategies and opportunities to improve computational predictions of conformational B-cell epitopes.
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Affiliation(s)
| | | | - R. Viswanathan
- Department of Chemistry and Biochemistry, Yeshiva College, Yeshiva University, New York, NY 10033, USA; (M.C.); (E.R.)
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4
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Sun Y, Li H, Wang H, Wang R, Kong Y, Chu L, Zhang X, Li H, Lv Y, Qiu J, Guo H. Development and characterization of a novel specific monoclonal antibody against mink enteritis virus and its antigen epitope analysis. Microb Pathog 2024; 192:106709. [PMID: 38810766 DOI: 10.1016/j.micpath.2024.106709] [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: 02/06/2024] [Revised: 05/13/2024] [Accepted: 05/21/2024] [Indexed: 05/31/2024]
Abstract
This study prepared a novel monoclonal antibody (MAb) against mink enteritis parvovirus (MEV) and identified its antigen epitope. The antibody subclass is identified as IgG1, the titers of the MAb is up to 1:1 × 106 and keeps stably after low-temperature storage for 9 months or 11 passages of the MAb cells. The MAb can specifically recognize MEV in the cells in IFA, but not Aleutian disease virus (ADV) or canine distemper virus (CDV). Its antigen epitope was identified as a polypeptide containing 5 key amino acids (378YAFGR382) and the homology in 20 MEV strains, 4 canine parvovirus strains, and 4 feline panleukopenia virus strains was 100%. This study supplies a biological material for developing new methods to detect MEV.
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Affiliation(s)
- Yuxin Sun
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China
| | - Hongmei Li
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China
| | - Haoran Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China
| | - Ruonan Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China
| | - Yijia Kong
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China
| | - Lina Chu
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China
| | - Xiying Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China
| | - Hongxin Li
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China
| | - Yunwei Lv
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China
| | - Jianhua Qiu
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China.
| | - Huijun Guo
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China.
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5
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Poulton JS, Lamba S, Free M, Xi G, McInnis E, Williams G, Kudlacek ST, Thieker D, Kuhlman B, Falk R. High-resolution epitope mapping of commercial antibodies to ANCA antigens by yeast surface display. J Immunol Methods 2024; 528:113654. [PMID: 38432292 PMCID: PMC11023775 DOI: 10.1016/j.jim.2024.113654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024]
Abstract
Epitope mapping provides critical insight into antibody-antigen interactions. Epitope mapping of autoantibodies from patients with autoimmune diseases can help elucidate disease immunogenesis and guide the development of antigen-specific therapies. Similarly, epitope mapping of commercial antibodies targeting known autoantigens enables the use of those antibodies to test specific hypotheses. Anti-Neutrophil Cytoplasmic Autoantibody (ANCA) vasculitis results from the formation of autoantibodies to multiple autoantigens, including myeloperoxidase (MPO), proteinase-3 (PR3), plasminogen (PLG), and peroxidasin (PXDN). To perform high-resolution epitope mapping of commercial antibodies to these autoantigens, we developed a novel yeast surface display library based on a series of >5000 overlapping peptides derived from their protein sequences. Using both FACS and magnetic bead isolation of reactive yeast, we screened 19 commercially available antibodies to the ANCA autoantigens. This approach to epitope mapping resulted in highly specific, fine epitope mapping, down to single amino acid resolution in many cases. Our study also identified cross-reactivity between some commercial antibodies to MPO and PXDN, which suggests that patients with apparent autoantibodies to both proteins may be the result of cross-reactivity. Together, our data validate yeast surface display using maximally overlapping peptides as an excellent approach to linear epitope mapping.
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Affiliation(s)
- John S Poulton
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; UNC Kidney Center, Chapel Hill, North Carolina, USA.
| | - Sajan Lamba
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Meghan Free
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; UNC Kidney Center, Chapel Hill, North Carolina, USA
| | - Gang Xi
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; UNC Kidney Center, Chapel Hill, North Carolina, USA
| | - Elizabeth McInnis
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Gabrielle Williams
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Stephan T Kudlacek
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; Menten AI, San Francisco, California, USA
| | - David Thieker
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Ronald Falk
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; UNC Kidney Center, Chapel Hill, North Carolina, USA
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6
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Bai G, Sun C, Guo Z, Wang Y, Zeng X, Su Y, Zhao Q, Ma B. Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects. Semin Cancer Biol 2023; 95:13-24. [PMID: 37355214 DOI: 10.1016/j.semcancer.2023.06.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/09/2023] [Accepted: 06/18/2023] [Indexed: 06/26/2023]
Abstract
Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.
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Affiliation(s)
- Ganggang Bai
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Chuance Sun
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ziang Guo
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China
| | - Yangjing Wang
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xincheng Zeng
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuhong Su
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China; MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa, Macao Special Administrative Region of China.
| | - Buyong Ma
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Digiwiser BioTechnolgy, Limited, Shanghai 201203, China.
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7
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Azulay A, Cohen-Lavi L, Friedman LM, McGargill MA, Hertz T. Mapping antibody footprints using binding profiles. CELL REPORTS METHODS 2023; 3:100566. [PMID: 37671022 PMCID: PMC10475849 DOI: 10.1016/j.crmeth.2023.100566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 07/26/2023] [Accepted: 07/31/2023] [Indexed: 09/07/2023]
Abstract
The increasing use of monoclonal antibodies (mAbs) in biology and medicine necessitates efficient methods for characterizing their binding epitopes. Here, we developed a high-throughput antibody footprinting method based on binding profiles. We used an antigen microarray to profile 23 human anti-influenza hemagglutinin (HA) mAbs using HA proteins of 43 human influenza strains isolated between 1918 and 2018. We showed that the mAb's binding profile can be used to characterize its influenza subtype specificity, binding region, and binding site. We present mAb-Patch-an epitope prediction method that is based on a mAb's binding profile and the 3D structure of its antigen. mAb-Patch was evaluated using four mAbs with known solved mAb-HA structures. mAb-Patch identifies over 67% of the true epitope when considering only 50-60 positions along the antigen. Our work provides proof of concept for utilizing antibody binding profiles to screen large panels of mAbs and to down-select antibodies for further functional studies.
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Affiliation(s)
- Asaf Azulay
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- National Institute of Biotechnology in the Negev, Beer-Sheva, Israel
| | - Liel Cohen-Lavi
- National Institute of Biotechnology in the Negev, Beer-Sheva, Israel
- Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Lilach M. Friedman
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- National Institute of Biotechnology in the Negev, Beer-Sheva, Israel
| | - Maureen A. McGargill
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Tomer Hertz
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- National Institute of Biotechnology in the Negev, Beer-Sheva, Israel
- Vaccine and Infectious Disease Division, Fred Hutch Cancer Research Center, Seattle, WA, USA
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8
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Zeng X, Bai G, Sun C, Ma B. Recent Progress in Antibody Epitope Prediction. Antibodies (Basel) 2023; 12:52. [PMID: 37606436 PMCID: PMC10443277 DOI: 10.3390/antib12030052] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/23/2023] Open
Abstract
Recent progress in epitope prediction has shown promising results in the development of vaccines and therapeutics against various diseases. However, the overall accuracy and success rate need to be improved greatly to gain practical application significance, especially conformational epitope prediction. In this review, we examined the general features of antibody-antigen recognition, highlighting the conformation selection mechanism in flexible antibody-antigen binding. We recently highlighted the success and warning signs of antibody epitope predictions, including linear and conformation epitope predictions. While deep learning-based models gradually outperform traditional feature-based machine learning, sequence and structure features still provide insight into antibody-antigen recognition problems.
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Affiliation(s)
- Xincheng Zeng
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; (X.Z.); (C.S.)
| | - Ganggang Bai
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; (X.Z.); (C.S.)
| | - Chuance Sun
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; (X.Z.); (C.S.)
| | - Buyong Ma
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; (X.Z.); (C.S.)
- Shanghai Digiwiser Biological, Inc., Shanghai 200131, China
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9
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Jimenez-Vasquez V, Calvay-Sanchez KD, Zarate-Sulca Y, Mendoza-Mujica G. In-silico identification of linear B-cell epitopes in specific proteins of Bartonella bacilliformis for the serological diagnosis of Carrion's disease. PLoS Negl Trop Dis 2023; 17:e0011321. [PMID: 37228134 DOI: 10.1371/journal.pntd.0011321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 04/20/2023] [Indexed: 05/27/2023] Open
Abstract
Carrion´s disease is caused by Bartonella bacilliformis, it is a Gram-negative pleomorphic bacterium. B. bacilliformis is transmitted by Lutzomyia verrucarum in endemic areas of the Peruvian Inter-Andean valleys. Additionally, the pathogenicity of B. bacilliformis involves an initial infection of erythrocytes and the further infection of endothelial cells, which mainly affects children and expectant women from extreme poverty rural areas. Therefore, the implementation of serological diagnostic methods and the development of candidate vaccines for the control of CD could be facilitated by the prediction of linear b-cell epitopes in specific proteins of B. bacilliformis by bioinformatics analysis. In this study, We used an in-silico analysis employing six web servers for the identification of epitopes in proteins of B. bacilliformis. The selection of B. bacilliformis-specific proteins and their analysis to identify epitopes allowed the selection of seven protein candidates that are expected to have high antigenic activity.
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Affiliation(s)
| | | | - Yanina Zarate-Sulca
- Laboratory of Vector-Borne Bacterial Diseases. National Institute of Health, Lima, Peru
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10
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Qiu T, Zhang L, Chen Z, Wang Y, Mao T, Wang C, Cun Y, Zheng G, Yan D, Zhou M, Tang K, Cao Z. SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs. Nucleic Acids Res 2023:7175357. [PMID: 37216611 DOI: 10.1093/nar/gkad427] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/07/2023] [Accepted: 05/10/2023] [Indexed: 05/24/2023] Open
Abstract
Identifying the exact epitope positions for a monoclonal antibody (mAb) is of critical importance yet highly challenging to the Ab design of biomedical research. Based on previous versions of SEPPA 3.0, we present SEPPA-mAb for the above purpose with high accuracy and low false positive rate (FPR), suitable for both experimental and modelled structures. In practice, SEPPA-mAb appended a fingerprints-based patch model to SEPPA 3.0, considering the structural and physic-chemical complementarity between a possible epitope patch and the complementarity-determining region of mAb and trained on 860 representative antigen-antibody complexes. On independent testing of 193 antigen-antibody pairs, SEPPA-mAb achieved an accuracy of 0.873 with an FPR of 0.097 in classifying epitope and non-epitope residues under the default threshold, while docking-based methods gave the best AUC of 0.691, and the top epitope prediction tool gave AUC of 0.730 with balanced accuracy of 0.635. A study on 36 independent HIV glycoproteins displayed a high accuracy of 0.918 and a low FPR of 0.058. Further testing illustrated outstanding robustness on new antigens and modelled antibodies. Being the first online tool predicting mAb-specific epitopes, SEPPA-mAb may help to discover new epitopes and design better mAbs for therapeutic and diagnostic purposes. SEPPA-mAb can be accessed at http://www.badd-cao.net/seppa-mab/.
