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Chen H, Fan X, Zhu S, Pei Y, Zhang X, Zhang X, Liu L, Qian F, Tian B. Accurate prediction of CDR-H3 loop structures of antibodies with deep learning. eLife 2024; 12:RP91512. [PMID: 38921957 PMCID: PMC11208048 DOI: 10.7554/elife.91512] [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] [Indexed: 06/27/2024] Open
Abstract
Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D structures of monoclonal antibodies and nanobodies. H3-OPT combines the strengths of AlphaFold2 with a pre-trained protein language model and provides a 2.24 Å average RMSDCα between predicted and experimentally determined CDR-H3 loops, thus outperforming other current computational methods in our non-redundant high-quality dataset. The model was validated by experimentally solving three structures of anti-VEGF nanobodies predicted by H3-OPT. We examined the potential applications of H3-OPT through analyzing antibody surface properties and antibody-antigen interactions. This structural prediction tool can be used to optimize antibody-antigen binding and engineer therapeutic antibodies with biophysical properties for specialized drug administration route.
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Affiliation(s)
- Hedi Chen
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua UniversityBeijingChina
| | - Xiaoyu Fan
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua UniversityBeijingChina
| | - Shuqian Zhu
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua UniversityBeijingChina
| | - Yuchan Pei
- Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua UniversityBeijingChina
| | - Xiaochun Zhang
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua UniversityBeijingChina
| | - Xiaonan Zhang
- Department of Natural Language Processing, Baidu International Technology (Shenzhen) Co LtdShenzhenChina
| | - Lihang Liu
- Department of Natural Language Processing, Baidu International Technology (Shenzhen) Co LtdShenzhenChina
| | - Feng Qian
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua UniversityBeijingChina
| | - Boxue Tian
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua UniversityBeijingChina
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Kim HM, Kim KJ, Lee K, Yoon MJ, Choih J, Hong TJ, Cho EJ, Jung HJ, Kim J, Park JS, Na HY, Heo YS, Park CG, Park H, Han S, Bae D. GNUV201, a novel human/mouse cross-reactive and low pH-selective anti-PD-1 monoclonal antibody for cancer immunotherapy. BMC Immunol 2024; 25:29. [PMID: 38730320 PMCID: PMC11088064 DOI: 10.1186/s12865-024-00609-z] [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: 10/18/2023] [Accepted: 02/13/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Several PD-1 antibodies approved as anti-cancer therapies work by blocking the interaction of PD-1 with its ligand PD-L1, thus restoring anti-cancer T cell activities. These PD-1 antibodies lack inter-species cross-reactivity, necessitating surrogate antibodies for preclinical studies, which may limit the predictability and translatability of the studies. RESULTS To overcome this limitation, we have developed an inter-species cross-reactive PD-1 antibody, GNUV201, by utilizing an enhanced diversity mouse platform (SHINE MOUSE™). GNUV201 equally binds to human PD-1 and mouse PD-1, equally inhibits the binding of human PD-1/PD-L1 and mouse PD-1/PD-L1, and effectively suppresses tumor growth in syngeneic mouse models. The epitope of GNUV201 mapped to the "FG loop" of hPD-1, distinct from those of Keytruda® ("C'D loop") and Opdivo® (N-term). Notably, the structural feature where the protruding epitope loop fits into GNUV201's binding pocket supports the enhanced binding affinity due to slower dissociation (8.7 times slower than Keytruda®). Furthermore, GNUV201 shows a stronger binding affinity at pH 6.0 (5.6 times strong than at pH 7.4), which mimics the hypoxic and acidic tumor microenvironment (TME). This phenomenon is not observed with marketed antibodies (Keytruda®, Opdivo®), implying that GNUV201 achieves more selective binding to and better occupancy on PD-1 in the TME. CONCLUSIONS In summary, GNUV201 exhibited enhanced affinity for PD-1 with slow dissociation and preferential binding in TME-mimicking low pH. Human/monkey/mouse inter-species cross-reactivity of GNUV201 could enable more predictable and translatable efficacy and toxicity preclinical studies. These results suggest that GNUV201 could be an ideal antibody candidate for anti-cancer drug development.
