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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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Abu-Shmais AA, Vukovich MJ, Wasdin PT, Suresh YP, Marinov TM, Rush SA, Gillespie RA, Sankhala RS, Choe M, Joyce MG, Kanekiyo M, McLellan JS, Georgiev IS. Antibody sequence determinants of viral antigen specificity. mBio 2024:e0156024. [PMID: 39264172 DOI: 10.1128/mbio.01560-24] [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: 05/21/2024] [Accepted: 08/13/2024] [Indexed: 09/13/2024] Open
Abstract
Throughout life, humans experience repeated exposure to viral antigens through infection and vaccination, resulting in the generation of diverse, antigen-specific antibody repertoires. A paramount feature of antibodies that enables their critical contributions in counteracting recurrent and novel pathogens, and consequently fostering their utility as valuable targets for therapeutic and vaccine development, is the exquisite specificity displayed against their target antigens. Yet, there is still limited understanding of the determinants of antibody-antigen specificity, particularly as a function of antibody sequence. In recent years, experimental characterization of antibody repertoires has led to novel insights into fundamental properties of antibody sequences but has been largely decoupled from at-scale antigen specificity analysis. Here, using the LIBRA-seq technology, we generated a large data set mapping antibody sequence to antigen specificity for thousands of B cells, by screening the repertoires of a set of healthy individuals against 20 viral antigens representing diverse pathogens of biomedical significance. Analysis uncovered virus-specific patterns in variable gene usage, gene pairing, somatic hypermutation, as well as the presence of convergent antiviral signatures across multiple individuals, including the presence of public antibody clonotypes. Notably, our results showed that, for B-cell receptors originating from different individuals but leveraging an identical combination of heavy and light chain variable genes, there is a specific CDRH3 identity threshold above which B cells appear to exclusively share the same antigen specificity. This finding provides a quantifiable measure of the relationship between antibody sequence and antigen specificity and further defines experimentally grounded criteria for defining public antibody clonality.IMPORTANCEThe B-cell compartment of the humoral immune system plays a critical role in the generation of antibodies upon new and repeated pathogen exposure. This study provides an unprecedented level of detail on the molecular characteristics of antibody repertoires that are specific to each of the different target pathogens studied here and provides empirical evidence in support of a 70% CDRH3 amino acid identity threshold in pairs of B cells encoded by identical IGHV:IGL(K)V genes, as a means of defining public clonality and therefore predicting B-cell antigen specificity in different individuals. This is of exceptional importance when leveraging public clonality as a method to annotate B-cell receptor data otherwise lacking antigen specificity information. Understanding the fundamental rules of antibody-antigen interactions can lead to transformative new approaches for the development of antibody therapeutics and vaccines against current and emerging viruses.
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Affiliation(s)
- Alexandra A Abu-Shmais
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Matthew J Vukovich
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Perry T Wasdin
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Program in Chemical and Physical Biology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Yukthi P Suresh
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Toma M Marinov
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Scott A Rush
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, USA
| | - Rebecca A Gillespie
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Rajeshwer S Sankhala
- Emerging Infectious Disease Branch, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA
| | - Misook Choe
- Emerging Infectious Disease Branch, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA
| | - M Gordon Joyce
- Emerging Infectious Disease Branch, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA
| | - Masaru Kanekiyo
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Jason S McLellan
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, USA
| | - Ivelin S Georgiev
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Program in Chemical and Physical Biology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt Institute for Infection, Immunology and Inflammation, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, USA
- Program in Computational Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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3
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Gomes DEB, Yang B, Vanella R, Nash MA, Bernardi RC. Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions. J Am Chem Soc 2024; 146:23842-23853. [PMID: 39146039 DOI: 10.1021/jacs.4c05869] [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: 08/17/2024]
Abstract
Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure are long-standing goals with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost-intensive. Computational methods have potential to accelerate epitope predictions; however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologues. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis were ineffective in distinguishing between two proposed binding models, parallel and perpendicular. However, our integrated approach, utilizing dynamic network analysis, demonstrated that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols, including cross-linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Conversely, AlphaFold3 failed to predict a structure bound in the perpendicular pose, highlighting the necessity for exploratory research in the search for binding epitopes and challenging the notion that AI-generated protein structures can be accepted without scrutiny. Our research underscores the potential of employing dynamic network analysis to enhance AI-based structure predictions for more accurate identification of protein-protein interaction interfaces.
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Affiliation(s)
- Diego E B Gomes
- Department of Physics, Auburn University, Auburn, Alabama 36849, United States
| | - Byeongseon Yang
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, Basel 4058, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Rosario Vanella
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, Basel 4058, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Michael A Nash
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, Basel 4058, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Rafael C Bernardi
- Department of Physics, Auburn University, Auburn, Alabama 36849, United States
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4
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Mitchell CL, Kurouski D. Novel strategies in Parkinson's disease treatment: a review. Front Mol Neurosci 2024; 17:1431079. [PMID: 39183754 PMCID: PMC11341544 DOI: 10.3389/fnmol.2024.1431079] [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: 05/11/2024] [Accepted: 07/29/2024] [Indexed: 08/27/2024] Open
Abstract
An unprecedented extension of life expectancy observed during the past century drastically increased the number of patients diagnosed with Parkinson's diseases (PD) worldwide. Estimated costs of PD alone reached $52 billion per year, making effective neuroprotective treatments an urgent and unmet need. Current treatments of both AD and PD focus on mitigating the symptoms associated with these pathologies and are not neuroprotective. In this review, we discuss the most advanced therapeutic strategies that can be used to treat PD. We also critically review the shift of the therapeutic paradigm from a small molecule-based inhibition of protein aggregation to the utilization of natural degradation pathways and immune cells that are capable of degrading toxic amyloid deposits in the brain of PD patients.
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Affiliation(s)
- Charles L. Mitchell
- Interdisciplinary Program in Genetics and Genomics, Texas A&M University, College Station, TX, United States
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
| | - Dmitry Kurouski
- Interdisciplinary Program in Genetics and Genomics, Texas A&M University, College Station, TX, United States
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
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5
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Nasaev SS, Mukanov AR, Mishkorez IV, Kuznetsov II, Leibin IV, Dolgusheva VA, Pavlyuk GA, Manasyan AL, Veselovsky AV. Molecular Modeling Methods in the Development of Affine and Specific Protein-Binding Agents. BIOCHEMISTRY. BIOKHIMIIA 2024; 89:1451-1473. [PMID: 39245455 DOI: 10.1134/s0006297924080066] [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: 04/12/2024] [Revised: 06/12/2024] [Accepted: 07/11/2024] [Indexed: 09/10/2024]
Abstract
High-affinity and specific agents are widely applied in various areas, including diagnostics, scientific research, and disease therapy (as drugs and drug delivery systems). It takes significant time to develop them. For this reason, development of high-affinity agents extensively utilizes computer methods at various stages for the analysis and modeling of these molecules. The review describes the main affinity and specific agents, such as monoclonal antibodies and their fragments, antibody mimetics, aptamers, and molecularly imprinted polymers. The methods of their obtaining as well as their main advantages and disadvantages are briefly described, with special attention focused on the molecular modeling methods used for their analysis and development.