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Affiliation(s)
- Tianyi Qiu
- School of Life Sciences, Fudan University, Shanghai 200092, China
- Institute of Clinical Science, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Lu Zhang
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Zikun Chen
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Yuan Wang
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Tiantian Mao
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Caicui Wang
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Yewei Cun
- School of Life Sciences, Fudan University, Shanghai 200092, China
| | - Genhui Zheng
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Deyu Yan
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Mengdi Zhou
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Kailin Tang
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Zhiwei Cao
- School of Life Sciences, Fudan University, Shanghai 200092, China
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
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11
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Chen Z, Wang X, Chen X, Huang J, Wang C, Wang J, Wang Z. Accelerating therapeutic protein design with computational approaches toward the clinical stage. Comput Struct Biotechnol J 2023; 21:2909-2926. [PMID: 38213894 PMCID: PMC10781723 DOI: 10.1016/j.csbj.2023.04.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/11/2023] [Accepted: 04/27/2023] [Indexed: 01/13/2024] Open
Abstract
Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.
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Affiliation(s)
- Zhidong Chen
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xinpei Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xu Chen
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Juyang Huang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Chenglin Wang
- Shenzhen Qiyu Biotechnology Co., Ltd, Shenzhen 518107, China
| | - Junqing Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Zhe Wang
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
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12
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Rhee JH, Khim K, Puth S, Choi Y, Lee SE. Deimmunization of flagellin adjuvant for clinical application. Curr Opin Virol 2023; 60:101330. [PMID: 37084463 DOI: 10.1016/j.coviro.2023.101330] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/23/2023] [Indexed: 04/23/2023]
Abstract
Flagellin is the cognate ligand for host pattern recognition receptors, toll-like receptor 5 (TLR5) in the cell surface, and NAIP5/NLRC4 inflammasome in the cytosol. TLR5-binding domain is located in D1 domain, where crucial amino acid sequences are conserved among diverse bacteria. The highly conserved C-terminal 35 amino acids of flagellin were proved to be responsible for the inflammasome activation by binding to NAIP5. D2/D3 domains, located in the central region and exposed to the outside surface of flagellar filament, are heterogeneous across bacterial species and highly immunogenic. Taking advantage of TLR5- and NLRC4-stimulating activities, flagellin has been actively developed as a vaccine adjuvant and immunotherapeutic. Because of its immunogenicity, there exist worries concerning diminished efficacy and possible reactogenicity after repeated administration. Deimmunization of flagellin derivatives while preserving the TLR5/NLRC4-mediated immunomodulatory activity should be the most reasonable option for clinical application. This review describes strategies and current achievements in flagellin deimmunization.
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Affiliation(s)
- Joon Haeng Rhee
- Clinical Vaccine R&D Center, Chonnam National University, Hwasun-gun, Jeonnam, Republic of Korea; Combinatorial Tumor Immunotherapy MRC, Chonnam National University Medical School, Hwasun-gun, Jeonnam, Republic of Korea; Department of Microbiology, Chonnam National University Medical School, Hwasun-gun, Jeonnam, Republic of Korea.
| | - Koemchhoy Khim
- Clinical Vaccine R&D Center, Chonnam National University, Hwasun-gun, Jeonnam, Republic of Korea; Combinatorial Tumor Immunotherapy MRC, Chonnam National University Medical School, Hwasun-gun, Jeonnam, Republic of Korea
| | - Sao Puth
- Clinical Vaccine R&D Center, Chonnam National University, Hwasun-gun, Jeonnam, Republic of Korea; Combinatorial Tumor Immunotherapy MRC, Chonnam National University Medical School, Hwasun-gun, Jeonnam, Republic of Korea
| | - Yoonjoo Choi
- Combinatorial Tumor Immunotherapy MRC, Chonnam National University Medical School, Hwasun-gun, Jeonnam, Republic of Korea; Department of Microbiology, Chonnam National University Medical School, Hwasun-gun, Jeonnam, Republic of Korea
| | - Shee Eun Lee
- Clinical Vaccine R&D Center, Chonnam National University, Hwasun-gun, Jeonnam, Republic of Korea; Immunotherapy Innovation Center, Hwasun-gun, Jeonnam, Republic of Korea
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13
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Cong X, Zhang L, Zhu H, Wu M, Zhu Y, Lian Y, Huang B, Gu Y, Cong F. Preparation of a new monoclonal antibody against nucleocapsid protein of swine acute diarrhea syndrome coronavirus and identification of its linear antigenic epitope. Int J Biol Macromol 2023; 239:124241. [PMID: 36996959 DOI: 10.1016/j.ijbiomac.2023.124241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 03/24/2023] [Accepted: 03/26/2023] [Indexed: 03/31/2023]
Abstract
Swine acute diarrhea syndrome coronavirus (SADS-CoV), which causes severe diarrhea in newborn piglets, was first identified in Southern China in 2017. Since the Nucleocapsid (N) protein in SADS-CoV is highly conserved and plays a key role in virus replication, it is often used as a target protein in scientific research. In this study, the N protein of SADS-CoV was successfully expressed, and a new monoclonal antibody (mAb), 5G12, against the protein was generated successfully. The mAb 5G12 can be used to detect SADS-CoV strains by indirect immunofluorescence assay (IFA) and western blotting. The mAb 5G12 epitope was located to amino acids 11 EQAESRGRK 19 by evaluating the antibody for reactivity with a series of truncated N protein segments. The biological information analysis showed that the antigenic epitope had a high antigenic index and conservation. This study will help further understand the protein structure and function of SADS-CoV and in the establishment of specific SADS-CoV detection methods.
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14
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Khan A, Cowen-Rivers AI, Grosnit A, Deik DGX, Robert PA, Greiff V, Smorodina E, Rawat P, Akbar R, Dreczkowski K, Tutunov R, Bou-Ammar D, Wang J, Storkey A, Bou-Ammar H. Toward real-world automated antibody design with combinatorial Bayesian optimization. CELL REPORTS METHODS 2023; 3:100374. [PMID: 36814835 PMCID: PMC9939385 DOI: 10.1016/j.crmeth.2022.100374] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 10/08/2022] [Accepted: 12/07/2022] [Indexed: 06/14/2023]
Abstract
Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.
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Affiliation(s)
- Asif Khan
- School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK
| | | | | | | | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Eva Smorodina
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | | | | | - Dany Bou-Ammar
- American University of Beirut Medical Centre, Beirut 11-0236, Lebanon
| | - Jun Wang
- Huawei Noah’s Ark Lab, London N1C 4AG, UK
- University College London, London WC1E 6BT, UK
| | - Amos Storkey
- School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK
| | - Haitham Bou-Ammar
- Huawei Noah’s Ark Lab, London N1C 4AG, UK
- University College London, London WC1E 6BT, UK
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15
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Cia G, Pucci F, Rooman M. Critical review of conformational B-cell epitope prediction methods. Brief Bioinform 2023; 24:6972295. [PMID: 36611255 DOI: 10.1093/bib/bbac567] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/17/2022] [Accepted: 11/19/2022] [Indexed: 01/09/2023] Open
Abstract
Accurate in silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.
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Affiliation(s)
- Gabriel Cia
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, F. Roosevelt Avenue, 1050, Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Triumph Boulevard, 1050, Brussels, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, F. Roosevelt Avenue, 1050, Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Triumph Boulevard, 1050, Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, F. Roosevelt Avenue, 1050, Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Triumph Boulevard, 1050, Brussels, Belgium
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16
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Robert PA, Akbar R, Frank R, Pavlović M, Widrich M, Snapkov I, Slabodkin A, Chernigovskaya M, Scheffer L, Smorodina E, Rawat P, Mehta BB, Vu MH, Mathisen IF, Prósz A, Abram K, Olar A, Miho E, Haug DTT, Lund-Johansen F, Hochreiter S, Haff IH, Klambauer G, Sandve GK, Greiff V. Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction. NATURE COMPUTATIONAL SCIENCE 2022; 2:845-865. [PMID: 38177393 DOI: 10.1038/s43588-022-00372-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/09/2022] [Indexed: 01/06/2024]
Abstract
Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.
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Affiliation(s)
- Philippe A Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
| | - Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Robert Frank
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | - Michael Widrich
- ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | - Igor Snapkov
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Andrei Slabodkin
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Maria Chernigovskaya
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | - Eva Smorodina
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Mai Ha Vu
- Department of Linguistics and Scandinavian Studies, University of Oslo, Oslo, Norway
| | | | - Aurél Prósz
- Danish Cancer Society Research Center, Translational Cancer Genomics, Copenhagen, Denmark
| | - Krzysztof Abram
- The Novo Nordisk Foundation Center for Biosustainability, Autoflow, DTU Biosustain and IT University of Copenhagen, Copenhagen, Denmark
| | - Alex Olar
- Department of Complex Systems in Physics, Eötvös Loránd University, Budapest, Hungary
| | - Enkelejda Miho
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
- aiNET GmbH, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | | | - Sepp Hochreiter
- ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
- Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria
| | | | - Günter Klambauer
- ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | | | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
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17
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Waury K, Willemse EAJ, Vanmechelen E, Zetterberg H, Teunissen CE, Abeln S. Bioinformatics tools and data resources for assay development of fluid protein biomarkers. Biomark Res 2022; 10:83. [DOI: 10.1186/s40364-022-00425-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 10/25/2022] [Indexed: 11/16/2022] Open
Abstract
AbstractFluid protein biomarkers are important tools in clinical research and health care to support diagnosis and to monitor patients. Especially within the field of dementia, novel biomarkers could address the current challenges of providing an early diagnosis and of selecting trial participants. While the great potential of fluid biomarkers is recognized, their implementation in routine clinical use has been slow. One major obstacle is the often unsuccessful translation of biomarker candidates from explorative high-throughput techniques to sensitive antibody-based immunoassays. In this review, we propose the incorporation of bioinformatics into the workflow of novel immunoassay development to overcome this bottleneck and thus facilitate the development of novel biomarkers towards clinical laboratory practice. Due to the rapid progress within the field of bioinformatics many freely available and easy-to-use tools and data resources exist which can aid the researcher at various stages. Current prediction methods and databases can support the selection of suitable biomarker candidates, as well as the choice of appropriate commercial affinity reagents. Additionally, we examine methods that can determine or predict the epitope - an antibody’s binding region on its antigen - and can help to make an informed choice on the immunogenic peptide used for novel antibody production. Selected use cases for biomarker candidates help illustrate the application and interpretation of the introduced tools.
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18
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Xu Z, Ismanto HS, Zhou H, Saputri DS, Sugihara F, Standley DM. Advances in antibody discovery from human BCR repertoires. FRONTIERS IN BIOINFORMATICS 2022; 2:1044975. [PMID: 36338807 PMCID: PMC9631452 DOI: 10.3389/fbinf.2022.1044975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022] Open
Abstract
Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans.
