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Khan S, Ahmad N, Fazal H, Saleh IA, Abdel-Maksoud MA, Malik A, AbdElgayed G, Jalal A, Rauf K, Ali L, Ullah S, Niqabullah, Ahmad S. Exploring stevioside binding affinity with various proteins and receptors actively involved in the signaling pathway and a future candidate for diabetic patients. Front Pharmacol 2024; 15:1377916. [PMID: 39170696 PMCID: PMC11335537 DOI: 10.3389/fphar.2024.1377916] [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: 01/28/2024] [Accepted: 07/16/2024] [Indexed: 08/23/2024] Open
Abstract
Introduction and Background: Diabetes is a chronic metabolic disease characterized by elevated blood glucose levels and is one of the main global health concerns. Synthetic sugar substrate has many side effects such as leukemia, bladder cancer, hepatotoxicity, breast cancer, headache, and brain toxicity. The WHO and FDA has recently banned some of the synthetic sugar alternatives due to their carcinogenic effects. Objective and Methodology: Therefore, the main objective of the current study was to investigate the safety and binding affinity of Stevioside with Glucose Transpoter-4 (GLUT-4), Akt, Insulin Receptor (IR) and Insulin Receptor Substrate-1 (IRS-1) to confirmed that Stevioside is one the potent natural sweetener/drug for diabetes. This study delves into the molecular interaction between Stevioside and key diabetic proteins: GLUT-4, Akt, IR and IRS-1. A precise molecular docking approach was used to simulate the binding affinity of Stevioside to these proteins. The pharmacokinetic properties of the molecule should be taken into consideration as important variables throughout the virtual screening process. Results: The result of active site analysis of GLUT-4, Akt, IR and IRS-1 showed a zone of 2158.359 Ǻ2, 579.259 Ǻ2, 762.651 Ǻ2, and 152.167 Ǻ2 and a volume of 2765.094 Ǻ³, 355.567 Ǻ³, 686.806 Ǻ³, and 116.874 Ǻ³, respectively. Docking analysis of the Stevioside compound showed the highest docking energy with scores of -9.9 with GLUT-4, -6.7 with Akt, -8.0 with IR and -8.8 with IRS-1. Studies indicated that it remains undigested by stomach acids and enzymes and is not absorbed in the upper small intestine. Further, tests revealed no hepatotoxicity, AMES toxicity, or skin sensitivity, making it a promising candidate for safe consumption as drug metabolism. Conclusion and Recommendations: Instead of other sugar alternatives, Stevioside will help diabetic patients with a lower chance of infections, lowered blood pressure/blood sugar, and increased glucose uptake in diabetic muscles. Stevioside is a natural sweetener, and the current study recommends its usage in various dietary products for diabetic patients.
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Affiliation(s)
- Salman Khan
- Center for Biotechnology and Microbiology, University of Swat, Swat, Pakistan
| | - Nisar Ahmad
- Center for Biotechnology and Microbiology, University of Swat, Swat, Pakistan
| | - Hina Fazal
- Pakistan Council of Scientific and Industrial Research (PCSIR) Laboratories Complex, Peshawar, Pakistan
| | | | - Mostafa A. Abdel-Maksoud
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Abdul Malik
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Gehad AbdElgayed
- Integrated Molecular Plant Physiology Research, Department of Biology, University of Antwerp, Antwerp, Belgium
| | - Arshad Jalal
- São Paulo State University (UNESP), Ilha Solteira, Brazil
| | - Kamran Rauf
- Department of Horticulture the University of Agriculture Peshawar, Peshawar, Pakistan
| | - Liaqat Ali
- Center for Biotechnology and Microbiology, University of Swat, Swat, Pakistan
| | - Sami Ullah
- Center for Biotechnology and Microbiology, University of Swat, Swat, Pakistan
| | - Niqabullah
- Department of General Medicine, Semey Medical University Kazakhstan, Semey, Kazakhstan
| | - Sajjad Ahmad
- Center for Biotechnology and Microbiology, University of Swat, Swat, Pakistan
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2
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Han X, Lu X, Li PH, Wang S, Schalek R, Meirovitch Y, Lin Z, Adhinarta J, Murray KD, MacNiven LM, Berger DR, Wu Y, Fang T, Meral ES, Asraf S, Ploegh H, Pfister H, Wei D, Jain V, Trimmer JS, Lichtman JW. Multiplexed volumetric CLEM enabled by scFvs provides insights into the cytology of cerebellar cortex. Nat Commun 2024; 15:6648. [PMID: 39103318 DOI: 10.1038/s41467-024-50411-z] [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: 06/28/2023] [Accepted: 07/01/2024] [Indexed: 08/07/2024] Open
Abstract
Mapping neuronal networks is a central focus in neuroscience. While volume electron microscopy (vEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide molecular information to identify cell types or functions. We developed an approach that uses fluorescent single-chain variable fragments (scFvs) to perform multiplexed detergent-free immunolabeling and volumetric-correlated-light-and-electron-microscopy on the same sample. We generated eight fluorescent scFvs targeting brain markers. Six fluorescent probes were imaged in the cerebellum of a female mouse, using confocal microscopy with spectral unmixing, followed by vEM of the same sample. The results provide excellent ultrastructure superimposed with multiple fluorescence channels. Using this approach, we documented a poorly described cell type, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.