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Affiliation(s)
| | - Artem R Mukanov
- Research & Development Department, Xelari Ltd., Moscow, 121601, Russia
| | - Ivan V Mishkorez
- Research & Development Department, Xelari Ltd., Moscow, 121601, Russia
- Institute of Biomedical Chemistry, Moscow, 119121, Russia
| | - Ivan I Kuznetsov
- Research & Development Department, Xelari Ltd., Moscow, 121601, Russia
| | - Iosif V Leibin
- Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Moscow, 121205, Russia
| | | | - Gleb A Pavlyuk
- Research & Development Department, Xelari Ltd., Moscow, 121601, Russia
| | - Artem L Manasyan
- Research & Development Department, Xelari Ltd., Moscow, 121601, Russia
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6
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Bashour H, Smorodina E, Pariset M, Zhong J, Akbar R, Chernigovskaya M, Lê Quý K, Snapkow I, Rawat P, Krawczyk K, Sandve GK, Gutierrez-Marcos J, Gutierrez DNZ, Andersen JT, Greiff V. Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability. Commun Biol 2024; 7:922. [PMID: 39085379 PMCID: PMC11291509 DOI: 10.1038/s42003-024-06561-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 07/05/2024] [Indexed: 08/02/2024] Open
Abstract
Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as "developability", which reflects an antibody's ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We find lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. We show that sequence DPs are more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies localize within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.
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Affiliation(s)
- Habib Bashour
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
- School of Life Sciences, University of Warwick, Coventry, UK.
| | - Eva Smorodina
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | - Jahn Zhong
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Division of Genetics, Department Biology, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Rahmad Akbar
- 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
| | - Khang Lê Quý
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Igor Snapkow
- Department of Chemical Toxicology, Norwegian Institute of Public Health, Oslo, Norway
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | | | | | | | - Jan Terje Andersen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Pharmacology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Precision Immunotherapy Alliance (PRIMA), University of Oslo, Oslo, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
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7
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Zhang H, Lan J, Wang H, Lu R, Zhang N, He X, Yang J, Chen L. AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease. Front Mol Biosci 2024; 11:1414916. [PMID: 39139810 PMCID: PMC11319189 DOI: 10.3389/fmolb.2024.1414916] [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: 04/09/2024] [Accepted: 07/15/2024] [Indexed: 08/15/2024] Open
Abstract
Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind's AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology.
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Affiliation(s)
- Hong Zhang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Jiajing Lan
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Huijie Wang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Ruijie Lu
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Nanqi Zhang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Xiaobai He
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
- Key Laboratory of Biomarkers and In Vitro Diagnosis Translation of Zhejiang Province, Hangzhou, China
| | - Jun Yang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Linjie Chen
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
- Zhejiang Engineering Research Centre for Key Technology of Diagnostic Testing, Hangzhou, China
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8
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Liang T, Sun ZY, Hines MG, Penrose KJ, Hao Y, Chu X, Mellors JW, Dimitrov DS, Xie XQ, Li W, Feng Z. AI-based IsAb2.0 for antibody design. Brief Bioinform 2024; 25:bbae445. [PMID: 39285513 PMCID: PMC11405125 DOI: 10.1093/bib/bbae445] [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: 05/31/2024] [Revised: 08/06/2024] [Accepted: 08/27/2024] [Indexed: 09/22/2024] Open
Abstract
Therapeutic antibody design has garnered widespread attention, highlighting its interdisciplinary importance. Advancements in technology emphasize the critical role of designing nanobodies and humanized antibodies in antibody engineering. However, current experimental methods are costly and time-consuming. Computational approaches, while progressing, faced limitations due to insufficient structural data and the absence of a standardized protocol. To tackle these challenges, our lab previously developed IsAb1.0, an in silico antibody design protocol. Yet, IsAb1.0 lacked accuracy, had a complex procedure, and required extensive antibody bioinformation. Moreover, it overlooked nanobody and humanized antibody design, hindering therapeutic antibody development. Building upon IsAb1.0, we enhanced our design protocol with artificial intelligence methods to create IsAb2.0. IsAb2.0 utilized AlphaFold-Multimer (2.3/3.0) for accurate modeling and complex construction without templates and employed the precise FlexddG method for in silico antibody optimization. Validated through optimization of a humanized nanobody J3 (HuJ3) targeting HIV-1 gp120, IsAb2.0 predicted five mutations that can improve HuJ3-gp120 binding affinity. These predictions were confirmed by commercial software and validated through binding and neutralization assays. IsAb2.0 streamlined antibody design, offering insights into future techniques to accelerate immunotherapy development.
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Affiliation(s)
- Tianjian Liang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Drug Discovery Institute, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Department of Computational Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15261, United States
- Department of Structural Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15261, United States
| | - Ze-Yu Sun
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Drug Discovery Institute, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Department of Computational Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15261, United States
- Department of Structural Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15261, United States
| | - Margaret G Hines
- Division of Infectious Diseases, Department of Medicine, Center for Antibody Therapeutics, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - Kerri Jo Penrose
- Division of Infectious Diseases, Department of Medicine, Center for AIDS Research, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - Yixuan Hao
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Drug Discovery Institute, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Department of Computational Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15261, United States
- Department of Structural Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15261, United States
| | - Xiaojie Chu
- Division of Infectious Diseases, Department of Medicine, Center for Antibody Therapeutics, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - John W Mellors
- Division of Infectious Diseases, Department of Medicine, Center for Antibody Therapeutics, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
- Division of Infectious Diseases, Department of Medicine, Center for AIDS Research, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - Dimiter S Dimitrov
- Division of Infectious Diseases, Department of Medicine, Center for Antibody Therapeutics, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Drug Discovery Institute, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Department of Computational Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15261, United States
- Department of Structural Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15261, United States
| | - Wei Li
- Division of Infectious Diseases, Department of Medicine, Center for Antibody Therapeutics, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - Zhiwei Feng
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Drug Discovery Institute, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Department of Computational Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15261, United States
- Department of Structural Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15261, United States
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9
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Foglierini M, Nortier P, Schelling R, Winiger RR, Jacquet P, O'Dell S, Demurtas D, Mpina M, Lweno O, Muller YD, Petrovas C, Daubenberger C, Perreau M, Doria-Rose NA, Gottardo R, Perez L. RAIN: machine learning-based identification for HIV-1 bNAbs. Nat Commun 2024; 15:5339. [PMID: 38914562 PMCID: PMC11196741 DOI: 10.1038/s41467-024-49676-1] [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: 02/29/2024] [Accepted: 06/17/2024] [Indexed: 06/26/2024] Open
Abstract
Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infections. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoires is still lacking. Here, we develop a straightforward computational method for the Rapid Automatic Identification of bNAbs (RAIN) based on machine learning methods. In contrast to other approaches, which use one-hot encoding amino acid sequences or structural alignment for prediction, RAIN uses a combination of selected sequence-based features for the accurate prediction of HIV-1 bNAbs. We demonstrate the performance of our approach on non-biased, experimentally obtained and sequenced BCR repertoires from HIV-1 immune donors. RAIN processing leads to the successful identification of distinct HIV-1 bNAbs targeting the CD4-binding site of the envelope glycoprotein. In addition, we validate the identified bNAbs using an in vitro neutralization assay and we solve the structure of one of them in complex with the soluble native-like heterotrimeric envelope glycoprotein by single-particle cryo-electron microscopy (cryo-EM). Overall, we propose a method to facilitate and accelerate HIV-1 bNAbs discovery from non-selected immune repertoires.