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Affiliation(s)
- Zichang Xu
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Hendra S. Ismanto
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Hao Zhou
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Dianita S. Saputri
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Fuminori Sugihara
- Core Instrumentation Facility, Immunology Frontier Research Center, Osaka University, Suita, Japan
| | - Daron M. Standley
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Department Systems Immunology, Immunology Frontier Research Center, Osaka University, Suita, Japan
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19
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Zhou J, Chen J, Peng Y, Xie Y, Xiao Y. A Promising Tool in Serological Diagnosis: Current Research Progress of Antigenic Epitopes in Infectious Diseases. Pathogens 2022; 11:1095. [PMID: 36297152 PMCID: PMC9609281 DOI: 10.3390/pathogens11101095] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 09/20/2022] [Accepted: 09/23/2022] [Indexed: 07/30/2023] Open
Abstract
Infectious diseases, caused by various pathogens in the clinic, threaten the safety of human life, are harmful to physical and mental health, and also increase economic burdens on society. Infections are a complex mechanism of interaction between pathogenic microorganisms and their host. Identification of the causative agent of the infection is vital for the diagnosis and treatment of diseases. Etiological laboratory diagnostic tests are therefore essential to identify pathogens. However, due to its rapidity and automation, the serological diagnostic test is among the methods of great significance for the diagnosis of infections with the basis of detecting antigens or antibodies in body fluids clinically. Epitopes, as a special chemical group that determines the specificity of antigens and the basic unit of inducing immune responses, play an important role in the study of immune responses. Identifying the epitopes of a pathogen may contribute to the development of a vaccine to prevent disease, the diagnosis of the corresponding disease, and the determination of different stages of the disease. Moreover, both the preparation of neutralizing antibodies based on useful epitopes and the assembly of several associated epitopes can be used in the treatment of disease. Epitopes can be divided into B cell epitopes and T cell epitopes; B cell epitopes stimulate the body to produce antibodies and are therefore commonly used as targets for the design of serological diagnostic experiments. Meanwhile, epitopes can fall into two possible categories: linear and conformational. This article reviews the role of B cell epitopes in the clinical diagnosis of infectious diseases.
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20
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Zheng W, Saliba JG, Wei X, Shu Q, Pierson LM, Mao L, Liu C, Lyon CJ, Li CZ, Wimley WC, Hu TY. Nanopore-based disease diagnosis using pathogen-derived tryptic peptides from serum. NANO TODAY 2022; 45:101515. [PMID: 37034182 PMCID: PMC10081497 DOI: 10.1016/j.nantod.2022.101515] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Nanopore sensors have shown great utility in nucleic acid detection and sequencing approaches. Recent studies also indicate that current signatures produced by peptide-nanopore interactions can distinguish high purity peptide mixtures, but the utility of nanopore sensors in clinical applications still needs to be explored due to the inherent complexity of clinical specimens. To fill this gap between research and clinical nanopore applications, we describe a methodology to select peptide biomarkers suitable for use in an immunoprecipitation-coupled nanopore (IP-NP) assay, based on their pathogen specificity, antigenicity, charge, water solubility and ability to produce a characteristic nanopore interaction signature. Using tuberculosis as a proof-of-principle example in a disease that can be challenging to diagnose, we demonstrate that a peptide identified by this approach produced high-affinity antibodies and yielded a characteristic peptide signature that was detectable over a broad linear range, to detect and quantify a pathogen-derived peptide from digested human serum samples with high sensitivity and specificity. This nanopore signal distinguished serum from a TB case, non-disease controls, and from a TB-case after extended anti-TB treatment. We believe this assay approach should be readily adaptable to other infectious and chronic diseases that can be diagnosed by peptide biomarkers.
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Affiliation(s)
- Wenshu Zheng
- Center for Cellular and Molecular Diagnostics, Tulane University School of Medicine, New Orleans, LA, USA
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Julian G. Saliba
- Center for Cellular and Molecular Diagnostics, Tulane University School of Medicine, New Orleans, LA, USA
- Department of Biomedical Engineering, Tulane University School of Science & Engineering, New Orleans, LA, USA
| | - Xiaojun Wei
- Biomedical Engineering Program, University of South Carolina, Columbia, SC 29208, USA
- Department of Chemical Engineering, University of South Carolina, Columbia, SC 29208, USA
| | - Qingbo Shu
- Center for Cellular and Molecular Diagnostics, Tulane University School of Medicine, New Orleans, LA, USA
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Lane M. Pierson
- Center for Cellular and Molecular Diagnostics, Tulane University School of Medicine, New Orleans, LA, USA
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Liyan Mao
- Center for Cellular and Molecular Diagnostics, Tulane University School of Medicine, New Orleans, LA, USA
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Chang Liu
- Biomedical Engineering Program, University of South Carolina, Columbia, SC 29208, USA
- Department of Chemical Engineering, University of South Carolina, Columbia, SC 29208, USA
| | - Christopher J. Lyon
- Center for Cellular and Molecular Diagnostics, Tulane University School of Medicine, New Orleans, LA, USA
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Chen-Zhong Li
- Center for Cellular and Molecular Diagnostics, Tulane University School of Medicine, New Orleans, LA, USA
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, LA, USA
| | - William C. Wimley
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Tony Ye Hu
- Center for Cellular and Molecular Diagnostics, Tulane University School of Medicine, New Orleans, LA, USA
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, LA, USA
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21
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Lee JH, Yin R, Ofek G, Pierce BG. Structural Features of Antibody-Peptide Recognition. Front Immunol 2022; 13:910367. [PMID: 35874680 PMCID: PMC9302003 DOI: 10.3389/fimmu.2022.910367] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 06/08/2022] [Indexed: 11/22/2022] Open
Abstract
Antibody recognition of antigens is a critical element of adaptive immunity. One key class of antibody-antigen complexes is comprised of antibodies targeting linear epitopes of proteins, which in some cases are conserved elements of viruses and pathogens of relevance for vaccine design and immunotherapy. Here we report a detailed analysis of the structural and interface features of this class of complexes, based on a set of nearly 200 nonredundant high resolution antibody-peptide complex structures that were assembled from the Protein Data Bank. We found that antibody-bound peptides adopt a broad range of conformations, often displaying limited secondary structure, and that the same peptide sequence bound by different antibodies can in many cases exhibit varying conformations. Propensities of contacts with antibody loops and extent of antibody binding conformational changes were found to be broadly similar to those for antibodies in complex with larger protein antigens. However, antibody-peptide interfaces showed lower buried surface areas and fewer hydrogen bonds than antibody-protein antigen complexes, while calculated binding energy per buried interface area was found to be higher on average for antibody-peptide interfaces, likely due in part to a greater proportion of buried hydrophobic residues and higher shape complementarity. This dataset and these observations can be of use for future studies focused on this class of interactions, including predictive computational modeling efforts and the design of antibodies or epitope-based vaccine immunogens.
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Affiliation(s)
- Jessica H. Lee
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, United States
| | - Rui Yin
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, United States
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, United States
| | - Gilad Ofek
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, United States
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, United States
| | - Brian G. Pierce
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, United States
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, United States
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, United States
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22
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Davila A, Xu Z, Li S, Rozewicki J, Wilamowski J, Kotelnikov S, Kozakov D, Teraguchi S, Standley DM. AbAdapt: an adaptive approach to predicting antibody-antigen complex structures from sequence. BIOINFORMATICS ADVANCES 2022; 2:vbac015. [PMID: 36699363 PMCID: PMC9710585 DOI: 10.1093/bioadv/vbac015] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 02/15/2022] [Accepted: 03/03/2022] [Indexed: 01/28/2023]
Abstract
Motivation The scoring of antibody-antigen docked poses starting from unbound homology models has not been systematically optimized for a large and diverse set of input sequences. Results To address this need, we have developed AbAdapt, a webserver that accepts antibody and antigen sequences, models their 3D structures, predicts epitope and paratope, and then docks the modeled structures using two established docking engines (Piper and Hex). Each of the key steps has been optimized by developing and training new machine-learning models. The sequences from a diverse set of 622 antibody-antigen pairs with known structure were used as inputs for leave-one-out cross-validation. The final set of cluster representatives included at least one 'Adequate' pose for 550/622 (88.4%) of the queries. The median (interquartile range) ranks of these 'Adequate' poses were 22 (5-77). Similar results were obtained on a holdout set of 100 unrelated antibody-antigen pairs. When epitopes were repredicted using docking-derived features for specific antibodies, the median ROC AUC increased from 0.679 to 0.720 in cross-validation and from 0.694 to 0.730 in the holdout set. Availability and implementation AbAdapt and related data are available at https://sysimm.org/abadapt/. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Ana Davila
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan
| | - Zichang Xu
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan
| | - Songling Li
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan
| | - John Rozewicki
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan
| | - Jan Wilamowski
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-5252, USA,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-5252, USA,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Shunsuke Teraguchi
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan,Faculty of Data Science, Shiga University, Hikone 522-8522, Japan
| | - Daron M Standley
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan,Immunology Frontier Research Center, Department of Systems Immunology, Osaka University, Suita 565-0871, Japan,To whom correspondence should be addressed.
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23
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Bukhari SNH, Jain A, Haq E, Mehbodniya A, Webber J. Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review. Pathogens 2022; 11:146. [PMID: 35215090 PMCID: PMC8879824 DOI: 10.3390/pathogens11020146] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 02/01/2023] Open
Abstract
The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety-they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.
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Affiliation(s)
- Syed Nisar Hussain Bukhari
- University Institute of Computing, Chandigarh University, NH-95, Chandigarh-Ludhiana Highway, Mohali 140413, India;
| | - Amit Jain
- University Institute of Computing, Chandigarh University, NH-95, Chandigarh-Ludhiana Highway, Mohali 140413, India;
| | - Ehtishamul Haq
- Department of Biotechnology, University of Kashmir, Srinagar 190006, India;
| | - Abolfazl Mehbodniya
- Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Kuwait City 20185145, Kuwait;
| | - Julian Webber
- Graduate School of Engineering Science, Osaka University, Osaka 560-8531, Japan;
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Akbar R, Bashour H, Rawat P, Robert PA, Smorodina E, Cotet TS, Flem-Karlsen K, Frank R, Mehta BB, Vu MH, Zengin T, Gutierrez-Marcos J, Lund-Johansen F, Andersen JT, Greiff V. Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. MAbs 2022; 14:2008790. [PMID: 35293269 PMCID: PMC8928824 DOI: 10.1080/19420862.2021.2008790] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 11/04/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022] Open
Abstract
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
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Affiliation(s)
- Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Habib Bashour
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eva Smorodina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russia
| | | | - Karine Flem-Karlsen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Robert Frank
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Mai Ha Vu
- Department of Linguistics and Scandinavian Studies, University of Oslo, Norway
| | - Talip Zengin
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Bioinformatics, Mugla Sitki Kocman University, Turkey
| | | | | | - Jan Terje Andersen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
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Vakilian M. A review on the effect of prolyl isomerization on immune response aberration and hypersensitivity reactions: A unifying hypothesis. Clin Immunol 2021; 234:108896. [PMID: 34848356 DOI: 10.1016/j.clim.2021.108896] [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: 08/27/2021] [Revised: 11/19/2021] [Accepted: 11/21/2021] [Indexed: 12/01/2022]
Abstract
Little is known about the causes and mechanisms of ectopic immune responses, including different types of hypersensitivity, superantigens, and cytokine storms. Two of the most questionable phenomena observed in immunology are why the intensity and extent of immune responses to different antigens are different, and why some self-antigens are attacked as foreign. The secondary structure of the peptides involved in the immune system, such as the epitope-paratope interfaces plays a pivotal role in the resulting immune responses. Prolyl cis/trans isomerization plays a fundamental role in the form of the secondary structure and the folding of proteins. This review covers some of the emerging evidence indicating the impact of prolyl isomerization on protein conformation, aberration of immune responses, and the development of hypersensitivity reactions.