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Affiliation(s)
- Xiaomeng Han
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
| | - Xiaotang Lu
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
| | | | - Shuohong Wang
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Richard Schalek
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Yaron Meirovitch
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Zudi Lin
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Jason Adhinarta
- Computer Science Department, Boston College, Chestnut Hill, MA, USA
| | - Karl D Murray
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA, USA
| | - Leah M MacNiven
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA, USA
| | - Daniel R Berger
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Yuelong Wu
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Tao Fang
- Program of Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA
| | | | - Shadnan Asraf
- School of Public Health, University of Massachusetts Amherst, Amherst, MA, USA
| | - Hidde Ploegh
- Program of Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Hanspeter Pfister
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA, USA
| | - Donglai Wei
- Computer Science Department, Boston College, Chestnut Hill, MA, USA
| | | | - James S Trimmer
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA, USA
| | - Jeff W Lichtman
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
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3
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Listov D, Goverde CA, Correia BE, Fleishman SJ. Opportunities and challenges in design and optimization of protein function. Nat Rev Mol Cell Biol 2024; 25:639-653. [PMID: 38565617 PMCID: PMC7616297 DOI: 10.1038/s41580-024-00718-y] [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] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.
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Affiliation(s)
- Dina Listov
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Casper A Goverde
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Sarel Jacob Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel.
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4
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Dahlström KM, Salminen TA. Apprehensions and emerging solutions in ML-based protein structure prediction. Curr Opin Struct Biol 2024; 86:102819. [PMID: 38631107 DOI: 10.1016/j.sbi.2024.102819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/05/2024] [Accepted: 03/31/2024] [Indexed: 04/19/2024]
Abstract
The three-dimensional structure of proteins determines their function in vital biological processes. Thus, when the structure is known, the molecular mechanism of protein function can be understood in more detail and obtained information utilized in biotechnological, diagnostics, and therapeutic applications. Over the past five years, machine learning (ML)-based modeling has pushed protein structure prediction to the next level with AlphaFold in the front line, predicting the structure for hundreds of millions of proteins. Further advances recently report promising ML-based approaches for solving remaining challenges by incorporating functionally important metals, co-factors, post-translational modifications, structural dynamics, and interdomain and multimer interactions in the structure prediction process.
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Affiliation(s)
- Käthe M Dahlström
- Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, Tykistökatu 6A, 20520 Turku, Finland; InFLAMES Research Flagship Center, Åbo Akademi University, 20520 Turku, Finland
| | - Tiina A Salminen
- Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, Tykistökatu 6A, 20520 Turku, Finland; InFLAMES Research Flagship Center, Åbo Akademi University, 20520 Turku, Finland.
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5
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Tennenhouse A, Khmelnitsky L, Khalaila R, Yeshaya N, Noronha A, Lindzen M, Makowski EK, Zaretsky I, Sirkis YF, Galon-Wolfenson Y, Tessier PM, Abramson J, Yarden Y, Fass D, Fleishman SJ. Computational optimization of antibody humanness and stability by systematic energy-based ranking. Nat Biomed Eng 2024; 8:30-44. [PMID: 37550425 PMCID: PMC10842793 DOI: 10.1038/s41551-023-01079-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 07/13/2023] [Indexed: 08/09/2023]
Abstract
Conventional methods for humanizing animal-derived antibodies involve grafting their complementarity-determining regions onto homologous human framework regions. However, this process can substantially lower antibody stability and antigen-binding affinity, and requires iterative mutational fine-tuning to recover the original antibody properties. Here we report a computational method for the systematic grafting of animal complementarity-determining regions onto thousands of human frameworks. The method, which we named CUMAb (for computational human antibody design; available at http://CUMAb.weizmann.ac.il ), starts from an experimental or model antibody structure and uses Rosetta atomistic simulations to select designs by energy and structural integrity. CUMAb-designed humanized versions of five antibodies exhibited similar affinities to those of the parental animal antibodies, with some designs showing marked improvement in stability. We also show that (1) non-homologous frameworks are often preferred to highest-homology frameworks, and (2) several CUMAb designs that differ by dozens of mutations and that use different human frameworks are functionally equivalent.
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Affiliation(s)
- Ariel Tennenhouse
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Lev Khmelnitsky
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Razi Khalaila
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Noa Yeshaya
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ashish Noronha
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
| | - Moshit Lindzen
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Emily K Makowski
- Biointerfaces Institute and Departments of Chemical Engineering, Pharmaceutical Sciences and Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Ira Zaretsky
- Antibody Engineering Unit, Weizmann Institute of Science, Rehovot, Israel
| | | | | | - Peter M Tessier
- Biointerfaces Institute and Departments of Chemical Engineering, Pharmaceutical Sciences and Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Jakub Abramson
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Yosef Yarden
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Deborah Fass
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel.
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6
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Bai G, Sun C, Guo Z, Wang Y, Zeng X, Su Y, Zhao Q, Ma B. Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects. Semin Cancer Biol 2023; 95:13-24. [PMID: 37355214 DOI: 10.1016/j.semcancer.2023.06.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/09/2023] [Accepted: 06/18/2023] [Indexed: 06/26/2023]
Abstract
Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.
