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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Gavade A, Nagraj AK, Patel R, Pais R, Dhanure P, Scheele J, Seiz W, Patil J. Understanding the Specific Implications of Amino Acids in the Antibody Development. Protein J 2024; 43:405-424. [PMID: 38724751 DOI: 10.1007/s10930-024-10201-4] [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] [Accepted: 04/21/2024] [Indexed: 06/01/2024]
Abstract
As the demand for immunotherapy to treat and manage cancers, infectious diseases and other disorders grows, a comprehensive understanding of amino acids and their intricate role in antibody engineering has become a prime requirement. Naturally produced antibodies may not have the most suitable amino acids at the complementarity determining regions (CDR) and framework regions, for therapeutic purposes. Therefore, to enhance the binding affinity and therapeutic properties of an antibody, the specific impact of certain amino acids on the antibody's architecture must be thoroughly studied. In antibody engineering, it is crucial to identify the key amino acid residues that significantly contribute to improving antibody properties. Therapeutic antibodies with higher binding affinity and improved functionality can be achieved through modifications or substitutions with highly suitable amino acid residues. Here, we have indicated the frequency of amino acids and their association with the binding free energy in CDRs. The review also analyzes the experimental outcome of two studies that reveal the frequency of amino acids in CDRs and provides their significant correlation between the outcomes. Additionally, it discusses the various bond interactions within the antibody structure and antigen binding. A detailed understanding of these amino acid properties should assist in the analysis of antibody sequences and structures needed for designing and enhancing the overall performance of therapeutic antibodies.
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Affiliation(s)
- Akshata Gavade
- Innoplexus Consulting Services Pvt Ltd, 7Th Floor, Midas Tower, Hinjawadi, Pune, Maharashtra, 411057, India
| | - Anil Kumar Nagraj
- Innoplexus Consulting Services Pvt Ltd, 7Th Floor, Midas Tower, Hinjawadi, Pune, Maharashtra, 411057, India
| | - Riya Patel
- Innoplexus Consulting Services Pvt Ltd, 7Th Floor, Midas Tower, Hinjawadi, Pune, Maharashtra, 411057, India
| | - Roylan Pais
- Innoplexus Consulting Services Pvt Ltd, 7Th Floor, Midas Tower, Hinjawadi, Pune, Maharashtra, 411057, India
| | - Pratiksha Dhanure
- Innoplexus Consulting Services Pvt Ltd, 7Th Floor, Midas Tower, Hinjawadi, Pune, Maharashtra, 411057, India
| | | | | | - Jaspal Patil
- Innoplexus Consulting Services Pvt Ltd, 7Th Floor, Midas Tower, Hinjawadi, Pune, Maharashtra, 411057, India.
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3
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Kausar MA, Bhardwaj T, Alenazi F, Alshammari KF, Anwar S, Ali A, AboElnaga SMH, Najm MZ, Saeed M. A comprehensive immunoinformatics study to explore and characterize potential vaccine constructs against Ole e 9 allergen of Olea europaea. J Biomol Struct Dyn 2024; 42:4644-4655. [PMID: 37340658 DOI: 10.1080/07391102.2023.2224884] [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: 03/18/2023] [Accepted: 05/31/2023] [Indexed: 06/22/2023]
Abstract
Immunoglobulin E (IgE)-mediated allergy, which affects more than 30% of the population, is the most prevalent hypersensitivity illness. In an atopic individual, even a small amount of allergen exposure can cause IgE antibodies to be produced. Due to the engagement of receptors that are highly selective for IgE, even tiny amounts of allergens can induce massive inflammation. This study focuses on the exploration and characterization of the allergen potential of Olea europaea allergen (Ole e 9) affecting the population in Saudi Arabia. A systematic computational approach was performed to identify potential epitopes of allergens and complementary determining regions of IgE. In support, physiochemical characterization and secondary structure analysis unravel the structural conformations of allergens and active sites. Epitope prediction uses a pool of computational algorithms to identify plausible epitopes. Furthermore, the vaccine construct was assessed for its binding efficiency using molecular docking and molecular dynamics simulation studies, which led to strong and stable interactions. This is because IgE is known to play a role in allergic responses, which facilitate host cell activation for an immune response. Overall, the immunoinformatics analysis advocates that the proposed vaccine candidate is safe and immunogenic and therefore can be pushed as a lead for in vitro and in vivo investigations.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Mohd Adnan Kausar
- Department of Biochemistry, College of Medicine, University of Ha'il, Ha'il, Saudi Arabia
| | - Tulika Bhardwaj