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Affiliation(s)
- Mehrdad Vakilian
- Department of Cell Biology, Genetics and Physiology, University of Malaga (UMA), The Institute of Biomedical Research in Malaga (IBIMA), Málaga, Spain.
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26
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Kadam K, Peerzada N, Karbhal R, Sawant S, Valadi J, Kulkarni-Kale U. Antibody Class(es) Predictor for Epitopes (AbCPE): A Multi-Label Classification Algorithm. FRONTIERS IN BIOINFORMATICS 2021; 1:709951. [PMID: 36303781 PMCID: PMC9581038 DOI: 10.3389/fbinf.2021.709951] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/23/2021] [Indexed: 01/14/2023] Open
Abstract
Development of vaccines and therapeutic antibodies to deal with infectious and other diseases are the most perceptible scientific interventions that have had huge impact on public health including that in the current Covid-19 pandemic. From inactivation methodologies to reverse vaccinology, vaccine development strategies of 21st century have undergone several transformations and are moving towards rational design approaches. These developments are driven by data as the combinatorials involved in antigenic diversity of pathogens and immune repertoire of hosts are enormous. The computational prediction of epitopes is central to these developments and numerous B-cell epitope prediction methods developed over the years in the field of immunoinformatics have contributed enormously. Most of these methods predict epitopes that could potentially bind to an antibody regardless of its type and only a few account for antibody class specific epitope prediction. Recent studies have provided evidence of more than one class of antibodies being associated with a particular disease. Therefore, it is desirable to predict and prioritize ‘peptidome’ representing B-cell epitopes that can potentially bind to multiple classes of antibodies, as an open problem in immunoinformatics. To address this, AbCPE, a novel algorithm based on multi-label classification approach has been developed for prediction of antibody class(es) to which an epitope can potentially bind. The epitopes binding to one or more antibody classes (IgG, IgE, IgA and IgM) have been used as a knowledgebase to derive features for prediction. Multi-label algorithms, Binary Relevance and Label Powerset were applied along with Random Forest and AdaBoost. Classifier performance was assessed using evaluation measures like Hamming Loss, Precision, Recall and F1 score. The Binary Relevance model based on dipeptide composition, Random Forest and AdaBoost achieved the best results with Hamming Loss of 0.1121 and 0.1074 on training and test sets respectively. The results obtained by AbCPE are promising. To the best of our knowledge, this is the first multi-label method developed for prediction of antibody class(es) for sequential B-cell epitopes and is expected to bring a paradigm shift in the field of immunoinformatics and immunotherapeutic developments in synthetic biology. The AbCPE web server is available at http://bioinfo.unipune.ac.in/AbCPE/Home.html.
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Affiliation(s)
- Kiran Kadam
- Bioinformatics Centre, Savitribai Phule Pune University, Pune, India
| | - Noor Peerzada
- Centre for Modeling and Simulation, Savitribai Phule Pune University, Pune, India
| | - Rajiv Karbhal
- Bioinformatics Centre, Savitribai Phule Pune University, Pune, India
| | - Sangeeta Sawant
- Bioinformatics Centre, Savitribai Phule Pune University, Pune, India
| | - Jayaraman Valadi
- Department of Computer Science, FLAME University, Pune, India
- *Correspondence: Jayaraman Valadi, ; Urmila Kulkarni-Kale, ,
| | - Urmila Kulkarni-Kale
- Bioinformatics Centre, Savitribai Phule Pune University, Pune, India
- *Correspondence: Jayaraman Valadi, ; Urmila Kulkarni-Kale, ,
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Chizari M, Fani-Kheshti S, Taeb J, Farajollahi MM, Mohsenzadegan M. The Anti-Proliferative Effect of a Newly-Produced Anti-PSCA-Peptide Antibody by Multiple Bioinformatics Tools, on Prostate Cancer Cells. Recent Pat Anticancer Drug Discov 2021; 16:73-83. [PMID: 33176663 DOI: 10.2174/1574892815999201110212411] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 10/08/2020] [Accepted: 10/12/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Prostate Stem Cell Antigen (PSCA) is a small cell surface protein, overexpressed in 90% of prostate cancers. Determination of epitopes that elicit an appropriate response to the antibody generation is vital for diagnostic and immunotherapeutic purposes for prostate cancer treatment. Presently, bioinformatics B-cell prediction tools can predict the location of epitopes, which is uncomplicated, faster, and more cost-effective than experimental methods. OBJECTIVE We aimed to predict a novel linear peptide for Prostate Stem Cell Antigen (PSCA) protein in order to generate anti-PSCA-peptide (p) antibody and to investigate its effect on prostate cancer cells. METHODS In the current study, a novel linear peptide for PSCA was predicted using in silico methods that utilize a set of linear B-cell epitope prediction tools. Polyclonal antibody (anti-PSCA-p antibody "Patent No. 99318") against PSCA peptide was generated. The antibody reactivity was determined by the Enzyme-Linked Immunosorbent Assay (ELISA) and its specificity by immunocytochemistry (ICC), immunohistochemistry (IHC), and Western Blotting (WB) assays. The effect of the anti-PSCA-p antibody on PSCA-expressing prostate cancer cell line was assessed by Methylthiazolyldiphenyl- Tetrazolium bromide (MTT) assay. RESULTS New peptide-fragment of PSCA sequence as "N-CVDDSQDYYVGKKN-C" (PSCA-p) was selected and synthesized. The anti-PSCA-p antibody against the PSCA-p showed immunoreactivity with PSCA-p specifically bound to PC-3 cells. Also, the anti-PSCA-p antibody strongly stained the prostate cancer tissues as compared to Benign Prostatic Hyperplasia (BPH) and normal tissues (P < 0.001). As the degree of malignancy increased, the staining intensity was also elevated in prostate cancer tissue (P < 0.001). Interestingly, the anti-PSCA-p antibody showed anti-proliferative effects on PC-3 cells (31%) with no growth inhibition effect on PSCA-negative cells. CONCLUSION In this study, we developed a new peptide sequence (PSCA-p) of PSCA. The PSCA-p targeting by anti-PSCA-p antibody inhibited the proliferation of prostate cancer cells, suggesting the potential of PSCA-p immunotherapy for future prostate cancer studies.
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Affiliation(s)
- Milad Chizari
- Department of Medical Biotechnology, School of Allied Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Sajad Fani-Kheshti
- Department of Medical Biotechnology, School of Allied Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Jaleh Taeb
- Department of Medical Biotechnology, School of Allied Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad M Farajollahi
- Department of Medical Biotechnology, School of Allied Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Monireh Mohsenzadegan
- Department of Medical Laboratory Sciences, School of Allied Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
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28
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Dai B, Bailey-Kellogg C. Protein Interaction Interface Region Prediction by Geometric Deep Learning. Bioinformatics 2021; 37:2580-2588. [PMID: 33693581 PMCID: PMC8428585 DOI: 10.1093/bioinformatics/btab154] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 01/10/2021] [Accepted: 03/02/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Protein-protein interactions drive wide-ranging molecular processes, and characterizing at the atomic level how proteins interact (beyond just the fact that they interact) can provide key insights into understanding and controlling this machinery. Unfortunately, experimental determination of three-dimensional protein complex structures remains difficult and does not scale to the increasingly large sets of proteins whose interactions are of interest. Computational methods are thus required to meet the demands of large-scale, high-throughput prediction of how proteins interact, but unfortunately both physical modeling and machine learning methods suffer from poor precision and/or recall. RESULTS In order to improve performance in predicting protein interaction interfaces, we leverage the best properties of both data- and physics-driven methods to develop a unified Geometric Deep Neural Network, "PInet" (Protein Interface Network). PInet consumes pairs of point clouds encoding the structures of two partner proteins, in order to predict their structural regions mediating interaction. To make such predictions, PInet learns and utilizes models capturing both geometrical and physicochemical molecular surface complementarity. In application to a set of benchmarks, PInet simultaneously predicts the interface regions on both interacting proteins, achieving performance equivalent to or even much better than the state-of-the-art predictor for each dataset. Furthermore, since PInet is based on joint segmentation of a representation of a protein surfaces, its predictions are meaningful in terms of the underlying physical complementarity driving molecular recognition. AVAILABILITY PInet scripts and models are available at https://github.com/FTD007/PInet. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bowen Dai
- Computer Science Department, Dartmouth, Hanover, 03755, United States
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29
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Zinsli LV, Stierlin N, Loessner MJ, Schmelcher M. Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion. Comput Struct Biotechnol J 2020; 19:315-329. [PMID: 33425259 PMCID: PMC7779837 DOI: 10.1016/j.csbj.2020.12.024] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 12/15/2020] [Accepted: 12/16/2020] [Indexed: 12/11/2022] Open
Abstract
Biotherapeutics, and antimicrobial proteins in particular, are of increasing interest for human medicine. An important challenge in the development of such therapeutics is their potential immunogenicity, which can induce production of anti-drug-antibodies, resulting in altered pharmacokinetics, reduced efficacy, and potentially severe anaphylactic or hypersensitivity reactions. For this reason, the development and application of effective deimmunization methods for protein drugs is of utmost importance. Deimmunization may be achieved by unspecific shielding approaches, which include PEGylation, fusion to polypeptides (e.g., XTEN or PAS), reductive methylation, glycosylation, and polysialylation. Alternatively, the identification of epitopes for T cells or B cells and their subsequent deletion through site-directed mutagenesis represent promising deimmunization strategies and can be accomplished through either experimental or computational approaches. This review highlights the most recent advances and current challenges in the deimmunization of protein therapeutics, with a special focus on computational epitope prediction and deletion tools.