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Affiliation(s)
- Ganggang Bai
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Chuance Sun
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ziang Guo
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China
| | - Yangjing Wang
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xincheng Zeng
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuhong Su
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China; MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa, Macao Special Administrative Region of China.
| | - Buyong Ma
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Digiwiser BioTechnolgy, Limited, Shanghai 201203, China.
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7
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Fischman S, Levin I, Rondeau JM, Štrajbl M, Lehmann S, Huber T, Nimrod G, Cebe R, Omer D, Kovarik J, Bernstein S, Sasson Y, Demishtein A, Shlamkovich T, Bluvshtein O, Grossman N, Barak-Fuchs R, Zhenin M, Fastman Y, Twito S, Vana T, Zur N, Ofran Y. "Redirecting an anti-IL-1β antibody to bind a new, unrelated and computationally predicted epitope on hIL-17A". Commun Biol 2023; 6:997. [PMID: 37773269 PMCID: PMC10542344 DOI: 10.1038/s42003-023-05369-x] [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: 01/16/2023] [Accepted: 09/18/2023] [Indexed: 10/01/2023] Open
Abstract
Antibody engineering technology is at the forefront of therapeutic antibody development. The primary goal for engineering a therapeutic antibody is the generation of an antibody with a desired specificity, affinity, function, and developability profile. Mature antibodies are considered antigen specific, which may preclude their use as a starting point for antibody engineering. Here, we explore the plasticity of mature antibodies by engineering novel specificity and function to a pre-selected antibody template. Using a small, focused library, we engineered AAL160, an anti-IL-1β antibody, to bind the unrelated antigen IL-17A, with the introduction of seven mutations. The final redesigned antibody, 11.003, retains favorable biophysical properties, binds IL-17A with sub-nanomolar affinity, inhibits IL-17A binding to its cognate receptor and is functional in a cell-based assay. The epitope of the engineered antibody can be computationally predicted based on the sequence of the template antibody, as is confirmed by the crystal structure of the 11.003/IL-17A complex. The structures of the 11.003/IL-17A and the AAL160/IL-1β complexes highlight the contribution of germline residues to the paratopes of both the template and re-designed antibody. This case study suggests that the inherent plasticity of antibodies allows for re-engineering of mature antibodies to new targets, while maintaining desirable developability profiles.
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Affiliation(s)
| | - Itay Levin
- Biolojic Design LTD, Rehovot, Israel
- Enzymit LTD, Ness Ziona, Israel
| | | | | | - Sylvie Lehmann
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Thomas Huber
- Novartis Institutes for Biomedical Research, Basel, Switzerland
- Ridgelinediscovery, Basel, Switzerland
| | | | - Régis Cebe
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Dotan Omer
- Biolojic Design LTD, Rehovot, Israel
- EmendoBio Inc., Rehovot, Israel
| | - Jiri Kovarik
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | | | | | - Alik Demishtein
- Biolojic Design LTD, Rehovot, Israel
- Anima Biotech, Ramat-Gan, Israel
| | | | - Olga Bluvshtein
- Biolojic Design LTD, Rehovot, Israel
- Enzymit LTD, Ness Ziona, Israel
| | | | | | | | | | - Shir Twito
- Biolojic Design LTD, Rehovot, Israel
- Enzymit LTD, Ness Ziona, Israel
| | - Tal Vana
- Biolojic Design LTD, Rehovot, Israel
| | - Nevet Zur
- Biolojic Design LTD, Rehovot, Israel
| | - Yanay Ofran
- Biolojic Design LTD, Rehovot, Israel
- The Goodman Faculty of Life Sciences, Nanotechnology Building, Bar Ilan University, Ramat Gan, Israel
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8
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McConnell A, Hackel BJ. Protein engineering via sequence-performance mapping. Cell Syst 2023; 14:656-666. [PMID: 37494931 PMCID: PMC10527434 DOI: 10.1016/j.cels.2023.06.009] [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: 02/27/2023] [Revised: 05/10/2023] [Accepted: 06/21/2023] [Indexed: 07/28/2023]
Abstract
Discovery and evolution of new and improved proteins has empowered molecular therapeutics, diagnostics, and industrial biotechnology. Discovery and evolution both require efficient screens and effective libraries, although they differ in their challenges because of the absence or presence, respectively, of an initial protein variant with the desired function. A host of high-throughput technologies-experimental and computational-enable efficient screens to identify performant protein variants. In partnership, an informed search of sequence space is needed to overcome the immensity, sparsity, and complexity of the sequence-performance landscape. Early in the historical trajectory of protein engineering, these elements aligned with distinct approaches to identify the most performant sequence: selection from large, randomized combinatorial libraries versus rational computational design. Substantial advances have now emerged from the synergy of these perspectives. Rational design of combinatorial libraries aids the experimental search of sequence space, and high-throughput, high-integrity experimental data inform computational design. At the core of the collaborative interface, efficient protein characterization (rather than mere selection of optimal variants) maps sequence-performance landscapes. Such quantitative maps elucidate the complex relationships between protein sequence and performance-e.g., binding, catalytic efficiency, biological activity, and developability-thereby advancing fundamental protein science and facilitating protein discovery and evolution.