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Affiliation(s)
- Mathilde Foglierini
- Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Centre for Human Immunology, Lausanne, Switzerland
- Biomedical Data Science Centre, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Pauline Nortier
- Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Centre for Human Immunology, Lausanne, Switzerland
| | - Rachel Schelling
- Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Centre for Human Immunology, Lausanne, Switzerland
| | - Rahel R Winiger
- Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Centre for Human Immunology, Lausanne, Switzerland
| | - Philippe Jacquet
- Scientific Computing and Research Support Unit, University of Lausanne, Lausanne, Switzerland
| | - Sijy O'Dell
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Davide Demurtas
- Interdisciplinary center of electron microscopy, CIME, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | - Omar Lweno
- Ifakara Health Institute, Bagamoyo, United Republic of Tanzania
| | - Yannick D Muller
- Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Centre for Human Immunology, Lausanne, Switzerland
| | - Constantinos Petrovas
- Department of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital, Lausanne, Switzerland
| | - Claudia Daubenberger
- Department of Medical Parasitology and Infection Biology, Clinical Immunology Unit, Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Matthieu Perreau
- Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Nicole A Doria-Rose
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Raphael Gottardo
- Biomedical Data Science Centre, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Laurent Perez
- Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Centre for Human Immunology, Lausanne, Switzerland.
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10
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Mahomed S. Broadly neutralizing antibodies for HIV prevention: a comprehensive review and future perspectives. Clin Microbiol Rev 2024; 37:e0015222. [PMID: 38687039 PMCID: PMC11324036 DOI: 10.1128/cmr.00152-22] [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: 05/02/2024] Open
Abstract
SUMMARYThe human immunodeficiency virus (HIV) epidemic remains a formidable global health concern, with 39 million people living with the virus and 1.3 million new infections reported in 2022. Despite anti-retroviral therapy's effectiveness in pre-exposure prophylaxis, its global adoption is limited. Broadly neutralizing antibodies (bNAbs) offer an alternative strategy for HIV prevention through passive immunization. Historically, passive immunization has been efficacious in the treatment of various diseases ranging from oncology to infectious diseases. Early clinical trials suggest bNAbs are safe, tolerable, and capable of reducing HIV RNA levels. Although challenges such as bNAb resistance have been noted in phase I trials, ongoing research aims to assess the additive or synergistic benefits of combining multiple bNAbs. Researchers are exploring bispecific and trispecific antibodies, and fragment crystallizable region modifications to augment antibody efficacy and half-life. Moreover, the potential of other antibody isotypes like IgG3 and IgA is under investigation. While promising, the application of bNAbs faces economic and logistical barriers. High manufacturing costs, particularly in resource-limited settings, and logistical challenges like cold-chain requirements pose obstacles. Preliminary studies suggest cost-effectiveness, although this is contingent on various factors like efficacy and distribution. Technological advancements and strategic partnerships may mitigate some challenges, but issues like molecular aggregation remain. The World Health Organization has provided preferred product characteristics for bNAbs, focusing on optimizing their efficacy, safety, and accessibility. The integration of bNAbs in HIV prophylaxis necessitates a multi-faceted approach, considering economic, logistical, and scientific variables. This review comprehensively covers the historical context, current advancements, and future avenues of bNAbs in HIV prevention.
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Affiliation(s)
- Sharana Mahomed
- Centre for the AIDS
Programme of Research in South Africa (CAPRISA), Doris Duke Medical
Research Institute, Nelson R Mandela School of Medicine, University of
KwaZulu-Natal, Durban,
South Africa
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11
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Cheng J, Liang T, Xie XQ, Feng Z, Meng L. A new era of antibody discovery: an in-depth review of AI-driven approaches. Drug Discov Today 2024; 29:103984. [PMID: 38642702 DOI: 10.1016/j.drudis.2024.103984] [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: 10/12/2023] [Revised: 04/02/2024] [Accepted: 04/15/2024] [Indexed: 04/22/2024]
Abstract
Given their high affinity and specificity for a range of macromolecules, antibodies are widely used in the treatment of autoimmune diseases, cancers, inflammatory diseases, and Alzheimer's disease (AD). Traditional experimental methods are time-consuming, expensive, and labor-intensive. Recent advances in artificial intelligence (AI) technologies provide complementary methods that can reduce the time and costs required for antibody design by minimizing failures and increasing the success rate of experimental tests. In this review, we scrutinize the plethora of AI-driven methodologies that have been deployed over the past 4 years for modeling antibody structures, predicting antibody-antigen interactions, optimizing antibody affinity, and generating novel antibody candidates. We also briefly address the challenges faced in integrating AI-based models with traditional antibody discovery pipelines and highlight the potential future directions in this burgeoning field.
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Affiliation(s)
- Jin Cheng
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng, 224005, China
| | - Tianjian Liang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA.
| | - Zhiwei Feng
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.
| | - Li Meng
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng, 224005, China.
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12
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Joubbi S, Micheli A, Milazzo P, Maccari G, Ciano G, Cardamone D, Medini D. Antibody design using deep learning: from sequence and structure design to affinity maturation. Brief Bioinform 2024; 25:bbae307. [PMID: 38960409 PMCID: PMC11221890 DOI: 10.1093/bib/bbae307] [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: 03/03/2024] [Revised: 05/20/2024] [Accepted: 06/12/2024] [Indexed: 07/05/2024] Open
Abstract
Deep learning has achieved impressive results in various fields such as computer vision and natural language processing, making it a powerful tool in biology. Its applications now encompass cellular image classification, genomic studies and drug discovery. While drug development traditionally focused deep learning applications on small molecules, recent innovations have incorporated it in the discovery and development of biological molecules, particularly antibodies. Researchers have devised novel techniques to streamline antibody development, combining in vitro and in silico methods. In particular, computational power expedites lead candidate generation, scaling and potential antibody development against complex antigens. This survey highlights significant advancements in protein design and optimization, specifically focusing on antibodies. This includes various aspects such as design, folding, antibody-antigen interactions docking and affinity maturation.
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Affiliation(s)
- Sara Joubbi
- Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, 56127, Pisa, Italy
- Data Science for Health (DaScH) Lab, Fondazione Toscana Life Sciences, Via Fiorentina, 1, 53100, Siena, Italy
| | - Alessio Micheli
- Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, 56127, Pisa, Italy
| | - Paolo Milazzo
- Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, 56127, Pisa, Italy
| | - Giuseppe Maccari
- Data Science for Health (DaScH) Lab, Fondazione Toscana Life Sciences, Via Fiorentina, 1, 53100, Siena, Italy
| | - Giorgio Ciano
- Data Science for Health (DaScH) Lab, Fondazione Toscana Life Sciences, Via Fiorentina, 1, 53100, Siena, Italy
| | - Dario Cardamone
- Data Science for Health (DaScH) Lab, Fondazione Toscana Life Sciences, Via Fiorentina, 1, 53100, Siena, Italy
| | - Duccio Medini
- Data Science for Health (DaScH) Lab, Fondazione Toscana Life Sciences, Via Fiorentina, 1, 53100, Siena, Italy
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13
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Tang X, Dai H, Knight E, Wu F, Li Y, Li T, Gerstein M. A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation. Brief Bioinform 2024; 25:bbae338. [PMID: 39007594 PMCID: PMC11247410 DOI: 10.1093/bib/bbae338] [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: 03/10/2024] [Revised: 05/21/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024] Open
Abstract
Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://github.com/gersteinlab/GenAI4Drug.