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Key Words
- ABR, Antigen-binding region
- ADA, Anti-drug antibody
- ANN, Artificial neural network
- APC, Antigen-presenting cell
- Anti-drug-antibody
- B cell epitope
- BCR, B cell receptor
- Bab, Binding antibody
- CDR, Complementarity determining region
- CRISPR, Clustered regularly interspaced short palindromic repeats
- DC, Dendritic cell
- ELP, Elastin-like polypeptide
- EPO, Erythropoietin
- ER, Endoplasmatic reticulum
- GLK, Gelatin-like protein
- HAP, Homo-amino-acid polymer
- HLA, Human leukocyte antigen
- HMM, Hidden Markov model
- IL, Interleukin
- Ig, Immunoglobulin
- Immunogenicity
- LPS, Lipopolysaccharide
- MHC, Major histocompatibility complex
- NMR, Nuclear magnetic resonance
- Nab, Neutralizing antibody
- PAMP, Pathogen-associated molecular pattern
- PAS, Polypeptide composed of proline, alanine, and/or serine
- PBMC, Peripheral blood mononuclear cell
- PD, Pharmacodynamics
- PEG, Polyethylene glycol
- PK, Pharmacokinetics
- PRR, Pattern recognition receptor
- PSA, Sialic acid polymers
- Protein therapeutic
- RNN, Recurrent artificial neural network
- SVM, Support vector machine
- T cell epitope
- TAP, Transporter associated with antigen processing
- TCR, T cell receptor
- TLR, Toll-like receptor
- XTEN, “Xtended” recombinant polypeptide
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Affiliation(s)
- Léa V. Zinsli
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Noël Stierlin
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Martin J. Loessner
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Mathias Schmelcher
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
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Brooks BD, Closmore A, Yang J, Holland M, Cairns T, Cohen GH, Bailey-Kellogg C. Characterizing Epitope Binding Regions of Entire Antibody Panels by Combining Experimental and Computational Analysis of Antibody: Antigen Binding Competition. Molecules 2020; 25:molecules25163659. [PMID: 32796656 PMCID: PMC7464469 DOI: 10.3390/molecules25163659] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 07/27/2020] [Accepted: 07/28/2020] [Indexed: 11/16/2022] Open
Abstract
Vaccines and immunotherapies depend on the ability of antibodies to sensitively and specifically recognize particular antigens and specific epitopes on those antigens. As such, detailed characterization of antibody-antigen binding provides important information to guide development. Due to the time and expense required, high-resolution structural characterization techniques are typically used sparingly and late in a development process. Here, we show that antibody-antigen binding can be characterized early in a process for whole panels of antibodies by combining experimental and computational analyses of competition between monoclonal antibodies for binding to an antigen. Experimental "epitope binning" of monoclonal antibodies uses high-throughput surface plasmon resonance to reveal which antibodies compete, while a new complementary computational analysis that we call "dock binning" evaluates antibody-antigen docking models to identify why and where they might compete, in terms of possible binding sites on the antigen. Experimental and computational characterization of the identified antigenic hotspots then enables the refinement of the competitors and their associated epitope binding regions on the antigen. While not performed at atomic resolution, this approach allows for the group-level identification of functionally related monoclonal antibodies (i.e., communities) and identification of their general binding regions on the antigen. By leveraging extensive epitope characterization data that can be readily generated both experimentally and computationally, researchers can gain broad insights into the basis for antibody-antigen recognition in wide-ranging vaccine and immunotherapy discovery and development programs.
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Affiliation(s)
- Benjamin D. Brooks
- Department of Biomedical Sciences, Rocky Vista University, Ivins, UT 84738, USA
- Inovan Inc., Fargo, ND 58102, USA
- Department of Microbiology, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (T.C.); (G.H.C.)
- Correspondence: ; Tel.: +1-435-222-1403
| | - Adam Closmore
- Department of Pharmacy, North Dakota State University, Fargo, ND 58102, USA;
| | - Juechen Yang
- Department of Biomedical Engineering, North Dakota State University, Fargo, ND 58102, USA; (J.Y.); (M.H.)
| | - Michael Holland
- Department of Biomedical Engineering, North Dakota State University, Fargo, ND 58102, USA; (J.Y.); (M.H.)
| | - Tina Cairns
- Department of Microbiology, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (T.C.); (G.H.C.)
| | - Gary H. Cohen
- Department of Microbiology, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (T.C.); (G.H.C.)
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31
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Choi Y, Jeong S, Choi JM, Ndong C, Griswold KE, Bailey-Kellogg C, Kim HS. Computer-guided binding mode identification and affinity improvement of an LRR protein binder without structure determination. PLoS Comput Biol 2020; 16:e1008150. [PMID: 32866140 PMCID: PMC7485979 DOI: 10.1371/journal.pcbi.1008150] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 09/11/2020] [Accepted: 07/14/2020] [Indexed: 12/24/2022] Open
Abstract
Precise binding mode identification and subsequent affinity improvement without structure determination remain a challenge in the development of therapeutic proteins. However, relevant experimental techniques are generally quite costly, and purely computational methods have been unreliable. Here, we show that integrated computational and experimental epitope localization followed by full-atom energy minimization can yield an accurate complex model structure which ultimately enables effective affinity improvement and redesign of binding specificity. As proof-of-concept, we used a leucine-rich repeat (LRR) protein binder, called a repebody (Rb), that specifically recognizes human IgG1 (hIgG1). We performed computationally-guided identification of the Rb:hIgG1 binding mode and leveraged the resulting model to reengineer the Rb so as to significantly increase its binding affinity for hIgG1 as well as redesign its specificity toward multiple IgGs from other species. Experimental structure determination verified that our Rb:hIgG1 model closely matched the co-crystal structure. Using a benchmark of other LRR protein complexes, we further demonstrated that the present approach may be broadly applicable to proteins undergoing relatively small conformational changes upon target binding.
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Affiliation(s)
- Yoonjoo Choi
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Sukyo Jeong
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Jung-Min Choi
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Christian Ndong
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Karl E. Griswold
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, United States of America
- Norris Cotton Cancer Center at Dartmouth, Lebanon, New Hampshire, United States of America
- Department of Biological Sciences, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Chris Bailey-Kellogg
- Department of Computer Science, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Hak-Sung Kim
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
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32
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Pittala S, Bailey-Kellogg C. Learning context-aware structural representations to predict antigen and antibody binding interfaces. Bioinformatics 2020; 36:3996-4003. [PMID: 32321157 PMCID: PMC7332568 DOI: 10.1093/bioinformatics/btaa263] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Revised: 04/10/2020] [Accepted: 04/15/2020] [Indexed: 01/19/2023] Open
Abstract
MOTIVATION Understanding how antibodies specifically interact with their antigens can enable better drug and vaccine design, as well as provide insights into natural immunity. Experimental structural characterization can detail the 'ground truth' of antibody-antigen interactions, but computational methods are required to efficiently scale to large-scale studies. To increase prediction accuracy as well as to provide a means to gain new biological insights into these interactions, we have developed a unified deep learning-based framework to predict binding interfaces on both antibodies and antigens. RESULTS Our framework leverages three key aspects of antibody-antigen interactions to learn predictive structural representations: (i) since interfaces are formed from multiple residues in spatial proximity, we employ graph convolutions to aggregate properties across local regions in a protein; (ii) since interactions are specific between antibody-antigen pairs, we employ an attention layer to explicitly encode the context of the partner; (iii) since more data are available for general protein-protein interactions, we employ transfer learning to leverage this data as a prior for the specific case of antibody-antigen interactions. We show that this single framework achieves state-of-the-art performance at predicting binding interfaces on both antibodies and antigens, and that each of its three aspects drives additional improvement in the performance. We further show that the attention layer not only improves performance, but also provides a biologically interpretable perspective into the mode of interaction. AVAILABILITY AND IMPLEMENTATION The source code is freely available on github at https://github.com/vamships/PECAN.git.
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Affiliation(s)
- Srivamshi Pittala
- Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA
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33
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Pritam M, Singh G, Swaroop S, Singh AK, Pandey B, Singh SP. A cutting-edge immunoinformatics approach for design of multi-epitope oral vaccine against dreadful human malaria. Int J Biol Macromol 2020; 158:159-179. [PMID: 32360460 PMCID: PMC7189201 DOI: 10.1016/j.ijbiomac.2020.04.191] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 03/28/2020] [Accepted: 04/22/2020] [Indexed: 12/18/2022]
Abstract
Human malaria is a pathogenic disease mainly caused by Plasmodium falciparum, which was responsible for about 405,000 deaths globally in the year 2018. To date, several vaccine candidates have been evaluated for prevention, which failed to produce optimal output at various preclinical/clinical stages. This study is based on designing of polypeptide vaccines (PVs) against human malaria that cover almost all stages of life-cycle of Plasmodium and for the same 5 genome derived predicted antigenic proteins (GDPAP) have been used. For the development of a multi-immune inducer, 15 PVs were initially designed using T-cell epitope ensemble, which covered >99% human population as well as linear B-cell epitopes with or without adjuvants. The immune simulation of PVs showed higher levels of T-cell and B-cell activities compared to positive and negative vaccine controls. Furthermore, in silico cloning of PVs and codon optimization followed by enhanced expression within Lactococcus lactis host system was also explored. Although, the study has sound theoretical and in silico findings, the in vitro/in vivo evaluation seems imperative to warrant the immunogenicity and safety of PVs towards management of P. falciparum infection in the future.
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Affiliation(s)
- Manisha Pritam
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow 226028, India
| | - Garima Singh
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow 226028, India
| | - Suchit Swaroop
- Experimental & Public Health Lab, Department of Zoology, University of Lucknow, Lucknow 226007, India
| | - Akhilesh Kumar Singh
- Department of Biotechnology, Mahatma Gandhi Central University, Bihar 845401, India
| | - Brijesh Pandey
- Department of Biotechnology, Mahatma Gandhi Central University, Bihar 845401, India
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Bhattacharya M, Sharma AR, Sharma G, Patra P, Mondal N, Patra BC, Lee SS, Chakraborty C. Computer aided novel antigenic epitopes selection from the outer membrane protein sequences of Aeromonas hydrophila and its analyses. INFECTION GENETICS AND EVOLUTION 2020; 82:104320. [PMID: 32298854 DOI: 10.1016/j.meegid.2020.104320] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 04/08/2020] [Accepted: 04/10/2020] [Indexed: 11/27/2022]
Abstract
OBJECTIVES Gram-negative bacteria are among the causative microorganisms for zoonotic diseases in humans and teleosts. Outer membrane proteins (Omps) of Aeromonas hydrophila, a gram-negative bacterium, are critical for the subcellular integration to eukaryotic cell that can modulate the functions of macrophages. Hence Omps are recognized as immune markers for the vaccine development. METHODS In the present study, a 3-D model of Omps was identified using in silico technique and recognized through the Swiss model web-server and confirmed with Procheck and ProSA server.. The B-cell binding sites of the protein were selected from sequence alignment.. Further, the identification of B-cell epitope was carried out using modules of BCpred server (i.e., BCPred and Amino Acid Pairs). The identified antigenic amino acid sequences for B-cells were used to determine the T-cell epitope (both MHC I & II allelic binding sequences) using ProPred 1 and ProPred servers. RESULTS The epitopic regions (9 mer: LAGKTTNES and GFDGSQYGK) in the Omps that are bound together with the MHC molecules (MHC-I & II), and have maximum possible numbers of MHC alleles are recognized. It was observed that Omps of A. hydrophila are conserved across the serotypes and are immunogenic. These epitopes can stimulate significant immune responses and can be advantageous while preparing peptide-based vaccines against A. hydrophila infections. Thus, suggesting the use of Omps in the development of vaccines and immunotherapeutics against the bacterial diseases in humans and teleosts.