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MESH Headings
- Animals
- Humans
- Programmed Cell Death 1 Receptor/immunology
- Programmed Cell Death 1 Receptor/metabolism
- Programmed Cell Death 1 Receptor/antagonists & inhibitors
- Mice
- Cross Reactions/immunology
- Immunotherapy/methods
- Hydrogen-Ion Concentration
- Neoplasms/immunology
- Neoplasms/therapy
- B7-H1 Antigen/immunology
- B7-H1 Antigen/metabolism
- B7-H1 Antigen/antagonists & inhibitors
- Cell Line, Tumor
- Antibodies, Monoclonal/immunology
- Antibodies, Monoclonal/pharmacology
- Antibodies, Monoclonal/therapeutic use
- Immune Checkpoint Inhibitors/pharmacology
- Epitopes/immunology
- Antibodies, Monoclonal, Humanized/immunology
- Antibodies, Monoclonal, Humanized/therapeutic use
- Antibodies, Monoclonal, Humanized/pharmacology
- Mice, Inbred C57BL
- Female
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Affiliation(s)
- Hae-Mi Kim
- Genuv Inc., B1 Shinyoung Building, 14 Gyeonghuigung-gil, Jongno-gu, Seoul, Republic of Korea
| | - Kyoung-Jin Kim
- Genuv Inc., B1 Shinyoung Building, 14 Gyeonghuigung-gil, Jongno-gu, Seoul, Republic of Korea
| | - Kwanghyun Lee
- Genuv Inc., B1 Shinyoung Building, 14 Gyeonghuigung-gil, Jongno-gu, Seoul, Republic of Korea
| | - Myeong Jin Yoon
- Genuv Inc., B1 Shinyoung Building, 14 Gyeonghuigung-gil, Jongno-gu, Seoul, Republic of Korea
| | - Jenny Choih
- Genuv Inc., B1 Shinyoung Building, 14 Gyeonghuigung-gil, Jongno-gu, Seoul, Republic of Korea
- Genuv US Subsidiary, CIC, 1 Broadway, Cambridge, MA, USA
| | - Tae-Joon Hong
- Genuv Inc., B1 Shinyoung Building, 14 Gyeonghuigung-gil, Jongno-gu, Seoul, Republic of Korea
| | - Eun Ji Cho
- Genuv Inc., B1 Shinyoung Building, 14 Gyeonghuigung-gil, Jongno-gu, Seoul, Republic of Korea
| | - Hak-Jun Jung
- Genuv Inc., B1 Shinyoung Building, 14 Gyeonghuigung-gil, Jongno-gu, Seoul, Republic of Korea
| | - Jayoung Kim
- Genuv Inc., B1 Shinyoung Building, 14 Gyeonghuigung-gil, Jongno-gu, Seoul, Republic of Korea
| | - Ji Soo Park
- Laboratory of Immunology, Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
- Brain Korea 21 PLUS/FOUR Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hye Young Na
- Laboratory of Immunology, Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Neurology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yong-Seok Heo
- Department of Chemistry, Konkuk University, 120 Neungdong-Ro, Gwangjin-Gu, Seoul, 05029, Republic of Korea
| | - Chae Gyu Park
- Genuv Inc., B1 Shinyoung Building, 14 Gyeonghuigung-gil, Jongno-gu, Seoul, Republic of Korea
- Laboratory of Immunology, Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Heungrok Park
- Genuv Inc., B1 Shinyoung Building, 14 Gyeonghuigung-gil, Jongno-gu, Seoul, Republic of Korea
| | - Sungho Han
- Genuv Inc., B1 Shinyoung Building, 14 Gyeonghuigung-gil, Jongno-gu, Seoul, Republic of Korea
- Genuv US Subsidiary, CIC, 1 Broadway, Cambridge, MA, USA
| | - Donggoo Bae
- Genuv Inc., B1 Shinyoung Building, 14 Gyeonghuigung-gil, Jongno-gu, Seoul, Republic of Korea.