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Affiliation(s)
- Adam McConnell
- Department of Biomedical Engineering, University of Minnesota - Twin Cities, 421 Washington Avenue SE, Minneapolis, MN 55455, USA
| | - Benjamin J Hackel
- Department of Biomedical Engineering, University of Minnesota - Twin Cities, 421 Washington Avenue SE, Minneapolis, MN 55455, USA; Department of Chemical Engineering and Materials Science, University of Minnesota - Twin Cities, 421 Washington Avenue SE, Minneapolis, MN 55455, USA.
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9
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Bauer J, Rajagopal N, Gupta P, Gupta P, Nixon AE, Kumar S. How can we discover developable antibody-based biotherapeutics? Front Mol Biosci 2023; 10:1221626. [PMID: 37609373 PMCID: PMC10441133 DOI: 10.3389/fmolb.2023.1221626] [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: 05/12/2023] [Accepted: 07/10/2023] [Indexed: 08/24/2023] Open
Abstract
Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.
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Affiliation(s)
- Joschka Bauer
- Early Stage Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach/Riss, Germany
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
| | - Nandhini Rajagopal
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Priyanka Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Pankaj Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Andrew E. Nixon
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Sandeep Kumar
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
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10
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Han X, Lu X, Li PH, Wang S, Schalek R, Meirovitch Y, Lin Z, Adhinarta J, Berger D, Wu Y, Fang T, Meral ES, Asraf S, Ploegh H, Pfister H, Wei D, Jain V, Trimmer JS, Lichtman JW. Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.20.540091. [PMID: 37292964 PMCID: PMC10245788 DOI: 10.1101/2023.05.20.540091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.
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11
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Han X, Lu X, Li PH, Wang S, Schalek R, Meirovitch Y, Lin Z, Adhinarta J, Berger D, Wu Y, Fang T, Meral ES, Asraf S, Ploegh H, Pfister H, Wei D, Jain V, Trimmer JS, Lichtman JW. Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex. RESEARCH SQUARE 2023:rs.3.rs-3121892. [PMID: 37461609 PMCID: PMC10350204 DOI: 10.21203/rs.3.rs-3121892/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.
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Affiliation(s)
- Xiaomeng Han
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Xiaotang Lu
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | | | - Shuohong Wang
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Richard Schalek
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Yaron Meirovitch
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Zudi Lin
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Jason Adhinarta
- Computer Science Department, Boston College, Chestnut Hill, MA
| | - Daniel Berger
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Yuelong Wu
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Tao Fang
- Program of Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
| | | | - Shadnan Asraf
- School of Public Health, University of Massachusetts Amherst, Amherst, MA
| | - Hidde Ploegh
- Program of Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
| | - Hanspeter Pfister
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Donglai Wei
- Computer Science Department, Boston College, Chestnut Hill, MA
| | | | - James S. Trimmer
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA
| | - Jeff W. Lichtman
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
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12
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Bennett NR, Coventry B, Goreshnik I, Huang B, Allen A, Vafeados D, Peng YP, Dauparas J, Baek M, Stewart L, DiMaio F, De Munck S, Savvides SN, Baker D. Improving de novo protein binder design with deep learning. Nat Commun 2023; 14:2625. [PMID: 37149653 PMCID: PMC10163288 DOI: 10.1038/s41467-023-38328-5] [Citation(s) in RCA: 54] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 04/24/2023] [Indexed: 05/08/2023] Open
Abstract
Recently it has become possible to de novo design high affinity protein binding proteins from target structural information alone. There is, however, considerable room for improvement as the overall design success rate is low. Here, we explore the augmentation of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.
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Affiliation(s)
- Nathaniel R Bennett
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Inna Goreshnik
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Buwei Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Aza Allen
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Dionne Vafeados
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Ying Po Peng
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justas Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Minkyung Baek
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lance Stewart
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Steven De Munck
- VIB-UGent Center for Inflammation Research, Ghent, Belgium
- Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
| | - Savvas N Savvides
- VIB-UGent Center for Inflammation Research, Ghent, Belgium
- Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
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13
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Tam C, Kukimoto-Niino M, Miyata-Yabuki Y, Tsuda K, Mishima-Tsumagari C, Ihara K, Inoue M, Yonemochi M, Hanada K, Matsumoto T, Shirouzu M, Zhang KYJ. Targeting Ras-binding domain of ELMO1 by computational nanobody design. Commun Biol 2023; 6:284. [PMID: 36932164 PMCID: PMC10023680 DOI: 10.1038/s42003-023-04657-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 03/02/2023] [Indexed: 03/19/2023] Open
Abstract
The control of cell movement through manipulation of cytoskeletal structure has therapeutic prospects notably in the development of novel anti-metastatic drugs. In this study, we determine the structure of Ras-binding domain (RBD) of ELMO1, a protein involved in cytoskeletal regulation, both alone and in complex with the activator RhoG and verify its targetability through computational nanobody design. Using our dock-and-design approach optimized with native-like initial pose selection, we obtain Nb01, a detectable binder from scratch in the first-round design. An affinity maturation step guided by structure-activity relationship at the interface generates 23 Nb01 sequence variants and 17 of them show enhanced binding to ELMO1-RBD and are modeled to form major spatial overlaps with RhoG. The best binder, Nb29, inhibited ELMO1-RBD/RhoG interaction. Molecular dynamics simulation of the flexibility of CDR2 and CDR3 of Nb29 reveal the design of stabilizing mutations at the CDR-framework junctions potentially confers the affinity enhancement.