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Affiliation(s)
- Xiangru Tang
- Department of Computer Science, Yale University, New Haven, CT 06520, United States
| | - Howard Dai
- Department of Computer Science, Yale University, New Haven, CT 06520, United States
| | - Elizabeth Knight
- School of Medicine, Yale University, New Haven, CT 06520, United States
| | - Fang Wu
- Computer Science Department, Stanford University, CA 94305, United States
| | - Yunyang Li
- Department of Computer Science, Yale University, New Haven, CT 06520, United States
| | - Tianxiao Li
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT 06520, United States
| | - Mark Gerstein
- Department of Computer Science, Yale University, New Haven, CT 06520, United States
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT 06520, United States
- Department of Statistics & Data Science, Yale University, New Haven, CT 06520, United States
- Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT 06520, United States
- Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, CT 06520, United States
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14
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Petersen BM, Kirby MB, Chrispens KM, Irvin OM, Strawn IK, Haas CM, Walker AM, Baumer ZT, Ulmer SA, Ayala E, Rhodes ER, Guthmiller JJ, Steiner PJ, Whitehead TA. An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries. Nat Commun 2024; 15:3974. [PMID: 38730230 PMCID: PMC11087541 DOI: 10.1038/s41467-024-48072-z] [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: 09/29/2023] [Accepted: 04/19/2024] [Indexed: 05/12/2024] Open
Abstract
Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.
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Affiliation(s)
- Brian M Petersen
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Monica B Kirby
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Karson M Chrispens
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Olivia M Irvin
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Isabell K Strawn
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Cyrus M Haas
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Alexis M Walker
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Zachary T Baumer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Sophia A Ulmer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Edgardo Ayala
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Emily R Rhodes
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Jenna J Guthmiller
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Paul J Steiner
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA.
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15
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DeRoo J, Terry JS, Zhao N, Stasevich TJ, Snow CD, Geiss BJ. PAbFold: Linear Antibody Epitope Prediction using AlphaFold2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.19.590298. [PMID: 38659833 PMCID: PMC11042291 DOI: 10.1101/2024.04.19.590298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Defining the binding epitopes of antibodies is essential for understanding how they bind to their antigens and perform their molecular functions. However, while determining linear epitopes of monoclonal antibodies can be accomplished utilizing well-established empirical procedures, these approaches are generally labor- and time-intensive and costly. To take advantage of the recent advances in protein structure prediction algorithms available to the scientific community, we developed a calculation pipeline based on the localColabFold implementation of AlphaFold2 that can predict linear antibody epitopes by predicting the structure of the complex between antibody heavy and light chains and target peptide sequences derived from antigens. We found that this AlphaFold2 pipeline, which we call PAbFold, was able to accurately flag known epitope sequences for several well-known antibody targets (HA / Myc) when the target sequence was broken into small overlapping linear peptides and antibody complementarity determining regions (CDRs) were grafted onto several different antibody framework regions in the single-chain antibody fragment (scFv) format. To determine if this pipeline was able to identify the epitope of a novel antibody with no structural information publicly available, we determined the epitope of a novel anti-SARS-CoV-2 nucleocapsid targeted antibody using our method and then experimentally validated our computational results using peptide competition ELISA assays. These results indicate that the AlphaFold2-based PAbFold pipeline we developed is capable of accurately identifying linear antibody epitopes in a short time using just antibody and target protein sequences. This emergent capability of the method is sensitive to methodological details such as peptide length, AlphaFold2 neural network versions, and multiple-sequence alignment database. PAbFold is available at https://github.com/jbderoo/PAbFold.
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Affiliation(s)
- Jacob DeRoo
- School of Biomedical Engineering, Colorado State University, Fort Collins CO USA
| | - James S. Terry
- Department of Microbiology, Immunology, & Pathology, Colorado State University, Fort Collins CO USA
| | - Ning Zhao
- Department of Biochemistry and Molecular Genetics, University of Colorado-Anschutz Medical Campus, Aurora, CO USA
| | - Timothy J. Stasevich
- Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins CO USA
| | - Christopher D. Snow
- School of Biomedical Engineering, Colorado State University, Fort Collins CO USA
- Department of Chemical & Biological Engineering, Colorado State University, Fort Collins CO USA
| | - Brian J. Geiss
- School of Biomedical Engineering, Colorado State University, Fort Collins CO USA
- Department of Microbiology, Immunology, & Pathology, Colorado State University, Fort Collins CO USA
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16
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Perez L, Foglierini M. RAIN: a Machine Learning-based identification for HIV-1 bNAbs. RESEARCH SQUARE 2024:rs.3.rs-4023897. [PMID: 38903123 PMCID: PMC11188109 DOI: 10.21203/rs.3.rs-4023897/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infection. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoire is still lacking. Here, we developed a straightforward computational method for Rapid Automatic Identification of bNAbs (RAIN) based on Machine Learning methods. In contrast to other approaches using one-hot encoding amino acid sequences or structural alignment for prediction, RAIN uses a combination of selected sequence-based features for accurate prediction of HIV-1 bNAbs. We demonstrate the performance of our approach on non-biased, experimentally obtained sequenced BCR repertoires from HIV-1 immune donors. RAIN processing leads to the successful identification of novel HIV-1 bNAbs targeting the CD4-binding site of the envelope glycoprotein. In addition, we validate the identified bNAbs using in vitro neutralization assay and we solve the structure of one of them in complex with the soluble native-like heterotrimeric envelope glycoprotein by single-particle cryo-electron microscopy (cryo-EM). Overall, we propose a method to facilitate and accelerate HIV-1 bNAbs discovery from non-selected immune repertoires.
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Affiliation(s)
- Laurent Perez
- Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Mathilde Foglierini
- Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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17
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Ozawa T, Ikeda Y, Chen L, Suzuki R, Hoshino A, Noguchi A, Kita S, Anraku Y, Igarashi E, Saga Y, Inasaki N, Taminishi S, Sasaki J, Kirita Y, Fukuhara H, Maenaka K, Hashiguchi T, Fukuhara T, Hirabayashi K, Tani H, Kishi H, Niimi H. Rational in silico design identifies two mutations that restore UT28K SARS-CoV-2 monoclonal antibody activity against Omicron BA.1. Structure 2024; 32:263-272.e7. [PMID: 38228146 DOI: 10.1016/j.str.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/27/2023] [Accepted: 12/20/2023] [Indexed: 01/18/2024]
Abstract
SARS-CoV-2 rapidly mutates and acquires resistance to neutralizing antibodies. We report an in-silico-designed antibody that restores the neutralizing activity of a neutralizing antibody. Our previously generated antibody, UT28K, exhibited broad neutralizing activity against mutant variants; however, its efficacy against Omicron BA.1 was compromised by the mutation. Using previously determined structural information, we designed a modified-UT28K (VH T28R/N57D), UT28K-RD targeting the mutation site. In vitro and in vivo experiments demonstrated the efficacy of UT28K-RD in neutralizing Omicron BA.1. Although the experimentally determined structure partially differed from the predicted model, our study serves as a successful case of antibody design, wherein the predicted amino acid substitution enhanced the recognition of the previously elusive Omicron BA.1. We anticipate that numerous similar cases will be reported, showcasing the potential of this approach for improving protein-protein interactions. Our findings will contribute to the development of novel therapeutic strategies for highly mutable viruses, such as SARS-CoV-2.