- Department of Agricultural, Food and Nutritional Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Fahaad Alenazi
- Department of Pharmacology, College of Medicine, University of Ha'il, Ha'il, Saudi Arabia
| | - Khalid F Alshammari
- Department of Internal Medicine, College of Medicine, University of Ha'il, Ha'il, Saudi Arabia
| | - Sadaf Anwar
- Department of Biochemistry, College of Medicine, University of Ha'il, Ha'il, Saudi Arabia
| | - Abrar Ali
- Department of Pharmacology, College of Medicine, University of Ha'il, Ha'il, Saudi Arabia
| | - Shimaa M H AboElnaga
- Department of Basic Science, Deanship of Preparatory Year, University of Ha'il, Ha'il, Saudi Arabia
| | - Mohammad Z Najm
- School of Biosciences, Apeejay Stya University, Gurugram, India
| | - Mohd Saeed
- Department of Biology, College of Sciences, University of Ha'il, Ha'il, Saudi Arabia
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4
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Li D, Pucci F, Rooman M. Prediction of Paratope-Epitope Pairs Using Convolutional Neural Networks. Int J Mol Sci 2024; 25:5434. [PMID: 38791470 PMCID: PMC11121317 DOI: 10.3390/ijms25105434] [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: 04/02/2024] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
Antibodies play a central role in the adaptive immune response of vertebrates through the specific recognition of exogenous or endogenous antigens. The rational design of antibodies has a wide range of biotechnological and medical applications, such as in disease diagnosis and treatment. However, there are currently no reliable methods for predicting the antibodies that recognize a specific antigen region (or epitope) and, conversely, epitopes that recognize the binding region of a given antibody (or paratope). To fill this gap, we developed ImaPEp, a machine learning-based tool for predicting the binding probability of paratope-epitope pairs, where the epitope and paratope patches were simplified into interacting two-dimensional patches, which were colored according to the values of selected features, and pixelated. The specific recognition of an epitope image by a paratope image was achieved by using a convolutional neural network-based model, which was trained on a set of two-dimensional paratope-epitope images derived from experimental structures of antibody-antigen complexes. Our method achieves good performances in terms of cross-validation with a balanced accuracy of 0.8. Finally, we showcase examples of application of ImaPep, including extensive screening of large libraries to identify paratope candidates that bind to a selected epitope, and rescoring and refining antibody-antigen docking poses.
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Affiliation(s)
- Dong Li
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium; (D.L.); (F.P.)
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium; (D.L.); (F.P.)
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium; (D.L.); (F.P.)
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
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5
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Zhou J, Le CQ, Zhang Y, Wells JA. A general approach for selection of epitope-directed binders to proteins. Proc Natl Acad Sci U S A 2024; 121:e2317307121. [PMID: 38683990 PMCID: PMC11087759 DOI: 10.1073/pnas.2317307121] [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: 10/15/2023] [Accepted: 03/18/2024] [Indexed: 05/02/2024] Open
Abstract
Directing antibodies to a particular epitope among many possible on a target protein is a significant challenge. Here, we present a simple and general method for epitope-directed selection (EDS) using a differential phage selection strategy. This involves engineering the protein of interest (POI) with the epitope of interest (EOI) mutated using a systematic bioinformatics algorithm to guide the local design of an EOI decoy variant. Using several alternating rounds of negative selection with the EOI decoy variant followed by positive selection on the wild-type POI, we were able to identify highly specific and potent antibodies to five different EOI antigens that bind and functionally block known sites of proteolysis. Among these, we developed highly specific antibodies that target the proteolytic site on the CUB domain containing protein 1 (CDCP1) to prevent its proteolysis allowing us to study the cellular maturation of this event that triggers malignancy. We generated antibodies that recognize the junction between the pro- and catalytic domains for three different matrix metalloproteases (MMPs), MMP1, MMP3, and MMP9, that selectively block activation of each of these enzymes and impair cell migration. We targeted a proteolytic epitope on the cell surface receptor, EPH Receptor A2 (EphA2), that is known to transform it from a tumor suppressor to an oncoprotein. We believe that the EDS method greatly facilitates the generation of antibodies to specific EOIs on a wide range of proteins and enzymes for broad therapeutic and diagnostic applications.