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Affiliation(s)
- Chunlai Tam
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan
| | - Mutsuko Kukimoto-Niino
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan.
| | - Yukako Miyata-Yabuki
- Drug Discovery Structural Biology Platform Unit, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Kengo Tsuda
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Chiemi Mishima-Tsumagari
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Kentaro Ihara
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Mio Inoue
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Mayumi Yonemochi
- Drug Discovery Structural Biology Platform Unit, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Kazuharu Hanada
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Takehisa Matsumoto
- Drug Discovery Structural Biology Platform Unit, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Mikako Shirouzu
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
- Drug Discovery Structural Biology Platform Unit, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan.
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan.
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14
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Lipsh-Sokolik R, Khersonsky O, Schröder SP, de Boer C, Hoch SY, Davies GJ, Overkleeft HS, Fleishman SJ. Combinatorial assembly and design of enzymes. Science 2023; 379:195-201. [PMID: 36634164 DOI: 10.1126/science.ade9434] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
The design of structurally diverse enzymes is constrained by long-range interactions that are necessary for accurate folding. We introduce an atomistic and machine learning strategy for the combinatorial assembly and design of enzymes (CADENZ) to design fragments that combine with one another to generate diverse, low-energy structures with stable catalytic constellations. We applied CADENZ to endoxylanases and used activity-based protein profiling to recover thousands of structurally diverse enzymes. Functional designs exhibit high active-site preorganization and more stable and compact packing outside the active site. Implementing these lessons into CADENZ led to a 10-fold improved hit rate and more than 10,000 recovered enzymes. This design-test-learn loop can be applied, in principle, to any modular protein family, yielding huge diversity and general lessons on protein design principles.
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Affiliation(s)
- R Lipsh-Sokolik
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - O Khersonsky
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - S P Schröder
- Leiden Institute of Chemistry, Leiden University, Einsteinweg 55, 2300 RA Leiden, Netherlands
| | - C de Boer
- Leiden Institute of Chemistry, Leiden University, Einsteinweg 55, 2300 RA Leiden, Netherlands
| | - S-Y Hoch
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - G J Davies
- York Structural Biology Laboratory, Department of Chemistry, The University of York, Heslington, York YO10 5DD, UK
| | - H S Overkleeft
- Leiden Institute of Chemistry, Leiden University, Einsteinweg 55, 2300 RA Leiden, Netherlands
| | - S J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001 Rehovot, Israel
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15
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Svilenov HL, Arosio P, Menzen T, Tessier P, Sormanni P. Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties. MAbs 2023; 15:2164459. [PMID: 36629855 PMCID: PMC9839375 DOI: 10.1080/19420862.2022.2164459] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/22/2022] [Accepted: 12/29/2022] [Indexed: 01/12/2023] Open
Abstract
Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.
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Affiliation(s)
- Hristo L. Svilenov
- Laboratory of General Biochemistry and Physical Pharmacy, Faculty of Pharmaceutical Sciences, Ghent University, Gent, Belgium
| | - Paolo Arosio
- Department of Chemistry and Applied Biosciences, ETH Zürich, Zürich, Switzerland
| | - Tim Menzen
- Coriolis Pharma Research GmbH, Martinsried, 82152, Germany
| | - Peter Tessier
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
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16
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Yang YX, Wang P, Zhu BT. Binding affinity prediction for antibody-protein antigen complexes: A machine learning analysis based on interface and surface areas. J Mol Graph Model 2023; 118:108364. [PMID: 36356467 DOI: 10.1016/j.jmgm.2022.108364] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/08/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022]
Abstract
Specific antibodies can bind to protein antigens with high affinity and specificity, and this property makes them one of the best protein-based therapeutics. Accurate prediction of antibody‒protein antigen binding affinity is crucial for designing effective antibodies. The current predictive methods for protein‒protein binding affinity usually fail to predict the binding affinity of an antibody‒protein antigen complex with a comparable level of accuracy. Here, new models specific for antibody‒antigen binding affinity prediction are developed according to the different types of interface and surface areas present in antibody‒antigen complex. The contacts-based descriptors are also employed to construct or train different models specific for antibody‒protein antigen binding affinity prediction. The results of this study show that (i) the area-based descriptors are slightly better than the contacts-based descriptors in terms of the predictive power; (ii) the new models specific for antibody‒protein antigen binding affinity prediction are superior to the previously-used general models for predicting the protein‒protein binding affinities; (iii) the performances of the best area-based and contacts-based models developed in this work are better than the performances of a recently-developed graph-based model (i.e., CSM-AB) specific for antibody‒protein antigen binding affinity prediction. The new models developed in this work would not only help understand the mechanisms underlying antibody‒protein antigen interactions, but would also be of some applicable utility in the design and virtual screening of antibody-based therapeutics.
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Affiliation(s)
- Yong Xiao Yang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China
| | - Pan Wang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China; Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Bao Ting Zhu
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China; Shenzhen Bay Laboratory, Shenzhen, 518055, China.