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Affiliation(s)
- Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore 756020, Odisha, India; Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-si 24252, Gangwon-do, Republic of Korea
| | - Ashish Ranjan Sharma
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-si 24252, Gangwon-do, Republic of Korea
| | - Garima Sharma
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-si 24252, Gangwon-do, Republic of Korea; Neuropsychopharmacology and Toxicology Program, College of Pharmacy, Kangwon National University, Chuncheon-si 24341, Gangwon-do, Republic of Korea
| | - Prasanta Patra
- Centre For Aquaculture Research, Extension & Livelihood, Department of Aquaculture Management & Technology, Vidyasagar University, Midnapore 721 102, West Bengal, India
| | - Niladri Mondal
- Centre For Aquaculture Research, Extension & Livelihood, Department of Aquaculture Management & Technology, Vidyasagar University, Midnapore 721 102, West Bengal, India
| | - Bidhan Chandra Patra
- Centre For Aquaculture Research, Extension & Livelihood, Department of Aquaculture Management & Technology, Vidyasagar University, Midnapore 721 102, West Bengal, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-si 24252, Gangwon-do, Republic of Korea.
| | - Chiranjib Chakraborty
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-si 24252, Gangwon-do, Republic of Korea; Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Barasat-Barrackpore Rd, Kolkata, West Bengal 700126, India.
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Kaur D, Patiyal S, Sharma N, Usmani SS, Raghava GPS. PRRDB 2.0: a comprehensive database of pattern-recognition receptors and their ligands. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2019:5523871. [PMID: 31250014 PMCID: PMC6597477 DOI: 10.1093/database/baz076] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 05/21/2019] [Accepted: 05/22/2019] [Indexed: 12/19/2022]
Abstract
PRRDB 2.0 is an updated version of PRRDB that maintains comprehensive information about pattern-recognition receptors (PRRs) and their ligands. The current version of the database has ~2700 entries, which are nearly five times of the previous version. It contains extensive information about 467 unique PRRs and 827 pathogens-associated molecular patterns (PAMPs), manually extracted from ~600 research articles. It possesses information about PRRs and PAMPs that has been extracted manually from research articles and public databases. Each entry provides comprehensive details about PRRs and PAMPs that includes their name, sequence, origin, source, type, etc. We have provided internal and external links to various databases/resources (like Swiss-Prot, PubChem) to obtain further information about PRRs and their ligands. This database also provides links to ~4500 experimentally determined structures in the protein data bank of various PRRs and their complexes. In addition, 110 PRRs with unknown structures have also been predicted, which are important in order to understand the structure-function relationship between receptors and their ligands. Numerous web-based tools have been integrated into PRRDB 2.0 to facilitate users to perform different tasks like (i) extensive searching of the database; (ii) browsing or categorization of data based on receptors, ligands, source, etc. and (iii) similarity search using BLAST and Smith-Waterman algorithm.
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Affiliation(s)
- Dilraj Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
| | - Neelam Sharma
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
| | - Salman Sadullah Usmani
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India.,Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, Chandigarh 160036, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
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Yan ZY, Li HM, Wang CC, Qiu J, Pan Y, Zhang D, Hu W, Guo HJ. Preparation of a new monoclonal antibody against subgroup A of avian leukosis virus and identifying its antigenic epitope. Int J Biol Macromol 2019; 156:1234-1242. [PMID: 31759029 DOI: 10.1016/j.ijbiomac.2019.11.161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Revised: 11/17/2019] [Accepted: 11/19/2019] [Indexed: 11/26/2022]
Abstract
This study focuses on preparing the monoclonal antibody (MAb) against subgroup A of avian leukosis virus (ALV-A) and identifying its antigenic epitope. The ALV-A gp85 gene with a size of 1005bp was amplified and expressed into a recombinant protein with a size of 46KD in E.coli. The products expressed after purification were inoculated into BALB/c mice for preparing antibody-secreting splenic lymphocytes and further obtaining hybridoma cells. Finally, one new hybridoma cell (A18GH) secreting MAb against ALV-A was screened, and the MAb was able to detect ALV-A/K strains in an indirect immunofluorescence assay (IFA), but not ALV-B/J strains. A total of 14 overlapping truncated ALV-A gp85 protein segments were expressed and eight peptides containing different antigenic amino acids were artificially synthesized for analyzing the antigenic epitope of the MAb using a western blot or an ELISA, and the results indicate that the antigenic epitope consists of seven amino acids within the 146-ATRFLLR -152 region of the ALV-A gp85 protein. A biological information analysis shows that the antigenic epitope has a high antigenic index and develops a curved linear spatial structure. Further, its 7 amino acids are completely within the 17 representative ALV-A strains, 4 are within the 11 ALV-K strains, and fewer are within the ALV-B/J/E strains. This study will significantly assist in a further understanding of the protein structure and function of ALV-A, and in the establishment of specific ALV-A detection methods.
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Affiliation(s)
- Ze-Yi Yan
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, Tai'an 271018, China; College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an 271018, China
| | - Hong-Mei Li
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, Tai'an 271018, China; College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an 271018, China
| | - Cheng-Cheng Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, Tai'an 271018, China; College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an 271018, China
| | - Jianhua Qiu
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, Tai'an 271018, China; College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an 271018, China
| | - Yao Pan
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, Tai'an 271018, China; College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an 271018, China
| | - Dandan Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, Tai'an 271018, China; College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an 271018, China
| | - Weiguo Hu
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, Tai'an 271018, China; College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an 271018, China
| | - Hui-Jun Guo
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, Tai'an 271018, China; College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an 271018, China.
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Karadag M, Arslan M, Kaleli NE, Kalyoncu S. Physicochemical determinants of antibody-protein interactions. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2019; 121:85-114. [PMID: 32312427 DOI: 10.1016/bs.apcsb.2019.08.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Antibodies are specialized proteins generated by immune system for high specificity and affinity binding to target antigens. Because of their essential roles in immune system, antibodies have been successfully developed and engineered as biopharmaceuticals for treatment of various diseases. Analysis of antibody-protein interactions is always required to get detailed information on effectivity of such antibody-based therapeutics. Although physicochemical rules cannot be generalized for every antibody-protein interaction, there are some features which should be taken into account during antibody development and engineering efforts. In this chapter, physicochemical analysis of antibody paratope-protein epitope interactions will be discussed to highlight important characteristics. First, paratope and non-paratope regions of antibodies will be described and important roles of these regions on binding and biophysical features of antibodies will be discussed. Then, general features of epitope regions of protein antigens will be introduced along with several computational/experimental tools to identify them. Lastly, a rising star of antibody biopharmaceuticals, nanobodies, will be described to show importance of next-generation antibody fragment based biopharmaceuticals in drug development.
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Affiliation(s)
- Murat Karadag
- Izmir Biomedicine and Genome Center, İzmir, Turkey; Izmir Biomedicine and Genome Institute, Dokuz Eylul University, İzmir, Turkey
| | - Merve Arslan
- Izmir Biomedicine and Genome Center, İzmir, Turkey; Izmir Biomedicine and Genome Institute, Dokuz Eylul University, İzmir, Turkey
| | - Nazli Eda Kaleli
- Izmir Biomedicine and Genome Center, İzmir, Turkey; Izmir Biomedicine and Genome Institute, Dokuz Eylul University, İzmir, Turkey
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Mahajan S, Yan Z, Jespersen MC, Jensen KK, Marcatili P, Nielsen M, Sette A, Peters B. Benchmark datasets of immune receptor-epitope structural complexes. BMC Bioinformatics 2019; 20:490. [PMID: 31601176 PMCID: PMC6785892 DOI: 10.1186/s12859-019-3109-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 09/20/2019] [Indexed: 02/05/2023] Open
Abstract
Background The development of accurate epitope prediction tools is important in facilitating disease diagnostics, treatment and vaccine development. The advent of new approaches making use of antibody and TCR sequence information to predict receptor-specific epitopes have the potential to transform the epitope prediction field. Development and validation of these new generation of epitope prediction methods would benefit from regularly updated high-quality receptor-antigen complex datasets. Results To address the need for high-quality datasets to benchmark performance of these new generation of receptor-specific epitope prediction tools, a webserver called SCEptRe (Structural Complexes of Epitope-Receptor) was created. SCEptRe extracts weekly updated 3D complexes of antibody-antigen, TCR-pMHC and MHC-ligand from the Immune Epitope Database and clusters them based on antigen, receptor and epitope features to generate benchmark datasets. SCEptRe also provides annotated information such as CDR sequences and VDJ genes on the receptors. Users can generate custom datasets based by selecting thresholds for structural quality and clustering parameters (e.g. resolution, R-free factor, antigen or epitope sequence identity) based on their need. Conclusions SCEptRe provides weekly updated, user-customized comprehensive benchmark datasets of immune receptor-epitope structural complexes. These datasets can be used to develop and benchmark performance of receptor-specific epitope prediction tools in the future. SCEptRe is freely accessible at http://tools.iedb.org/sceptre.
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Affiliation(s)
- Swapnil Mahajan
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, 92037, USA.