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Affiliation(s)
- Tatsuhiko Ozawa
- Department of Immunology, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama, Japan; Center for Advanced Antibody Drug Development, University of Toyama, Toyama, Japan.
| | - Yoshiki Ikeda
- Institute for Integrated Cell-Material Sciences, Kyoto University, Yoshidahonnmachi, Sakyo-ku, Kyoto, Japan.
| | - Liuan Chen
- Laboratory of Biomolecular Science, Faculty of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan
| | - Rigel Suzuki
- Department of Microbiology and Immunology, Faculty of Medicine, Hokkaido University, Sapporo, Japan; Institute for Vaccine Research and Development (HU-IVReD), Hokkaido University, Sapporo, Japan
| | - Atsushi Hoshino
- Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Akira Noguchi
- Department of Diagnostic Pathology, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama, Japan
| | - Shunsuke Kita
- Laboratory of Biomolecular Science, Faculty of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan
| | - Yuki Anraku
- Laboratory of Biomolecular Science, Faculty of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan
| | - Emiko Igarashi
- Department of Virology, Toyama Institute of Health, Toyama, Japan
| | - Yumiko Saga
- Department of Virology, Toyama Institute of Health, Toyama, Japan
| | - Noriko Inasaki
- Department of Virology, Toyama Institute of Health, Toyama, Japan
| | - Shunta Taminishi
- Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Jiei Sasaki
- Laboratory of Medical Virology, Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan
| | - Yuhei Kirita
- Department of Nephrology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Hideo Fukuhara
- Laboratory of Biomolecular Science, Faculty of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan; Institute for Vaccine Research and Development (HU-IVReD), Hokkaido University, Sapporo, Japan; Center for Research and Education on Drug Discovery, Faculty of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan; Division of Pathogen Structure, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Katsumi Maenaka
- Laboratory of Biomolecular Science, Faculty of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan; Institute for Vaccine Research and Development (HU-IVReD), Hokkaido University, Sapporo, Japan; Center for Research and Education on Drug Discovery, Faculty of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan; Division of Pathogen Structure, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan; Global Station for Biosurfaces and Drug Discovery, Hokkaido University, Sapporo, Japan
| | - Takao Hashiguchi
- Laboratory of Medical Virology, Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan
| | - Takasuke Fukuhara
- Department of Microbiology and Immunology, Faculty of Medicine, Hokkaido University, Sapporo, Japan; Institute for Vaccine Research and Development (HU-IVReD), Hokkaido University, Sapporo, Japan; Laboratory of Virus Control, Research Institute for Microbial Diseases, Osaka University, Suita, Japan; AMED-CREST, Japan Agency for Medical Research and Development (AMED), Tokyo, Japan
| | - Kenichi Hirabayashi
- Department of Diagnostic Pathology, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama, Japan
| | - Hideki Tani
- Department of Virology, Toyama Institute of Health, Toyama, Japan
| | - Hiroyuki Kishi
- Department of Immunology, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama, Japan; Center for Advanced Antibody Drug Development, University of Toyama, Toyama, Japan
| | - Hideki Niimi
- Center for Advanced Antibody Drug Development, University of Toyama, Toyama, Japan; Department of Clinical Laboratory and Molecular Pathology, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama, Japan
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18
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Cebi E, Lee J, Subramani VK, Bak N, Oh C, Kim KK. Cryo-electron microscopy-based drug design. Front Mol Biosci 2024; 11:1342179. [PMID: 38501110 PMCID: PMC10945328 DOI: 10.3389/fmolb.2024.1342179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/31/2024] [Indexed: 03/20/2024] Open
Abstract
Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.
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Affiliation(s)
| | | | | | | | - Changsuk Oh
- Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
| | - Kyeong Kyu Kim
- Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
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19
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Barton J, Gaspariunas A, Galson JD, Leem J. Building Representation Learning Models for Antibody Comprehension. Cold Spring Harb Perspect Biol 2024; 16:a041462. [PMID: 38012013 PMCID: PMC10910360 DOI: 10.1101/cshperspect.a041462] [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: 11/29/2023]
Abstract
Antibodies are versatile proteins with both the capacity to bind a broad range of targets and a proven track record as some of the most successful therapeutics. However, the development of novel antibody therapeutics is a lengthy and costly process. It is challenging to predict the functional and biophysical properties of antibodies from their amino acid sequence alone, requiring numerous experiments for full characterization. Machine learning, specifically deep representation learning, has emerged as a family of methods that can complement wet lab approaches and accelerate the overall discovery and engineering process. Here, we review advances in antibody sequence representation learning, and how this has improved antibody structure prediction and facilitated antibody optimization. We discuss challenges in the development and implementation of such models, such as the lack of publicly available, well-curated antibody function data and highlight opportunities for improvement. These and future advances in machine learning for antibody sequences have the potential to increase the success rate in developing new therapeutics, resulting in broader access to transformative medicines and improved patient outcomes.
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Affiliation(s)
- Justin Barton
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
| | | | - Jacob D Galson
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
| | - Jinwoo Leem
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
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20
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Gomes DEB, Yang B, Vanella R, Nash MA, Bernardi RC. Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.08.579577. [PMID: 38370725 PMCID: PMC10871313 DOI: 10.1101/2024.02.08.579577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure is a long standing goal with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost intensive. Computational methods have potential to accelerate epitope predictions, however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologs. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2 Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis alone each proved ineffectual in differentiating between two putative binding models referred to as parallel and perpendicular. However, our integrated approach based on dynamic network analysis showed that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols including cross linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Our research highlights the potential of deploying dynamic network analysis to refine AI-based structure predictions for precise predictions of protein-protein interaction interfaces.
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21
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Petersen BM, Kirby MB, Chrispens KM, Irvin OM, Strawn IK, Haas CM, Walker AM, Baumer ZT, Ulmer SA, Ayala E, Rhodes ER, Guthmiller JJ, Steiner PJ, Whitehead TA. An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.16.575852. [PMID: 38293170 PMCID: PMC10827193 DOI: 10.1101/2024.01.16.575852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of ten different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.
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Affiliation(s)
- Brian M. Petersen
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Monica B. Kirby
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Karson M. Chrispens
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Olivia M. Irvin
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Isabell K. Strawn
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Cyrus M. Haas
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Alexis M. Walker
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Zachary T. Baumer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Sophia A. Ulmer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Edgardo Ayala
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045
| | - Emily R. Rhodes
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Jenna J. Guthmiller
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045
| | - Paul J. Steiner
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Timothy A. Whitehead
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
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22
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Liu C, Denzler LM, Hood OE, Martin AC. Do antibody CDR loops change conformation upon binding? MAbs 2024; 16:2322533. [PMID: 38477253 PMCID: PMC10939163 DOI: 10.1080/19420862.2024.2322533] [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: 11/10/2023] [Accepted: 02/20/2024] [Indexed: 03/14/2024] Open
Abstract
Antibodies have increasingly been developed as drugs with over 100 now licensed in the US or EU. During development, it is often necessary to increase or reduce the affinity of an antibody and rational attempts to do so rely on having a structure of the antibody-antigen complex often obtained by modeling. The antigen-binding site consists primarily of six loops known as complementarity-determining regions (CDRs), and an open question has been whether these loops change their conformation when they bind to an antigen. Existing surveys of antibody-antigen complex structures have only examined CDR conformational change in case studies or small-scale surveys. With an increasing number of antibodies where both free and complexed structures have been deposited in the Protein Data Bank, a large-scale survey of CDR conformational change during binding is now possible. To this end, we built a dataset, AbAgDb, that currently includes 177 antibodies with high-quality CDRs, each of which has at least one bound and one unbound structure. We analyzed the conformational change of the Cα backbone of each CDR upon binding and found that, in most cases, the CDRs (other than CDR-H3) show minimal movement, while 70.6% and 87% of CDR-H3s showed global Cα RMSD ≤ 1.0Å and ≤ 2.0Å, respectively. We also compared bound CDR conformations with the conformational space of unbound CDRs and found most of the bound conformations are included in the unbound conformational space. In future, our results will contribute to developing insights into antibodies and new methods for modeling and docking.