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Han M, Zhang N, Mao Y, Huang B, Ren M, Peng Z, Bai Z, Chen L, Liu Y, Wang S, Huang S, Cheng Z. The Potential of Gut Microbiota Metabolic Capability to Detect Drug Response in Rheumatoid Arthritis Patients. Front Microbiol 2022; 13:839015. [PMID: 35464950 PMCID: PMC9024311 DOI: 10.3389/fmicb.2022.839015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 02/26/2022] [Indexed: 12/29/2022] Open
Abstract
Gut microbiota plays an essential role in the development of rheumatoid arthritis (RA) and affects drug responses. However, the underlying mechanism remains elusive and urgent to elucidate to explore the pathology and clinical treatment of RA. Therefore, we selected methotrexate (MTX) as an example of RA drugs to explore the interactions between the gut microbiota and drug responses and obtain an in-depth understanding of their correlation from the perspective of the metabolic capability of gut microbiota on drug metabolism. We identified 2,654 proteins and the corresponding genes involved in MTX metabolism and then profiled their abundances in the gut microbiome datasets of four cohorts. We found that the gut microbiota harbored various genes involved in MTX metabolism in healthy individuals and RA patients. Interestingly, the number of genes involved in MTX metabolism was not significantly different between response (R) and non-response (NR) groups to MTX, but the gene composition in the microbial communities significantly differed between these two groups. Particularly, several models were built based on clinical information, as well as data on the gene, taxonomical, and functional biomarkers by using the random forest algorithm and then validated. Our findings provide bases for clinical management not only of RA but also other gut microbiome–related diseases. First, it suggests that the potential metabolic capability of gut microbiota on drug metabolism is important because they affect drug efficiency; as such, clinical treatment strategies should incorporate the gene compositions of gut microbial communities, in particular genes involved in drug metabolism. Second, a suitable model can be developed to determine hosts’ responses to drugs before clinical treatment.
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Affiliation(s)
- Maozhen Han
- School of Life Sciences, Anhui Medical University, Hefei, China
- Department of Blood Transfusion, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
- *Correspondence: Maozhen Han,
| | - Na Zhang
- School of Life Sciences, Anhui Medical University, Hefei, China
| | - Yujie Mao
- School of Life Sciences, Anhui Medical University, Hefei, China
| | - Bingbing Huang
- Department of Maternal, Child, and Adolescent Health, School of Public Health, Anhui Medical University, Hefei, China
| | - Mengfei Ren
- School of Life Sciences, Anhui Medical University, Hefei, China
| | - Zhangjie Peng
- School of Life Sciences, Anhui Medical University, Hefei, China
| | - Zipeng Bai
- School of Life Sciences, Anhui Medical University, Hefei, China
| | - Long Chen
- School of Life Sciences, Anhui Medical University, Hefei, China
| | - Yan Liu
- School of Life Sciences, Anhui Medical University, Hefei, China
| | - Shanshan Wang
- Department of Blood Transfusion, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shenghai Huang
- School of Life Sciences, Anhui Medical University, Hefei, China
- Department of Microbiology, School of Basic Medical Sciences, Anhui Medical University, Hefei, China
- Shenghai Huang,
| | - Zhixiang Cheng
- Department of Blood Transfusion, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
- Zhixiang Cheng,
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Hsieh YC, Liao JM, Chuang KH, Ho KW, Hong ST, Liu HJ, Huang BC, Chen IJ, Liu YL, Wang JY, Tsai HL, Su YC, Wang YT, Cheng TL. A universal in silico V(D)J recombination strategy for developing humanized monoclonal antibodies. J Nanobiotechnology 2022; 20:58. [PMID: 35101043 PMCID: PMC8805405 DOI: 10.1186/s12951-022-01259-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/12/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Humanization of mouse monoclonal antibodies (mAbs) is crucial for reducing their immunogenicity in humans. However, humanized mAbs often lose their binding affinities. Therefore, an in silico humanization method that can prevent the loss of the binding affinity of mAbs is needed. METHODS We developed an in silico V(D)J recombination platform in which we used V(D)J human germline gene sequences to design five humanized candidates of anti-tumor necrosis factor (TNF)-α mAbs (C1-C5) by using different human germline templates. The candidates were subjected to molecular dynamics simulation. In addition, the structural similarities of their complementarity-determining regions (CDRs) to those of original mouse mAbs were estimated to derive the weighted interatomic root mean squared deviation (wRMSDi) value. Subsequently, the correlation of the derived wRMSDi value with the half maximal effective concentration (EC50) and the binding affinity (KD) of the humanized anti-TNF-α candidates was examined. To confirm whether our in silico estimation method can be used for other humanized mAbs, we tested our method using the anti-epidermal growth factor receptor (EGFR) a4.6.1, anti-glypican-3 (GPC3) YP9.1 and anti-α4β1 integrin HP1/2L mAbs. RESULTS The R2 value for the correlation between the wRMSDi and log(EC50) of the recombinant Remicade and those of the humanized anti-TNF-α candidates was 0.901, and the R2 value for the correlation between wRMSDi and log(KD) was 0.9921. The results indicated that our in silico V(D)J recombination platform could predict the binding affinity of humanized candidates and successfully identify the high-affinity humanized anti-TNF-α antibody (Ab) C1 with a binding affinity similar to that of the parental chimeric mAb (5.13 × 10-10). For the anti-EGFR a4.6.1, anti-GPC3 YP9.1, and anti-α4β1 integrin HP1/2L mAbs, the wRMSDi and log(EC50) exhibited strong correlations (R2 = 0.9908, 0.9999, and 0.8907, respectively). CONCLUSIONS Our in silico V(D)J recombination platform can facilitate the development of humanized mAbs with low immunogenicity and high binding affinities. This platform can directly transform numerous mAbs with therapeutic potential to humanized or even human therapeutic Abs for clinical use.