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Affiliation(s)
- Jie Zhou
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA94158
| | - Chau Q. Le
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA94158
| | - Yun Zhang
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA94158
| | - James A. Wells
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA94158
- Chan Zuckerberg Biohub, San Francisco, CA94158
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA94158
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6
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Tandiana R, Barletta GP, Soler MA, Fortuna S, Rocchia W. Computational Mutagenesis of Antibody Fragments: Disentangling Side Chains from ΔΔ G Predictions. J Chem Theory Comput 2024; 20:2630-2642. [PMID: 38445482 DOI: 10.1021/acs.jctc.3c01225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
The development of highly potent antibodies and antibody fragments as binding agents holds significant implications in fields such as biosensing and biotherapeutics. Their binding strength is intricately linked to the arrangement and composition of residues at the binding interface. Computational techniques offer a robust means to predict the three-dimensional structure of these complexes and to assess the affinity changes resulting from mutations. Given the interdependence of structure and affinity prediction, our objective here is to disentangle their roles. We aim to evaluate independently six side-chain reconstruction methods and ten binding affinity estimation techniques. This evaluation was pivotal in predicting affinity alterations due to single mutations, a key step in computational affinity maturation protocols. Our analysis focuses on a data set comprising 27 distinct antibody/hen egg white lysozyme complexes, each with crystal structures and experimentally determined binding affinities. Using six different side-chain reconstruction methods, we transformed each structure into its corresponding mutant via in silico single-point mutations. Subsequently, these structures undergo minimization and molecular dynamics simulation. We therefore estimate ΔΔG values based on the original crystal structure, its energy-minimized form, and the ensuing molecular dynamics trajectories. Our research underscores the critical importance of selecting reliable side-chain reconstruction methods and conducting thorough molecular dynamics simulations to accurately predict the impact of mutations. In summary, our study demonstrates that the integration of conformational sampling and scoring is a potent approach to precisely characterizing mutation processes in single-point mutagenesis protocols and crucial for computational antibody design.
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Affiliation(s)
- Rika Tandiana
- Computational MOdelling of NanosCalE and BioPhysical SysTems─CONCEPT Lab Istituto Italiano di Tecnologia (IIT), Via Melen-83, B Block, 16152 Genoa, Italy
| | - German P Barletta
- Computational MOdelling of NanosCalE and BioPhysical SysTems─CONCEPT Lab Istituto Italiano di Tecnologia (IIT), Via Melen-83, B Block, 16152 Genoa, Italy
- The Abdus Salam International Centre for Theoretical Physics─ICTP, Strada Costiera 11, 34151 Trieste, Italy
| | - Miguel Angel Soler
- Dipartimento di Scienze Matematiche, Informatiche e Fisiche, Universita' di Udine, Via delle Scienze 206, 33100 Udine, Italy
| | - Sara Fortuna
- Computational MOdelling of NanosCalE and BioPhysical SysTems─CONCEPT Lab Istituto Italiano di Tecnologia (IIT), Via Melen-83, B Block, 16152 Genoa, Italy
| | - Walter Rocchia
- Computational MOdelling of NanosCalE and BioPhysical SysTems─CONCEPT Lab Istituto Italiano di Tecnologia (IIT), Via Melen-83, B Block, 16152 Genoa, Italy
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7
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Guo D, De Sciscio ML, Chi-Fung Ng J, Fraternali F. Modelling the assembly and flexibility of antibody structures. Curr Opin Struct Biol 2024; 84:102757. [PMID: 38118364 DOI: 10.1016/j.sbi.2023.102757] [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: 10/13/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/22/2023]
Abstract
Antibodies are large protein assemblies capable of both specifically recognising antigens and engaging with other proteins and receptors to coordinate immune action. Traditionally, structural studies have been dedicated to antibody variable regions, but efforts to determine and model full-length antibody structures are emerging. Here we review the current knowledge on modelling the structures of antibody assemblies, focusing on their conformational flexibility and the challenge this poses to obtaining and evaluating structural models. Integrative modelling approaches, combining experiments (cryo-electron microscopy, mass spectrometry, etc.) and computational methods (molecular dynamics simulations, deep-learning based approaches, etc.), hold the promise to map the complex conformational landscape of full-length antibody structures.