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17
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Abstract
The immune systems protect vertebrates from foreign molecules or antigens, and antibodies are important mediators of this system. The sequences and structural features of antibodies vary depending on species. Many of antibodies from vertebrates, including camelids, have both heavy and light chain variable domains, but camelids also have antibodies that lack the light chains. In antibodies that lack light chains, the C-terminal variable region is called the VHH domain. Antibodies recognize antigens through six complementarity-determining regions (CDRs). The third CDR of the heavy chain (CDR-H3) is at the center of the antigen-binding site and is diverse in terms of sequence and structure. Due to the importance of antibodies in basic science as well as in medical applications, there have been many studies of CDR-H3s of antibodies that possess both light and heavy chains. However, nature of CDR-H3s of single-domain VHH antibodies is less well studied. In this chapter, we describe current knowledge of sequence-structure-function correlations of single-domain VHH antibodies with emphasis on CDR-H3. Based on the 370 crystal structures in the Protein Data Bank, we also attempt structural classification of CDR-H3 in single-domain VHH antibodies and discuss lessons learned from the ever-increasing number of the structures.
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Affiliation(s)
- Daisuke Kuroda
- Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, Tokyo, Japan.
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan.
| | - Kouhei Tsumoto
- Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, Tokyo, Japan.
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan.
- Laboratory of Medical Proteomics, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
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18
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Aguilar Rangel M, Bedwell A, Costanzi E, Taylor RJ, Russo R, Bernardes GJL, Ricagno S, Frydman J, Vendruscolo M, Sormanni P. Fragment-based computational design of antibodies targeting structured epitopes. SCIENCE ADVANCES 2022; 8:eabp9540. [PMID: 36367941 PMCID: PMC9651861 DOI: 10.1126/sciadv.abp9540] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Abstract
De novo design methods hold the promise of reducing the time and cost of antibody discovery while enabling the facile and precise targeting of predetermined epitopes. Here, we describe a fragment-based method for the combinatorial design of antibody binding loops and their grafting onto antibody scaffolds. We designed and tested six single-domain antibodies targeting different epitopes on three antigens, including the receptor-binding domain of the SARS-CoV-2 spike protein. Biophysical characterization showed that all designs are stable and bind their intended targets with affinities in the nanomolar range without in vitro affinity maturation. We further discuss how a high-resolution input antigen structure is not required, as similar predictions are obtained when the input is a crystal structure or a computer-generated model. This computational procedure, which readily runs on a laptop, provides a starting point for the rapid generation of lead antibodies binding to preselected epitopes.
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Affiliation(s)
- Mauricio Aguilar Rangel
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Alice Bedwell
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Elisa Costanzi
- Department of Bioscience, Università degli Studi di Milano, Milano 20133, Italy
| | - Ross J. Taylor
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Rosaria Russo
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milano 20122, Italy
| | - Gonçalo J. L. Bernardes
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Stefano Ricagno
- Department of Bioscience, Università degli Studi di Milano, Milano 20133, Italy
- Institute of Molecular and Translational Cardiology, IRCCS Policlinico San Donato, Milan 20097, Italy
| | - Judith Frydman
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Michele Vendruscolo
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
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19
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In Silico Maturation of a Nanomolar Antibody against the Human CXCR2. Biomolecules 2022; 12:biom12091285. [PMID: 36139124 PMCID: PMC9496334 DOI: 10.3390/biom12091285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 08/31/2022] [Accepted: 08/31/2022] [Indexed: 11/17/2022] Open
Abstract
The steady increase in computational power in the last 50 years is opening unprecedented opportunities in biology, as computer simulations of biological systems have become more accessible and can reproduce experimental results more accurately. Here, we wanted to test the ability of computer simulations to replace experiments in the limited but practically useful scope of improving the biochemical characteristics of the abN48 antibody, a nanomolar antagonist of the CXC chemokine receptor 2 (CXCR2) that was initially selected from a combinatorial antibody library. Our results showed a good correlation between the computed binding energies of the antibody to the peptide target and the experimental binding affinities. Moreover, we showed that it is possible to design new antibody sequences in silico with a higher affinity to the desired target using a Monte Carlo Metropolis algorithm. The newly designed sequences had an affinity comparable to the best ones obtained using in vitro affinity maturation and could be obtained within a similar timeframe. The methodology proposed here could represent a valid alternative for improving antibodies in cases in which experiments are too expensive or technically tricky and could open an opportunity for designing antibodies for targets that have been elusive so far.