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Affiliation(s)
- Chu’nan Liu
- Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
| | - Lilian M. Denzler
- Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
| | - Oliver E.C. Hood
- Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
| | - Andrew C.R. Martin
- Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
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23
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Mullin M, McClory J, Haynes W, Grace J, Robertson N, van Heeke G. Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions. MAbs 2024; 16:2341443. [PMID: 38666503 PMCID: PMC11057648 DOI: 10.1080/19420862.2024.2341443] [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: 09/22/2023] [Accepted: 04/05/2024] [Indexed: 05/01/2024] Open
Abstract
The development of bispecific antibodies that bind at least two different targets relies on bringing together multiple binding domains with different binding properties and biophysical characteristics to produce a drug-like therapeutic. These building blocks play an important role in the overall quality of the molecule and can influence many important aspects from potency and specificity to stability and half-life. Single-domain antibodies, particularly camelid-derived variable heavy domain of heavy chain (VHH) antibodies, are becoming an increasingly popular choice for bispecific construction due to their single-domain modularity, favorable biophysical properties, and potential to work in multiple antibody formats. Here, we review the use of VHH domains as building blocks in the construction of multispecific antibodies and the challenges in creating optimized molecules. In addition to exploring traditional approaches to VHH development, we review the integration of machine learning techniques at various stages of the process. Specifically, the utilization of machine learning for structural prediction, lead identification, lead optimization, and humanization of VHH antibodies.
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24
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Olechnovič K, Valančauskas L, Dapkūnas J, Venclovas Č. Prediction of protein assemblies by structure sampling followed by interface-focused scoring. Proteins 2023; 91:1724-1733. [PMID: 37578163 DOI: 10.1002/prot.26569] [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: 03/08/2023] [Revised: 07/12/2023] [Accepted: 07/31/2023] [Indexed: 08/15/2023]
Abstract
Proteins often function as part of permanent or transient multimeric complexes, and understanding function of these assemblies requires knowledge of their three-dimensional structures. While the ability of AlphaFold to predict structures of individual proteins with unprecedented accuracy has revolutionized structural biology, modeling structures of protein assemblies remains challenging. To address this challenge, we developed a protocol for predicting structures of protein complexes involving model sampling followed by scoring focused on the subunit-subunit interaction interface. In this protocol, we diversified AlphaFold models by varying construction and pairing of multiple sequence alignments as well as increasing the number of recycles. In cases when AlphaFold failed to assemble a full protein complex or produced unreliable results, additional diverse models were constructed by docking of monomers or subcomplexes. All the models were then scored using a newly developed method, VoroIF-jury, which relies only on structural information. Notably, VoroIF-jury is independent of AlphaFold self-assessment scores and therefore can be used to rank models originating from different structure prediction methods. We tested our protocol in CASP15 and obtained top results, significantly outperforming the standard AlphaFold-Multimer pipeline. Analysis of our results showed that the accuracy of our assembly models was capped mainly by structure sampling rather than model scoring. This observation suggests that better sampling, especially for the antibody-antigen complexes, may lead to further improvement. Our protocol is expected to be useful for modeling and/or scoring protein assemblies.
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Affiliation(s)
- Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Lukas Valančauskas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
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25
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Chungyoun M, Gray JJ. AI Models for Protein Design are Driving Antibody Engineering. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2023; 28:100473. [PMID: 37484815 PMCID: PMC10361400 DOI: 10.1016/j.cobme.2023.100473] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Therapeutic antibody engineering seeks to identify antibody sequences with specific binding to a target and optimized drug-like properties. When guided by deep learning, antibody generation methods can draw on prior knowledge and experimental efforts to improve this process. By leveraging the increasing quantity and quality of predicted structures of antibodies and target antigens, powerful structure-based generative models are emerging. In this review, we tie the advancements in deep learning-based protein structure prediction and design to the study of antibody therapeutics.
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Affiliation(s)
- Michael Chungyoun
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA
- Program in Molecular Biophysics, institute for Nanobiotechnology, and Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21287, USA
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26
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Khakzad H, Igashov I, Schneuing A, Goverde C, Bronstein M, Correia B. A new age in protein design empowered by deep learning. Cell Syst 2023; 14:925-939. [PMID: 37972559 DOI: 10.1016/j.cels.2023.10.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/22/2023] [Accepted: 10/11/2023] [Indexed: 11/19/2023]
Abstract
The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of proteins. Along with novel architectures for generative modeling and sequence analysis, they have revolutionized the protein design field in the past few years remarkably by improving the accuracy and ability to identify novel protein sequences and structures. Deep neural networks can now learn and extract the fundamental features of protein structures, predict how they interact with other biomolecules, and have the potential to create new effective drugs for treating disease. As their applicability in protein design is rapidly growing, we review the recent developments and technology in deep learning methods and provide examples of their performance to generate novel functional proteins.
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Affiliation(s)
- Hamed Khakzad
- Université de Lorraine, CNRS, Inria, LORIA, 54000 Nancy, France; École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Ilia Igashov
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Arne Schneuing
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Casper Goverde
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | | | - Bruno Correia
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
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27
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Fernández-Quintero ML, Pomarici ND, Fischer ALM, Hoerschinger VJ, Kroell KB, Riccabona JR, Kamenik AS, Loeffler JR, Ferguson JA, Perrett HR, Liedl KR, Han J, Ward AB. Structure and Dynamics Guiding Design of Antibody Therapeutics and Vaccines. Antibodies (Basel) 2023; 12:67. [PMID: 37873864 PMCID: PMC10594513 DOI: 10.3390/antib12040067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/10/2023] [Accepted: 10/13/2023] [Indexed: 10/25/2023] Open
Abstract
Antibodies and other new antibody-like formats have emerged as one of the most rapidly growing classes of biotherapeutic proteins. Understanding the structural features that drive antibody function and, consequently, their molecular recognition is critical for engineering antibodies. Here, we present the structural architecture of conventional IgG antibodies alongside other formats. We emphasize the importance of considering antibodies as conformational ensembles in solution instead of focusing on single-static structures because their functions and properties are strongly governed by their dynamic nature. Thus, in this review, we provide an overview of the unique structural and dynamic characteristics of antibodies with respect to their antigen recognition, biophysical properties, and effector functions. We highlight the numerous technical advances in antibody structure prediction and design, enabled by the vast number of experimentally determined high-quality structures recorded with cryo-EM, NMR, and X-ray crystallography. Lastly, we assess antibody and vaccine design strategies in the context of structure and dynamics.