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Affiliation(s)
- Dongjun Guo
- Institute of Structural and Molecular Biology, University College London, Darwin Building, Gower Street, London, WC1E 6BT, United Kingdom; Randall Centre for Cell & Molecular Biophysics, King's College London, New Hunt's House, Guy's Campus, London, SE1 1UL, United Kingdom
| | - Maria Laura De Sciscio
- Institute of Structural and Molecular Biology, University College London, Darwin Building, Gower Street, London, WC1E 6BT, United Kingdom; Department of Chemistry, Sapienza University of Rome, P.le A. Moro 5, Rome, 00185, Italy
| | - Joseph Chi-Fung Ng
- Institute of Structural and Molecular Biology, University College London, Darwin Building, Gower Street, London, WC1E 6BT, United Kingdom
| | - Franca Fraternali
- Institute of Structural and Molecular Biology, University College London, Darwin Building, Gower Street, London, WC1E 6BT, United Kingdom.
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8
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Sharma D, Rawat P, Greiff V, Janakiraman V, Gromiha MM. Predicting the immune escape of SARS-CoV-2 neutralizing antibodies upon mutation. Biochim Biophys Acta Mol Basis Dis 2024; 1870:166959. [PMID: 37967796 DOI: 10.1016/j.bbadis.2023.166959] [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: 09/18/2023] [Revised: 10/25/2023] [Accepted: 11/07/2023] [Indexed: 11/17/2023]
Abstract
COVID-19 has resulted in millions of deaths and severe impact on economies worldwide. Moreover, the emergence of SARS-CoV-2 variants presented significant challenges in controlling the pandemic, particularly their potential to avoid the immune system and evade vaccine immunity. This has led to a growing need for research to predict how mutations in SARS-CoV-2 reduces the ability of antibodies to neutralize the virus. In this study, we assembled a set of 1813 mutations from the interface of SARS-CoV-2 spike protein's receptor binding domain (RBD) and neutralizing antibody complexes and developed a machine learning model to classify high or low escape mutations using interaction energy, inter-residue contacts and predicted binding free energy change. Our approach achieved an Area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.91 using the Random Forest classifier on the test dataset with 217 mutations. The model was further utilized to predict the escape mutations on a dataset of 29,165 mutations located at the interface of 83 RBD-neutralizing antibody complexes. A small subset of this dataset was also validated based on available experimental data. We found that top 10 % high escape mutations were dominated by charged to nonpolar mutations whereas low escape mutations were dominated by polar to nonpolar mutations. We believe that the present method will allow prioritization of high/low escape mutations in the context of neutralizing antibodies targeting SARS-CoV-2 RBD region and assist antibody design for current and emerging variants.
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Affiliation(s)
- Divya Sharma
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Puneet Rawat
- University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Victor Greiff
- University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Vani Janakiraman
- Infection Biology Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - M Michael Gromiha
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama 226-8501, Japan; Department of Computer Science, National University of Singapore, Singapore.