| | - Zhen Yan
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, 92037, USA
| | | | | | - Paolo Marcatili
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Alessandro Sette
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, 92037, USA.,Department of Medicine, University of California, San Diego, CA, 92093, USA
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, 92037, USA.,Department of Medicine, University of California, San Diego, CA, 92093, USA
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Zhao L, Wu S, Jiang J, Li W, Luo J, Li J. Novel overlapping subgraph clustering for the detection of antigen epitopes. Bioinformatics 2019; 34:2061-2068. [PMID: 29409062 DOI: 10.1093/bioinformatics/bty051] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 02/01/2018] [Indexed: 11/12/2022] Open
Abstract
Motivation Antigens that contain overlapping epitopes have been occasionally reported. As current algorithms mainly take a one-antigen-one-epitope approach to the prediction of epitopes, they are not capable of detecting these multiple and overlapping epitopes accurately, or even those multiple and separated epitopes existing in some other antigens. Results We introduce a novel subgraph clustering algorithm for more accurate detection of epitopes. This algorithm takes graph partitions as seeds, and expands the seeds to merge overlapping subgraphs based on the term frequency-inverse document frequency (TF-IDF) featured similarity. Then, the merged subgraphs are each classified as an epitope or non-epitope. Tests of our algorithm were conducted on three newly collected datasets of antigens. In the first dataset, each antigen contains only a single epitope; in the second, each antigen contains only multiple and separated epitopes; and in the third, each antigen contains overlapping epitopes. The prediction performance of our algorithm is significantly better than the state-of-art methods. The lifts of the averaged f-scores on top of the best existing methods are 60, 75 and 22% for the single epitope detection, the multiple and separated epitopes detection, and the overlapping epitopes detection, respectively. Availability and implementation The source code is available at github.com/lzhlab/glep/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Liang Zhao
- Department of Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Hubei, China.,Department of Computer Science, School of Computing and Electronic Information, Guangxi University, Nanning, China
| | - Shaogui Wu
- Department of Computer Science, School of Computing and Electronic Information, Guangxi University, Nanning, China
| | - Jiawen Jiang
- Department of Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Hubei, China
| | - Wencui Li
- Department of Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Hubei, China
| | - Jie Luo
- Department of Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Hubei, China
| | - Jinyan Li
- Department of Data Science, Advanced Analytics Institute, Faculty of Engineering and IT, University of Technology Sydney, Broadway, NSW 2007, Australia
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Peng B, Zhuang Q, Yu M, Li J, Liu Y, Zhu L, Ming Y. Comparison of Physical Crossmatch and Virtual Crossmatch to Identify Preexisting Donor-Specific Human Leukocyte Antigen (HLA) Antibodies and Outcome Following Kidney Transplantation. Med Sci Monit 2019; 25:952-961. [PMID: 30712055 PMCID: PMC6369651 DOI: 10.12659/msm.914902] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Background Physical crossmatch (PXM) and virtual crossmatch (VXM) are applied to identify preexisting donor-specific human leukocyte antigen (HLA) antibodies in patients awaiting kidney transplantation. Recently, high-resolution epitope analysis has emerged as a novel strategy for VXM. A retrospective clinical study compared PXM with VXM before kidney transplantation and recipient outcome following transplantation. Material/Methods Between August 2017 and March 2018, 239 patients underwent crossmatching and 94 patients received a donor kidney. A complement-dependent cytotoxicity (CDC) PXM assay and VXM using serological and epitope analysis identified donor-specific antibodies (DSA). Crossmatch results and clinical outcome at 3 months were compared. Results VXM identified serological DSA (sDSA), verified epitope DSA, and total epitope DSA in 74 (31.0%), 39 (16.3%), and 49 (20.5%) cases, respectively. Eleven cases (4.6%) had a positive PXM detected by the CDC assay. Of 94 kidney transplant recipients, 21 had preexisting sDSA but were negative in PXM; there was 1 case of delayed graft function (DGF) and no cases of hyperacute rejection or acute rejection. Of the rest of the 73 recipients who were negative for sDSA, 8 had acute rejection (P=0.253) and 19 had DGF (P=0.037). No significant differences were found in graft survival at 3 months. Conclusions High-resolution epitope analysis identified fewer cases with DSA compared with serological analysis. Because patients with and without sDSA had a similar short-term outcome in the setting of a negative PXM, the presence of preexisting sDSA, determined by VXM, should not be an absolute contraindication for kidney transplantation.
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Affiliation(s)
- Bo Peng
- The Transplantation Center of The Third Xiangya Hospital, Central South University, Changsha, Hunan, China (mainland).,Engineering and Technology Research Center for Transplantation Medicine of The National Ministry of Health, Changsha, Hunan, China (mainland)
| | - Quan Zhuang
- The Transplantation Center of The Third Xiangya Hospital, Central South University, Changsha, Hunan, China (mainland).,Engineering and Technology Research Center for Transplantation Medicine of The National Ministry of Health, Changsha, Hunan, China (mainland)
| | - Meng Yu
- The Transplantation Center of The Third Xiangya Hospital, Central South University, Changsha, Hunan, China (mainland).,Engineering and Technology Research Center for Transplantation Medicine of The National Ministry of Health, Changsha, Hunan, China (mainland)
| | - Junhui Li
- The Transplantation Center of The Third Xiangya Hospital, Central South University, Changsha, Hunan, China (mainland).,Engineering and Technology Research Center for Transplantation Medicine of The National Ministry of Health, Changsha, Hunan, China (mainland)
| | - Yun Liu
- The Transplantation Center of The Third Xiangya Hospital, Central South University, Changsha, Hunan, China (mainland).,Engineering and Technology Research Center for Transplantation Medicine of The National Ministry of Health, Changsha, Hunan, China (mainland)
| | - Lijun Zhu
- The Transplantation Center of The Third Xiangya Hospital, Central South University, Changsha, Hunan, China (mainland).,Engineering and Technology Research Center for Transplantation Medicine of The National Ministry of Health, Changsha, Hunan, China (mainland)
| | - Yingzi Ming
- The Transplantation Center of The Third Xiangya Hospital, Central South University, Changsha, Hunan, China (mainland).,Engineering and Technology Research Center for Transplantation Medicine of The National Ministry of Health, Changsha, Hunan, China (mainland)
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Jung Y, El-Manzalawy Y, Dobbs D, Honavar VG. Partner-specific prediction of RNA-binding residues in proteins: A critical assessment. Proteins 2018; 87:198-211. [PMID: 30536635 PMCID: PMC6389706 DOI: 10.1002/prot.25639] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 10/10/2018] [Accepted: 11/29/2018] [Indexed: 01/06/2023]
Abstract
RNA-protein interactions play essential roles in regulating gene expression. While some RNA-protein interactions are "specific", that is, the RNA-binding proteins preferentially bind to particular RNA sequence or structural motifs, others are "non-RNA specific." Deciphering the protein-RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein-RNA interfaces, there is a need for computational methods to identify RNA-binding residues in proteins. While most of the existing computational methods for predicting RNA-binding residues in RNA-binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner-specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner-specific protein-RNA interface prediction tools, PS-PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA-specificity metric (RSM), for quantifying the RNA-specificity of the RNA binding residues predicted by such tools. Our results show that the RNA-binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner-agnostic metrics, RNA partner-specific methods are outperformed by the state-of-the-art partner-agnostic methods. We conjecture that either (a) the protein-RNA complexes in PDB are not representative of the protein-RNA interactions in nature, or (b) the current methods for partner-specific prediction of RNA-binding residues in proteins fail to account for the differences in RNA partner-specific versus partner-agnostic protein-RNA interactions, or both.
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Affiliation(s)
- Yong Jung
- Bioinformatics and Genomics Graduate Program, Pennsylvania State University, University Park, Pennsylvania.,Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, Pennsylvania.,The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania
| | - Yasser El-Manzalawy
- Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, Pennsylvania.,Clinical and Translational Sciences Institute, Pennsylvania State University, University Park, Pennsylvania.,College of Information Sciences and Technology, Pennsylvania State University, Pennsylvania
| | - Drena Dobbs
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa.,Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, Iowa
| | - Vasant G Honavar
- Bioinformatics and Genomics Graduate Program, Pennsylvania State University, University Park, Pennsylvania.,Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, Pennsylvania.,Institute for Cyberscience, Pennsylvania State University, University Park, Pennsylvania.,Clinical and Translational Sciences Institute, Pennsylvania State University, University Park, Pennsylvania.,The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania.,College of Information Sciences and Technology, Pennsylvania State University, Pennsylvania
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Abstract
Antibodies are protein molecules used routinely for therapeutic, diagnostic, and research purposes due to their exquisite ability to selectively recognize and bind a given antigen. The particular area of the antigen recognized by the antibody is called the epitope, and for proteinaceous antigens the epitope can be of complex nature. Information about the binding epitope of an antibody can provide important mechanistic insights and indicate for what applications an antibody might be useful. Therefore, a variety of epitope mapping techniques have been developed to localize such regions. Although the real picture is even more complex, epitopes in protein antigens are broadly grouped into linear or discontinuous epitopes depending on the positioning of the epitope residues in the antigen sequence and the requirement of structure. Specialized methods for mapping of the two different classes of epitopes, using high-throughput or high-resolution methods, have been developed. While different in their detail, all of the experimental methods rely on assessing the binding of the antibody to the antigen or a set of antigen mimics. Early approaches utilizing sets of truncated proteins, small numbers of synthesized peptides, and structural analyses of antibody-antigen complexes have been significantly refined. Current state-of-the-art methods involve combinations of mutational scanning, protein display, and high-throughput screening in conjunction with bioinformatic analyses of large datasets.
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Affiliation(s)
- Johan Nilvebrant
- KTH School of Engineering Sciences in Chemistry, Biotechnology and Health, Protein Engineering, Stockholm, Sweden.
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.
| | - Johan Rockberg
- KTH School of Engineering Sciences in Chemistry, Biotechnology and Health, Protein Technology, Stockholm, Sweden.
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Fundamentals and Methods for T- and B-Cell Epitope Prediction. J Immunol Res 2017; 2017:2680160. [PMID: 29445754 PMCID: PMC5763123 DOI: 10.1155/2017/2680160] [Citation(s) in RCA: 284] [Impact Index Per Article: 40.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Revised: 11/22/2017] [Accepted: 11/27/2017] [Indexed: 12/25/2022] Open
Abstract
Adaptive immunity is mediated by T- and B-cells, which are immune cells capable of developing pathogen-specific memory that confers immunological protection. Memory and effector functions of B- and T-cells are predicated on the recognition through specialized receptors of specific targets (antigens) in pathogens. More specifically, B- and T-cells recognize portions within their cognate antigens known as epitopes. There is great interest in identifying epitopes in antigens for a number of practical reasons, including understanding disease etiology, immune monitoring, developing diagnosis assays, and designing epitope-based vaccines. Epitope identification is costly and time-consuming as it requires experimental screening of large arrays of potential epitope candidates. Fortunately, researchers have developed in silico prediction methods that dramatically reduce the burden associated with epitope mapping by decreasing the list of potential epitope candidates for experimental testing. Here, we analyze aspects of antigen recognition by T- and B-cells that are relevant for epitope prediction. Subsequently, we provide a systematic and inclusive review of the most relevant B- and T-cell epitope prediction methods and tools, paying particular attention to their foundations.
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Sypek M, Kausman J, Holt S, Hughes P. HLA Epitope Matching in Kidney Transplantation: An Overview for the General Nephrologist. Am J Kidney Dis 2017; 71:720-731. [PMID: 29246419 DOI: 10.1053/j.ajkd.2017.09.021] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 09/20/2017] [Indexed: 11/11/2022]
Abstract
Rapid changes in tissue-typing technology, including the widespread availability of highly specific molecular typing methods and solid-phase assays for the detection of allele-specific anti-HLA antibodies, make it increasingly challenging to remain up to date with developments in organ matching. Terms such as epitopes and eplets abound in the transplantation literature, but often it can be difficult to see what they might mean for the patient awaiting transplantation. In this review, we provide the historical context for current practice in tissue typing and explore the potential role of HLA epitopes in kidney transplantation. Despite impressive gains in preventing and managing T-cell-mediated rejection and the associated improvements in graft survival, the challenge of the humoral alloresponse remains largely unmet and is the major cause of late graft loss. Describing HLA antigens as a series of antibody targets, or epitopes, rather than based on broad seroreactivity patterns or precise amino acid sequences may provide a more practical and clinically relevant system to help avoid antibody-mediated rejection, reduce sensitization, and select the most appropriate organs in the setting of pre-existing alloantibodies. We explain the systems proposed to define HLA epitopes, summarize the evidence to date for their role in transplantation, and explore the potential benefits of incorporating HLA epitopes into clinical practice as this field continues to evolve toward everyday practice.