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Affiliation(s)
- Monica L. Fernández-Quintero
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Nancy D. Pomarici
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Anna-Lena M. Fischer
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Valentin J. Hoerschinger
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Katharina B. Kroell
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Jakob R. Riccabona
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Anna S. Kamenik
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Johannes R. Loeffler
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - James A. Ferguson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Hailee R. Perrett
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Klaus R. Liedl
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Julianna Han
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Andrew B. Ward
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
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28
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Pang KT, Yang YS, Zhang W, Ho YS, Sormanni P, Michaels TCT, Walsh I, Chia S. Understanding and controlling the molecular mechanisms of protein aggregation in mAb therapeutics. Biotechnol Adv 2023; 67:108192. [PMID: 37290583 DOI: 10.1016/j.biotechadv.2023.108192] [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: 02/16/2023] [Revised: 05/09/2023] [Accepted: 06/01/2023] [Indexed: 06/10/2023]
Abstract
In antibody development and manufacturing, protein aggregation is a common challenge that can lead to serious efficacy and safety issues. To mitigate this problem, it is important to investigate its molecular origins. This review discusses (1) our current molecular understanding and theoretical models of antibody aggregation, (2) how various stress conditions related to antibody upstream and downstream bioprocesses can trigger aggregation, and (3) current mitigation strategies employed towards inhibiting aggregation. We discuss the relevance of the aggregation phenomenon in the context of novel antibody modalities and highlight how in silico approaches can be exploited to mitigate it.
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Affiliation(s)
- Kuin Tian Pang
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; School of Chemistry, Chemical Engineering, and Biotechnology, Nanyang Technology University, Singapore
| | - Yuan Sheng Yang
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Wei Zhang
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Ying Swan Ho
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Pietro Sormanni
- Chemistry of Health, Yusuf Hamied Department of Chemistry, University of Cambridge, United Kingdom
| | - Thomas C T Michaels
- Department of Biology, Institute of Biochemistry, ETH Zurich, Otto-Stern-Weg 3, 8093 Zurich, Switzerland; Bringing Materials to Life Initiative, ETH Zurich, Switzerland
| | - Ian Walsh
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore.
| | - Sean Chia
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore.
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29
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Zeng X, Wang T, Kang Y, Bai G, Ma B. Evaluation of Molecular Simulations and Deep Learning Prediction of Antibodies' Recognition of TRBC1 and TRBC2. Antibodies (Basel) 2023; 12:58. [PMID: 37753972 PMCID: PMC10525649 DOI: 10.3390/antib12030058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 08/25/2023] [Accepted: 09/05/2023] [Indexed: 09/28/2023] Open
Abstract
T cell receptor β-chain constant (TRBC) is a promising class of cancer targets consisting of two highly homologous proteins, TRBC1 and TRBC2. Developing targeted antibody therapeutics against TRBC1 or TRBC2 is expected to eradicate the malignant T cells and preserve half of the normal T cells. Recently, several antibody engineering strategies have been used to modulate the TRBC1 and TRBC2 specificity of antibodies. Here, we used molecular simulation and artificial intelligence methods to quantify the affinity difference in antibodies with various mutations for TRBC1 and TRBC2. The affinity of the existing mutants was verified by FEP calculations aided by the AI. We also performed long-time molecular dynamics simulations to reveal the dynamical antigen recognition mechanisms of the TRBC antibodies.
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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.); (G.B.)
| | - Tianqun Wang
- Shanghai Digiwiser Biological Inc., Shanghai 200240, China; (T.W.); (Y.K.)
| | - Yue Kang
- Shanghai Digiwiser Biological Inc., Shanghai 200240, China; (T.W.); (Y.K.)
| | - Ganggang Bai
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; (X.Z.); (G.B.)
| | - Buyong Ma
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; (X.Z.); (G.B.)
- Shanghai Digiwiser Biological Inc., Shanghai 200240, China; (T.W.); (Y.K.)
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30
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Li J, Kang G, Wang J, Yuan H, Wu Y, Meng S, Wang P, Zhang M, Wang Y, Feng Y, Huang H, de Marco A. Affinity maturation of antibody fragments: A review encompassing the development from random approaches to computational rational optimization. Int J Biol Macromol 2023; 247:125733. [PMID: 37423452 DOI: 10.1016/j.ijbiomac.2023.125733] [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: 04/03/2023] [Revised: 07/04/2023] [Accepted: 07/06/2023] [Indexed: 07/11/2023]
Abstract
Routinely screened antibody fragments usually require further in vitro maturation to achieve the desired biophysical properties. Blind in vitro strategies can produce improved ligands by introducing random mutations into the original sequences and selecting the resulting clones under more and more stringent conditions. Rational approaches exploit an alternative perspective that aims first at identifying the specific residues potentially involved in the control of biophysical mechanisms, such as affinity or stability, and then to evaluate what mutations could improve those characteristics. The understanding of the antigen-antibody interactions is instrumental to develop this process the reliability of which, consequently, strongly depends on the quality and completeness of the structural information. Recently, methods based on deep learning approaches critically improved the speed and accuracy of model building and are promising tools for accelerating the docking step. Here, we review the features of the available bioinformatic instruments and analyze the reports illustrating the result obtained with their application to optimize antibody fragments, and nanobodies in particular. Finally, the emerging trends and open questions are summarized.
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Affiliation(s)
- Jiaqi Li
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Guangbo Kang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Jiewen Wang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Haibin Yuan
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Yili Wu
- Zhejiang Provincial Clinical Research Center for Mental Disorders, School of Mental Health and the Affiliated Kangning Hospital, Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Wenzhou Medical University, Oujiang Laboratory, Wenzhou, Zhejiang 325035, China
| | - Shuxian Meng
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Ping Wang
- New Technology R&D Department, Tianjin Modern Innovative TCM Technology Company Limited, Tianjin 300392, China
| | - Miao Zhang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; China Resources Biopharmaceutical Company Limited, Beijing 100029, China
| | - Yuli Wang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Tianjin Pharmaceutical Da Ren Tang Group Corporation Limited, Traditional Chinese Pharmacy Research Institute, Tianjin Key Laboratory of Quality Control in Chinese Medicine, Tianjin 300457, China; State Key Laboratory of Drug Delivery Technology and Pharmacokinetics, Tianjin Institute of Pharmaceutical Research, Tianjin 300193, China
| | - Yuanhang Feng
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - He Huang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China.
| | - Ario de Marco
- Laboratory for Environmental and Life Sciences, University of Nova Gorica, Nova Gorica, Slovenia.
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31
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Guarra F, Colombo G. Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens. J Chem Theory Comput 2023; 19:5315-5333. [PMID: 37527403 PMCID: PMC10448727 DOI: 10.1021/acs.jctc.3c00513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Indexed: 08/03/2023]
Abstract
The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.
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Affiliation(s)
- Federica Guarra
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
| | - Giorgio Colombo
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
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32
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Nam KH. AI-based protein models enhance the accuracy of experimentally determined protein crystal structures. Front Mol Biosci 2023; 10:1208810. [PMID: 37426417 PMCID: PMC10324573 DOI: 10.3389/fmolb.2023.1208810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 06/13/2023] [Indexed: 07/11/2023] Open
Affiliation(s)
- Ki Hyun Nam
- Department of Life Science, Pohang University of Science and Technology, Pohang, Republic of Korea
- POSTECH Biotech Center, Pohang University of Science and Technology, Pohang, Republic of Korea
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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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Li T, Li Y, Zhu X, He Y, Wu Y, Ying T, Xie Z. Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction. Semin Cancer Biol 2023; 91:50-69. [PMID: 36870459 DOI: 10.1016/j.semcancer.2023.02.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/13/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023]
Abstract
Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.
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Affiliation(s)
- Tong Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yupeng Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyi Zhu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Yao He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yanling Wu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Tianlei Ying
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China.
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China.