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20
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Listov D, Lipsh‐Sokolik R, Rosset S, Yang C, Correia BE, Fleishman SJ. Assessing and enhancing foldability in designed proteins. Protein Sci 2022; 31:e4400. [PMID: 36040259 PMCID: PMC9375437 DOI: 10.1002/pro.4400] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/09/2022] [Accepted: 07/11/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Dina Listov
- Department of Biomolecular Sciences Weizmann Institute of Science Rehovot Israel
| | | | - Stéphane Rosset
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland
| | - Che Yang
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland
| | - Bruno E. Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland
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21
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Solubility-Aware Protein Binding Peptide Design Using AlphaFold. Biomedicines 2022; 10:biomedicines10071626. [PMID: 35884931 PMCID: PMC9312799 DOI: 10.3390/biomedicines10071626] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 07/02/2022] [Accepted: 07/05/2022] [Indexed: 01/02/2023] Open
Abstract
New protein–protein interactions (PPIs) are identified, but PPIs have different physicochemical properties compared with conventional targets, making it difficult to use small molecules. Peptides offer a new modality to target PPIs, but designing appropriate peptide sequences by computation is challenging. Recently, AlphaFold and RoseTTAFold have made it possible to predict protein structures from amino acid sequences with ultra-high accuracy, enabling de novo protein design. We designed peptides likely to have PPI as the target protein using the “binder hallucination” protocol of AfDesign, a de novo protein design method using AlphaFold. However, the solubility of the peptides tended to be low. Therefore, we designed a solubility loss function using solubility indices for amino acids and developed a solubility-aware AfDesign binder hallucination protocol. The peptide solubility in sequences designed using the new protocol increased with the weight of the solubility loss function; moreover, they captured the characteristics of the solubility indices. Moreover, the new protocol sequences tended to have higher affinity than random or single residue substitution sequences when evaluated by docking binding affinity. Our approach shows that it is possible to design peptide sequences that can bind to the interface of PPI while controlling solubility.
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22
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Magi Meconi G, Sasselli IR, Bianco V, Onuchic JN, Coluzza I. Key aspects of the past 30 years of protein design. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2022; 85:086601. [PMID: 35704983 DOI: 10.1088/1361-6633/ac78ef] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.
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Affiliation(s)
- Giulia Magi Meconi
- Computational Biophysics Lab, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Paseo de Miramon 182, 20014, Donostia-San Sebastián, Spain
| | - Ivan R Sasselli
- Computational Biophysics Lab, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Paseo de Miramon 182, 20014, Donostia-San Sebastián, Spain
| | | | - Jose N Onuchic
- Center for Theoretical Biological Physics, Department of Physics & Astronomy, Department of Chemistry, Department of Biosciences, Rice University, Houston, TX 77251, United States of America
| | - Ivan Coluzza
- BCMaterials, Basque Center for Materials, Applications and Nanostructures, Bld. Martina Casiano, UPV/EHU Science Park, Barrio Sarriena s/n, 48940 Leioa, Spain
- Basque Foundation for Science, Ikerbasque, 48009, Bilbao, Spain
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23
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Schauperl M, Denny RA. AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges. J Chem Inf Model 2022; 62:3142-3156. [PMID: 35727311 DOI: 10.1021/acs.jcim.2c00026] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Proteins are the molecular machinery of the human body, and their malfunctioning is often responsible for diseases, making them crucial targets for drug discovery. The three-dimensional structure of a protein determines its biological function, its conformational state determines substrates, cofactors, and protein binding. Rational drug discovery employs engineered small molecules to selectively interact with proteins to modulate their function. To selectively target a protein and to design small molecules, knowing the protein structure with all its specific conformation is critical. Unfortunately, for a large number of proteins relevant for drug discovery, the three-dimensional structure has not yet been experimentally solved. Therefore, accurately predicting their structure based on their amino acid sequence is one of the grant challenges in biology. Recently, AlphaFold2, a machine learning application based on a deep neural network, was able to predict unknown structures of proteins with an unprecedented accuracy. Despite the impressive progress made by AlphaFold2, nature still challenges the field of structure prediction. In this Perspective, we explore how AlphaFold2 and related methods help make drug design more efficient. Furthermore, we discuss the roles of predicting domain-domain orientations, all relevant conformational states, the influence of posttranslational modifications, and conformational changes due to protein binding partners. We highlight where further improvements are needed for advanced machine learning methods to be successfully and frequently used in the pharmaceutical industry.
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Affiliation(s)
- Michael Schauperl
- Department of Computational Sciences HotSpot Therapeutics 50 Milk Street, Boston, Massachusetts 02110, United States
| | - Rajiah Aldrin Denny
- Department of Computational Sciences HotSpot Therapeutics 50 Milk Street, Boston, Massachusetts 02110, United States
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25
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Gaudreault F, Corbeil CR, Purisima EO, Sulea T. Coevolved Canonical Loops Conformations of Single-Domain Antibodies: A Tale of Three Pockets Playing Musical Chairs. Front Immunol 2022; 13:884132. [PMID: 35720356 PMCID: PMC9203998 DOI: 10.3389/fimmu.2022.884132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/05/2022] [Indexed: 11/13/2022] Open
Abstract
Single-domain antibodies (sdAbs) are a promising class of biotherapeutics with unique structural traits within their paratope region. The distribution of canonical conformations explored by their complementarity determining region (CDR) loops differs to some extent from conventional two-chain Fv fragments of monoclonal antibodies (mAbs). In this study, we explored in detail the canonical structures of sdAb CDR-H1 and CDR-H2 loops and compared those with mAbs from the IGHV3 and IGHV1 gene families. We surveyed the antibody structures catalogued in SAbDab and clustered the CDR canonical loops in Cartesian space. While most of the sdAb clusters were sub-populations of previously defined canonical Fv conformations of CDR-H1 and CDR-H2, our stricter clustering approach defined narrower clusters in sequence-space. Meticulous visual inspection of sub-populations allowed a clearer understanding of sequence-structure relationships. The packing densities within structural pockets contacted by CDR-H1 and CDR-H2 canonical conformations were analyzed on the premise that these pockets cannot be left vacant as they would leave exposed supportive hydrophobic residues. The fine resolution of the canonical clusters defined here revealed unique signatures within these pockets, including distinct structural complementarities between CDR-H1 and CDR-H2 canonical clusters, which could not be perceived with the previous coarser clusters. We highlight examples where a single residue change in CDR-H1 sequence is sufficient to induce a dramatic population shift in CDR-H2 conformation. This suggests that preferences in combining CDR-H1 and CDR-H2 emerged naturally during antibody evolution, leading to preferred sets of conserved amino acids at key positions in the framework as well as within the CDR loops. We outline a game of musical chairs that is necessary to maintain the integrity of the antibody structures that arose during evolution. Our study also provides refined CDR-H1 and CDR-H2 structural templates for sdAb homology modeling that could be leveraged for improved antibody design.