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Affiliation(s)
- Yuan-Chin Hsieh
- Center for Biomarkers and Biotech Drugs, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan
- School of Medicine for International Students, I-Shou University, No.8, Yida Rd., Jiaosu Village Yanchao District, Kaohsiung, 82445, Taiwan
| | - Jun-Min Liao
- Center for Biomarkers and Biotech Drugs, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan
| | - Kuo-Hsiang Chuang
- Graduate Institute of Pharmacognosy, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan
- Ph.D. Program for Clinical Drug Discovery From Botanical Herbs, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan
| | - Kai-Wen Ho
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan
| | - Shih-Ting Hong
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan
| | - Hui-Ju Liu
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan
| | - Bo-Cheng Huang
- Institute of Biomedical Sciences, National Sun Yat-Sen University, 70 Lien-hai Road, Kaohsiung, 804, Taiwan
| | - I-Ju Chen
- School of Medicine, I-Shou University, No.8, Yida Rd., Jiaosu Village Yanchao District, Kaohsiung, 82445, Taiwan
| | - Yen-Ling Liu
- Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan
| | - Jaw-Yuan Wang
- Center for Biomarkers and Biotech Drugs, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan
- Division of Colorectal Surgery, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan
- Department of Surgery, Faculty of Medicine, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, No.100, Tzyou 1st Rd., Sanmin Dist., Kaohsiung, 80756, Taiwan
- Center for Cancer Research, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan
| | - Hsiang-Lin Tsai
- Division of Colorectal Surgery, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan
- Department of Surgery, Faculty of Medicine, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan
| | - Yu-Cheng Su
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd. East Dist., Hsin-Chu, 300, Taiwan
| | - Yen-Tseng Wang
- Center for Biomarkers and Biotech Drugs, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan.
- School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan.
| | - Tian-Lu Cheng
- Center for Biomarkers and Biotech Drugs, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan.
- Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, 100 Shih-Chuan First Road, Kaohsiung, 80708, Taiwan.
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Abstract
Antibody humanization process converts any nonhuman antibody sequence into humanized antibodies. This can be achieved using different methods of antibody design and engineering. This chapter will primarily focus on antibody design using a homology model followed by framework shuffling of murine to human germline sequence for humanization. Historically, mouse antibodies have been humanized using sequence-based approaches, in which all the murine frameworks are replaced with most homologous human germline sequence or related scaffold. Most often this humanized antibody design, when tested, has a significantly reduced binding or no binding to the cognate antigen. This is due to noncompatibility of mouse CDRs being supported by non-native human framework scaffold. This mismatch between mouse, human structural fold, and elimination of key conformational residues often leads to antibody humanization failures. Recently, there has been advent of homology modelor structure-guided antibody humanization. Instead of humanization based on linear sequence, this approach takes into account the tertiary structure and fold of the mouse antibody. A mouse homology model of the fragment variable is created, and based on sequence alignment with human germline, residues that are different in mouse are replaced with humanized sequence in the model. Energy minimization is applied to this humanized model that also delineates residues which might have steric clashes due to change in the overall tertiary conformation of the humanized antibody. Therefore, a homology model-guided with rational mutations, and reintroduction of key conformational residues from mouse antibody not only eliminates steric clashes but might also restore function in relation to binding affinity to its antigen.