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Lee FS, Anderson AG, Olafson BD. Benchmarking TriadAb using targets from the second antibody modeling assessment. Protein Eng Des Sel 2023; 36:gzad013. [PMID: 37864287 DOI: 10.1093/protein/gzad013] [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: 05/05/2023] [Revised: 08/10/2023] [Indexed: 10/22/2023] Open
Abstract
Computational modeling and design of antibodies has become an integral part of today's research and development in antibody therapeutics. Here we describe the Triad Antibody Homology Modeling (TriadAb) package, a functionality of the Triad protein design platform that predicts the structure of any heavy and light chain sequences of an antibody Fv domain using template-based modeling. To gauge the performance of TriadAb, we benchmarked against the results of the Second Antibody Modeling Assessment (AMA-II). On average, TriadAb produced main-chain carbonyl root-mean-square deviations between models and experimentally determined structures at 1.10 Å, 1.45 Å, 1.41 Å, 3.04 Å, 1.47 Å, 1.27 Å, 1.63 Å in the framework and the six complementarity-determining regions (H1, H2, H3, L1, L2, L3), respectively. The inaugural results are comparable to those reported in AMA-II, corroborating with our internal bench-based experiences that models generated using TriadAb are sufficiently accurate and useful for antibody engineering using the sequence design capabilities provided by Triad.
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Seo K, Hagino K, Ichihashi N. Progresses in Cell-Free In Vitro Evolution. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2023; 186:121-140. [PMID: 37306699 DOI: 10.1007/10_2023_219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Biopolymers, such as proteins and RNA, are integral components of living organisms and have evolved through a process of repeated mutation and selection. The technique of "cell-free in vitro evolution" is a powerful experimental approach for developing biopolymers with desired functions and structural properties. Since Spiegelman's pioneering work over 50 years ago, biopolymers with a wide range of functions have been developed using in vitro evolution in cell-free systems. The use of cell-free systems offers several advantages, including the ability to synthesize a wider range of proteins without the limitations imposed by cytotoxicity, and the capacity for higher throughput and larger library sizes than cell-based evolutionary experiments. In this chapter, we provide a comprehensive overview of the progress made in the field of cell-free in vitro evolution by categorizing evolution into directed and undirected. The biopolymers produced by these methods are valuable assets in medicine and industry, and as a means of exploring the potential of biopolymers.
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Affiliation(s)
- Kaito Seo
- Department of Life Science, Graduate School of Arts and Science, The University of Tokyo, Tokyo, Japan
| | - Katsumi Hagino
- Department of Life Science, Graduate School of Arts and Science, The University of Tokyo, Tokyo, Japan
| | - Norikazu Ichihashi
- Department of Life Science, Graduate School of Arts and Science, The University of Tokyo, Tokyo, Japan.
- Komaba Institute for Science, The University of Tokyo, Tokyo, Japan.
- Universal Biology Institute, The University of Tokyo, Tokyo, Japan.
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Lazzarini E, Pace A, Trozzi I, Zangheri M, Guardigli M, Calabria D, Mirasoli M. An Origami Paper-Based Biosensor for Allergen Detection by Chemiluminescence Immunoassay on Magnetic Microbeads. BIOSENSORS 2022; 12:825. [PMID: 36290961 PMCID: PMC9599061 DOI: 10.3390/bios12100825] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
Food allergies are adverse health effects that arise from specific immune responses, occurring upon exposure to given foods, even if present in traces. Egg allergy is one of the most common food allergies, mainly caused by egg white proteins, with ovalbumin being the most abundant. As allergens can also be present in foodstuff due to unintended contamination, there is a need for analytical tools that are able to rapidly detect allergens in food products at the point-of-use. Herein, we report an origami paper-based device for detecting ovalbumin in food samples, based on a competitive immunoassay with chemiluminescence detection. In this biosensor, magnetic microbeads have been employed for easy and efficient immobilization of ovalbumin on paper. Immobilized ovalbumin competes with the ovalbumin present in the sample for a limited amount of enzyme-labelled anti-ovalbumin antibody. By exploiting the origami approach, a multistep analytical procedure could be performed using reagents preloaded on paper layers, thus providing a ready-to-use immunosensing platform. The assay provided a limit of detection (LOD) of about 1 ng mL-1 for ovalbumin and, when tested on ovalbumin-spiked food matrices (chocolate chip cookies), demonstrated good assay specificity and accuracy, as compared with a commercial immunoassay kit.
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Affiliation(s)
- Elisa Lazzarini
- Department of Chemistry “Giacomo Ciamician”, Alma Mater Studiorum, University of Bologna, Via Francesco Selmi 2, I-40126 Bologna, Italy
| | - Andrea Pace
- Department of Chemistry “Giacomo Ciamician”, Alma Mater Studiorum, University of Bologna, Via Francesco Selmi 2, I-40126 Bologna, Italy
| | - Ilaria Trozzi
- Department of Chemistry “Giacomo Ciamician”, Alma Mater Studiorum, University of Bologna, Via Francesco Selmi 2, I-40126 Bologna, Italy
| | - Martina Zangheri
- Department of Chemistry “Giacomo Ciamician”, Alma Mater Studiorum, University of Bologna, Via Francesco Selmi 2, I-40126 Bologna, Italy
- Interdepartmental Centre for Industrial Agrofood Research (CIRI AGRO), Alma Mater Studiorum, University of Bologna, Via Quinto Bucci 336, I-47521 Cesena, Italy
- Interdepartmental Centre for Industrial Research in Advanced Mechanical Engineering Applications and Materials Technology (CIRI MAM), Alma Mater Studiorum, University of Bologna, Viale Risorgimento 2, I-40136 Bologna, Italy
| | - Massimo Guardigli
- Department of Chemistry “Giacomo Ciamician”, Alma Mater Studiorum, University of Bologna, Via Francesco Selmi 2, I-40126 Bologna, Italy
- Interdepartmental Centre for Industrial Research in Renewable Resources, Environment, Sea and Energy (CIRI FRAME), Alma Mater Studiorum, University of Bologna, Via Sant’Alberto 163, I-48123 Ravenna, Italy
- Interdepartmental Centre for Industrial Aerospace Research (CIRI AEROSPACE), Alma Mater Studiorum, University of Bologna, Via Baldassarre Canaccini 12, I-47121 Forlì, Italy
| | - Donato Calabria
- Department of Chemistry “Giacomo Ciamician”, Alma Mater Studiorum, University of Bologna, Via Francesco Selmi 2, I-40126 Bologna, Italy
- Interdepartmental Centre for Industrial Aerospace Research (CIRI AEROSPACE), Alma Mater Studiorum, University of Bologna, Via Baldassarre Canaccini 12, I-47121 Forlì, Italy
| | - Mara Mirasoli
- Department of Chemistry “Giacomo Ciamician”, Alma Mater Studiorum, University of Bologna, Via Francesco Selmi 2, I-40126 Bologna, Italy
- Interdepartmental Centre for Industrial Research in Renewable Resources, Environment, Sea and Energy (CIRI FRAME), Alma Mater Studiorum, University of Bologna, Via Sant’Alberto 163, I-48123 Ravenna, Italy
- Interdepartmental Centre for Industrial Aerospace Research (CIRI AEROSPACE), Alma Mater Studiorum, University of Bologna, Via Baldassarre Canaccini 12, I-47121 Forlì, Italy
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Wilman W, Wróbel S, Bielska W, Deszynski P, Dudzic P, Jaszczyszyn I, Kaniewski J, Młokosiewicz J, Rouyan A, Satława T, Kumar S, Greiff V, Krawczyk K. Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery. Brief Bioinform 2022; 23:bbac267. [PMID: 35830864 PMCID: PMC9294429 DOI: 10.1093/bib/bbac267] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/09/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody-antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.
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