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9
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Malinge P, Chauchet X, Bourguignon J, Bosson N, Calloud S, Bautzova T, Borlet M, Laursen M, Kelpsas V, Rose N, Gueneau F, Ravn U, Magistrelli G, Fischer N. Structural analysis of light chain-driven bispecific antibodies targeting CD47 and PD-L1. MAbs 2024; 16:2362432. [PMID: 38849989 PMCID: PMC11164222 DOI: 10.1080/19420862.2024.2362432] [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: 02/16/2024] [Accepted: 05/28/2024] [Indexed: 06/09/2024] Open
Abstract
In contrast to natural antibodies that rely mainly on the heavy chain to establish contacts with their cognate antigen, we have developed a bispecific antibody format in which the light chain (LC) drives antigen binding and specificity. To better understand epitope-paratope interactions in this context, we determined the X-ray crystallographic structures of an antigen binding fragment (Fab) in complex with human CD47 and another Fab in complex with human PD-L1. These Fabs contain a κ-LC and a λ-LC, respectively, which are paired with an identical heavy chain (HC). The structural analysis of these complexes revealed the dominant contribution of the LCs to antigen binding, but also that the common HC provides some contacts in both CD47 and PD-L1 Fab complexes. The anti-CD47 Fab was affinity optimized by diversifying complementary-determining regions of the LC followed by phage display selections. Using homology modeling, the contributions of the amino acid modification to the affinity increase were analyzed. Our results demonstrate that, despite a less prominent role in natural antibodies, the LC can mediate high affinity binding to different antigens and neutralize their biological function. Importantly, Fabs containing a common variable heavy (VH) domain enable the generation of bispecific antibodies retaining a truly native structure, maximizing their therapeutic potential.
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Affiliation(s)
- Pauline Malinge
- Light Chain Bioscience - Novimmune SA, Plan-les-Ouates, Switzerland
| | - Xavier Chauchet
- Light Chain Bioscience - Novimmune SA, Plan-les-Ouates, Switzerland
| | | | - Nicolas Bosson
- Light Chain Bioscience - Novimmune SA, Plan-les-Ouates, Switzerland
| | | | - Tereza Bautzova
- Light Chain Bioscience - Novimmune SA, Plan-les-Ouates, Switzerland
| | - Marie Borlet
- Light Chain Bioscience - Novimmune SA, Plan-les-Ouates, Switzerland
| | | | | | | | - Franck Gueneau
- Light Chain Bioscience - Novimmune SA, Plan-les-Ouates, Switzerland
| | - Ulla Ravn
- Light Chain Bioscience - Novimmune SA, Plan-les-Ouates, Switzerland
| | | | - Nicolas Fischer
- Light Chain Bioscience - Novimmune SA, Plan-les-Ouates, Switzerland
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Gagliardi L, Rocchia W. SiteFerret: Beyond Simple Pocket Identification in Proteins. J Chem Theory Comput 2023; 19:5242-5259. [PMID: 37470784 PMCID: PMC10413863 DOI: 10.1021/acs.jctc.2c01306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Indexed: 07/21/2023]
Abstract
We present a novel method for the automatic detection of pockets on protein molecular surfaces. The algorithm is based on an ad hoc hierarchical clustering of virtual probe spheres obtained from the geometrical primitives used by the NanoShaper software to build the solvent-excluded molecular surface. The final ranking of putative pockets is based on the Isolation Forest method, an unsupervised learning approach originally developed for anomaly detection. A detailed importance analysis of pocket features provides insight into which geometrical (clustering) and chemical (amino acidic composition) properties characterize a good binding site. The method also provides a segmentation of pockets into smaller subpockets. We prove that subpockets are a convenient representation to pinpoint the binding site with great precision. SiteFerret is outstanding in its versatility, accurately predicting a wide range of binding sites, from those binding small molecules to those binding peptides, including difficult shallow sites.
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Affiliation(s)
| | - Walter Rocchia
- CONCEPT Lab, Istituto Italiano di Tecnologia, Via Melen - 83, B Block, 16152 Genova, Italy
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