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Affiliation(s)
- Matthew Sypek
- Department of Nephrology, Royal Melbourne Hospital, Melbourne, Australia; The University of Melbourne, Melbourne, Australia; Department of Nephrology, Royal Children's Hospital, Melbourne, Australia.
| | - Joshua Kausman
- Department of Nephrology, Royal Melbourne Hospital, Melbourne, Australia; The University of Melbourne, Melbourne, Australia; Department of Nephrology, Royal Children's Hospital, Melbourne, Australia
| | - Steve Holt
- Department of Nephrology, Royal Melbourne Hospital, Melbourne, Australia; The University of Melbourne, Melbourne, Australia
| | - Peter Hughes
- Department of Nephrology, Royal Melbourne Hospital, Melbourne, Australia; The University of Melbourne, Melbourne, Australia
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Hegde NR, Gauthami S, Sampath Kumar HM, Bayry J. The use of databases, data mining and immunoinformatics in vaccinology: where are we? Expert Opin Drug Discov 2017; 13:117-130. [PMID: 29226722 DOI: 10.1080/17460441.2018.1413088] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
INTRODUCTION Vaccinology has evolved from a sub-discipline focussed on simplistic vaccine development based on antibody-mediated protection to a separate discipline involving epidemiology, host and pathogen biology, immunology, genomics, proteomics, structure biology, protein engineering, chemical biology, and delivery systems. Data mining in combination with bioinformatics has provided a scaffold linking all these disciplines to the design of vaccines and vaccine adjuvants. Areas covered: This review provides background knowledge on immunological aspects which have been exploited with informatics for the in silico analysis of immune responses and the design of vaccine antigens. Furthermore, the article presents various databases and bioinformatics tools, and discusses B and T cell epitope predictions, antigen design, adjuvant research and systems immunology, highlighting some important examples, and challenges for the future. Expert opinion: Informatics and data mining have not only reduced the time required for experimental immunology, but also contributed to the identification and design of novel vaccine candidates and the determination of biomarkers and pathways of vaccine response. However, more experimental data is required for benchmarking immunoinformatic tools. Nevertheless, developments in immunoinformatics and reverse vaccinology, which are nascent fields, are likely to hasten vaccine discovery, although the path to regulatory approval is likely to remain a necessary impediment.
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Affiliation(s)
| | - S Gauthami
- b Ella Foundation, Turkapally , Hyderabad , India
| | - H M Sampath Kumar
- c Council of Scientific and Industrial Research - Indian Institute of Chemical Technology , Hyderabad , India
| | - Jagadeesh Bayry
- d Institut National de la Santé et de la Recherche Médicale (INSERM) Unité 1138 , Centre de Recherche des Cordeliers, Paris , France
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Hua CK, Gacerez AT, Sentman CL, Ackerman ME, Choi Y, Bailey-Kellogg C. Computationally-driven identification of antibody epitopes. eLife 2017; 6:29023. [PMID: 29199956 PMCID: PMC5739537 DOI: 10.7554/elife.29023] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 12/02/2017] [Indexed: 12/21/2022] Open
Abstract
Understanding where antibodies recognize antigens can help define mechanisms of action and provide insights into progression of immune responses. We investigate the extent to which information about binding specificity implicitly encoded in amino acid sequence can be leveraged to identify antibody epitopes. In computationally-driven epitope localization, possible antibody–antigen binding modes are modeled, and targeted panels of antigen variants are designed to experimentally test these hypotheses. Prospective application of this approach to two antibodies enabled epitope localization using five or fewer variants per antibody, or alternatively, a six-variant panel for both simultaneously. Retrospective analysis of a variety of antibodies and antigens demonstrated an almost 90% success rate with an average of three antigen variants, further supporting the observation that the combination of computational modeling and protein design can reveal key determinants of antibody–antigen binding and enable efficient studies of collections of antibodies identified from polyclonal samples or engineered libraries.
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Affiliation(s)
- Casey K Hua
- Thayer School of Engineering, Dartmouth College, Hanover, United States.,Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Albert T Gacerez
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Charles L Sentman
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Margaret E Ackerman
- Thayer School of Engineering, Dartmouth College, Hanover, United States.,Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Yoonjoo Choi
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
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Abstract
Antibody-protein interactions play a critical role in the humoral immune response. B-cells secrete antibodies, which bind antigens (e.g., cell surface proteins of pathogens). The specific parts of antigens that are recognized by antibodies are called B-cell epitopes. These epitopes can be linear, corresponding to a contiguous amino acid sequence fragment of an antigen, or conformational, in which residues critical for recognition may not be contiguous in the primary sequence, but are in close proximity within the folded protein 3D structure.Identification of B-cell epitopes in target antigens is one of the key steps in epitope-driven subunit vaccine design, immunodiagnostic tests, and antibody production. In silico bioinformatics techniques offer a promising and cost-effective approach for identifying potential B-cell epitopes in a target vaccine candidate. In this chapter, we show how to utilize online B-cell epitope prediction tools to identify linear B-cell epitopes from the primary amino acid sequence of proteins.
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Affiliation(s)
- Yasser El-Manzalawy
- College of Information Sciences and Technology, Pennsylvania State University, 301A Information Sciences and Technology Building, University Park, PA, 16802, USA
| | - Drena Dobbs
- Genetics, Development and Cell Biology Department, Iowa State University, Ames, IA, 50011, USA
| | - Vasant G Honavar
- College of Information Sciences and Technology, Pennsylvania State University, 301A Information Sciences and Technology Building, University Park, PA, 16802, USA.
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Van Regenmortel MHV. Structure-Based Reverse Vaccinology Failed in the Case of HIV Because it Disregarded Accepted Immunological Theory. Int J Mol Sci 2016; 17:E1591. [PMID: 27657055 PMCID: PMC5037856 DOI: 10.3390/ijms17091591] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Revised: 08/30/2016] [Accepted: 09/07/2016] [Indexed: 12/14/2022] Open
Abstract
Two types of reverse vaccinology (RV) should be distinguished: genome-based RV for bacterial vaccines and structure-based RV for viral vaccines. Structure-based RV consists in trying to generate a vaccine by first determining the crystallographic structure of a complex between a viral epitope and a neutralizing monoclonal antibody (nMab) and then reconstructing the epitope by reverse molecular engineering outside the context of the native viral protein. It is based on the unwarranted assumption that the epitope designed to fit the nMab will have acquired the immunogenic capacity to elicit a polyclonal antibody response with the same protective capacity as the nMab. After more than a decade of intensive research using this type of RV, this approach has failed to deliver an effective, preventive HIV-1 vaccine. The structure and dynamics of different types of HIV-1 epitopes and of paratopes are described. The rational design of an anti-HIV-1 vaccine is shown to be a misnomer since investigators who claim that they design a vaccine are actually only improving the antigenic binding capacity of one epitope with respect to only one paratope and not the immunogenic capacity of an epitope to elicit neutralizing antibodies. Because of the degeneracy of the immune system and the polyspecificity of antibodies, each epitope studied by the structure-based RV procedure is only one of the many epitopes that the particular nMab is able to recognize and there is no reason to assume that this nMab must have been elicited by this one epitope of known structure. Recent evidence is presented that the trimeric Env spikes of the virus possess such an enormous plasticity and intrinsic structural flexibility that it is it extremely difficult to determine which Env regions are the best candidate vaccine immunogens most likely to elicit protective antibodies.
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Affiliation(s)
- Marc H V Van Regenmortel
- UMR 7242 Biotechnologie et Signalisation Cellulaire, Université de Strasbourg-CNRS, 300, Boulevard Sébastien Brant, CS 10413, 67412 Illkirch Cedex, France.
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Omics Approaches for the Study of Adaptive Immunity to Staphylococcus aureus and the Selection of Vaccine Candidates. Proteomes 2016; 4:proteomes4010011. [PMID: 28248221 PMCID: PMC5217363 DOI: 10.3390/proteomes4010011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Revised: 02/05/2016] [Accepted: 03/01/2016] [Indexed: 01/20/2023] Open
Abstract
Staphylococcus aureus is a dangerous pathogen both in hospitals and in the community. Due to the crisis of antibiotic resistance, there is an urgent need for new strategies to combat S. aureus infections, such as vaccination. Increasing our knowledge about the mechanisms of protection will be key for the successful prevention or treatment of S. aureus invasion. Omics technologies generate a comprehensive picture of the physiological and pathophysiological processes within cells, tissues, organs, organisms and even populations. This review provides an overview of the contribution of genomics, transcriptomics, proteomics, metabolomics and immunoproteomics to the current understanding of S. aureus‑host interaction, with a focus on the adaptive immune response to the microorganism. While antibody responses during colonization and infection have been analyzed in detail using immunoproteomics, the full potential of omics technologies has not been tapped yet in terms of T-cells. Omics technologies promise to speed up vaccine development by enabling reverse vaccinology approaches. In consequence, omics technologies are powerful tools for deepening our understanding of the “superbug” S. aureus and for improving its control.
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Burkovitz A, Ofran Y. Understanding differences between synthetic and natural antibodies can help improve antibody engineering. MAbs 2015; 8:278-87. [PMID: 26652053 DOI: 10.1080/19420862.2015.1123365] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
Abstract
Synthetic libraries are a major source of human-like antibody (Ab) drug leads. To assess the similarity between natural Abs and the products of these libraries, we compared large sets of natural and synthetic Abs using "CDRs Analyzer," a tool we introduce for structural analysis of Ab-antigen (Ag) interactions. Natural Abs, we found, recognize their Ags by combining multiple complementarity-determining regions (CDRs) to create an integrated interface. Synthetic Abs, however, rely dominantly, sometimes even exclusively on CDRH3. The increased contribution of CDRH3 to Ag binding in synthetic Abs comes with a substantial decrease in the involvement of CDRH2 and CDRH1. Furthermore, in natural Abs CDRs specialize in specific types of non-covalent interactions with the Ag. CDRH1 accounts for a significant portion of the cation-pi interactions; CDRH2 is the major source of salt-bridges and CDRH3 accounts for most hydrogen bonds. In synthetic Abs this specialization is lost, and CDRH3 becomes the main sources of all types of contacts. The reliance of synthetic Abs on CDRH3 reduces the complexity of their interaction with the Ag: More Ag residues contact only one CDR and fewer contact 3 CDRs or more. We suggest that the focus of engineering attempts on CDRH3 results in libraries enriched with variants that are not natural-like. This may affect not only Ag binding, but also Ab expression, stability and selectivity. Our findings can help guide library design, creating libraries that can bind more epitopes and Abs that better mimic the natural antigenic interactions.
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Affiliation(s)
- Anat Burkovitz
- a The Goodman Faculty of Life Sciences, Nanotechnology Building, Bar Ilan University , Ramat Gan , 5290002 , Israel
| | - Yanay Ofran
- a The Goodman Faculty of Life Sciences, Nanotechnology Building, Bar Ilan University , Ramat Gan , 5290002 , Israel
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