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Affiliation(s)
- Vinodh B Kurella
- Protein Engineering, Immuno-Oncology Division, Intrexon Corporation, Germantown, MD, USA
- Merrimack Pharmaceuticals, Protein Engineering, Cambridge, MA, USA
| | - Reddy Gali
- The Harvard Clinical and Translational Science Center and Countway Library of Medicine, Harvard Medical School, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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Bujotzek A, Lipsmeier F, Harris SF, Benz J, Kuglstatter A, Georges G. VH-VL orientation prediction for antibody humanization candidate selection: A case study. MAbs 2016; 8:288-305. [PMID: 26637054 DOI: 10.1080/19420862.2015.1117720] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
Abstract
Antibody humanization describes the procedure of grafting a non-human antibody's complementarity-determining regions, i.e., the variable loop regions that mediate specific interactions with the antigen, onto a β-sheet framework that is representative of the human variable region germline repertoire, thus reducing the number of potentially antigenic epitopes that might trigger an anti-antibody response. The selection criterion for the so-called acceptor frameworks (one for the heavy and one for the light chain variable region) is traditionally based on sequence similarity. Here, we propose a novel approach that selects acceptor frameworks such that the relative orientation of the 2 variable domains in 3D space, and thereby the geometry of the antigen-binding site, is conserved throughout the process of humanization. The methodology relies on a machine learning-based predictor of antibody variable domain orientation that has recently been shown to improve the quality of antibody homology models. Using data from 3 humanization campaigns, we demonstrate that preselecting humanization variants based on the predicted difference in variable domain orientation with regard to the original antibody leads to subsets of variants with a significant improvement in binding affinity.
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Affiliation(s)
- Alexander Bujotzek
- a Roche Pharmaceutical Research and Early Development, Large Molecule Research, Roche Innovation Center Penzberg , Nonnenwald 2, Penzberg , Germany
| | - Florian Lipsmeier
- b Roche Pharmaceutical Research and Early Development, Informatics, Roche Innovation Center Penzberg , Nonnenwald 2, Penzberg , Germany
| | - Seth F Harris
- c Genentech, Inc., Structural Biology Department , 1 DNA Way, South San Francisco , California 94080 , USA
| | - Jörg Benz
- d Roche Pharmaceutical Research and Early Development, Small Molecule Research, Roche Innovation Center Basel , Grenzacherstrasse 124, Basel , Switzerland
| | - Andreas Kuglstatter
- d Roche Pharmaceutical Research and Early Development, Small Molecule Research, Roche Innovation Center Basel , Grenzacherstrasse 124, Basel , Switzerland
| | - Guy Georges
- a Roche Pharmaceutical Research and Early Development, Large Molecule Research, Roche Innovation Center Penzberg , Nonnenwald 2, Penzberg , Germany
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Noël F, Malpertuy A, de Brevern AG. Global analysis of VHHs framework regions with a structural alphabet. Biochimie 2016; 131:11-19. [PMID: 27613403 DOI: 10.1016/j.biochi.2016.09.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 09/05/2016] [Accepted: 09/05/2016] [Indexed: 02/08/2023]
Abstract
The VHHs are antigen-binding region/domain of camelid heavy chain antibodies (HCAb). They have many interesting biotechnological and biomedical properties due to their small size, high solubility and stability, and high affinity and specificity for their antigens. HCAb and classical IgGs are evolutionary related and share a common fold. VHHs are composed of regions considered as constant, called the frameworks (FRs) connected by Complementarity Determining Regions (CDRs), a highly variable region that provide interaction with the epitope. Actually, no systematic structural analyses had been performed on VHH structures despite a significant number of structures. This work is the first study to analyse the structural diversity of FRs of VHHs. Using a structural alphabet that allows approximating the local conformation, we show that each of the four FRs do not have a unique structure but exhibit many structural variant patterns. Moreover, no direct simple link between the local conformational change and amino acid composition can be detected. These results indicate that long-range interactions affect the local conformation of FRs and impact the building of structural models.
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Affiliation(s)
- Floriane Noël
- INSERM, U 1134, DSIMB, F-75739 Paris, France; Univ Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, F-75739 Paris, France; Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France; Laboratoire d'Excellence GR-Ex, F-75739 Paris, France
| | | | - Alexandre G de Brevern
- INSERM, U 1134, DSIMB, F-75739 Paris, France; Univ Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, F-75739 Paris, France; Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France; Laboratoire d'Excellence GR-Ex, F-75739 Paris, France.
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