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26
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Wong MTY, Kelm S, Liu X, Taylor RD, Baker T, Essex JW. Higher Affinity Antibodies Bind With Lower Hydration and Flexibility in Large Scale Simulations. Front Immunol 2022; 13:884110. [PMID: 35707541 PMCID: PMC9190259 DOI: 10.3389/fimmu.2022.884110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/22/2022] [Indexed: 11/25/2022] Open
Abstract
We have carried out a long-timescale simulation study on crystal structures of nine antibody-antigen pairs, in antigen-bound and antibody-only forms, using molecular dynamics with enhanced sampling and an explicit water model to explore interface conformation and hydration. By combining atomic level simulation and replica exchange to enable full protein flexibility, we find significant numbers of bridging water molecules at the antibody-antigen interface. Additionally, a higher proportion of interactions excluding bulk waters and a lower degree of antigen bound CDR conformational sampling are correlated with higher antibody affinity. The CDR sampling supports enthalpically driven antibody binding, as opposed to entropically driven, in that the difference between antigen bound and unbound conformations do not correlate with affinity. We thus propose that interactions with waters and CDR sampling are aspects of the interface that may moderate antibody-antigen binding, and that explicit hydration and CDR flexibility should be considered to improve antibody affinity prediction and computational design workflows.
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Affiliation(s)
- Mabel T. Y. Wong
- School of Chemistry, University of Southampton, Southampton, United Kingdom
| | | | | | | | | | - Jonathan W. Essex
- School of Chemistry, University of Southampton, Southampton, United Kingdom
- *Correspondence: Jonathan W. Essex,
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27
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Löhr T, Sormanni P, Vendruscolo M. Conformational Entropy as a Potential Liability of Computationally Designed Antibodies. Biomolecules 2022; 12:718. [PMID: 35625644 PMCID: PMC9138470 DOI: 10.3390/biom12050718] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 05/06/2022] [Accepted: 05/08/2022] [Indexed: 01/28/2023] Open
Abstract
In silico antibody discovery is emerging as a viable alternative to traditional in vivo and in vitro approaches. Many challenges, however, remain open to enabling the properties of designed antibodies to match those produced by the immune system. A major question concerns the structural features of computer-designed complementarity determining regions (CDRs), including the role of conformational entropy in determining the stability and binding affinity of the designed antibodies. To address this problem, we used enhanced-sampling molecular dynamics simulations to compare the free energy landscapes of single-domain antibodies (sdAbs) designed using structure-based (DesAb-HSA-D3) and sequence-based approaches (DesAbO), with that of a nanobody derived from llama immunization (Nb10). Our results indicate that the CDR3 of DesAbO is more conformationally heterogeneous than those of both DesAb-HSA-D3 and Nb10, and the CDR3 of DesAb-HSA-D3 is slightly more dynamic than that of Nb10, which is the original scaffold used for the design of DesAb-HSA-D3. These differences underline the challenges in the rational design of antibodies by revealing the presence of conformational substates likely to have different binding properties and to generate a high entropic cost upon binding.
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Affiliation(s)
| | | | - Michele Vendruscolo
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK; (T.L.); (P.S.)
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28
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Cao L, Coventry B, Goreshnik I, Huang B, Sheffler W, Park JS, Jude KM, Marković I, Kadam RU, Verschueren KHG, Verstraete K, Walsh STR, Bennett N, Phal A, Yang A, Kozodoy L, DeWitt M, Picton L, Miller L, Strauch EM, DeBouver ND, Pires A, Bera AK, Halabiya S, Hammerson B, Yang W, Bernard S, Stewart L, Wilson IA, Ruohola-Baker H, Schlessinger J, Lee S, Savvides SN, Garcia KC, Baker D. Design of protein-binding proteins from the target structure alone. Nature 2022; 605:551-560. [PMID: 35332283 PMCID: PMC9117152 DOI: 10.1038/s41586-022-04654-9] [Citation(s) in RCA: 152] [Impact Index Per Article: 76.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 03/15/2022] [Indexed: 12/03/2022]
Abstract
The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains a challenge1-5. Here we describe a general solution to this problem that starts with a broad exploration of the vast space of possible binding modes to a selected region of a protein surface, and then intensifies the search in the vicinity of the most promising binding modes. We demonstrate the broad applicability of this approach through the de novo design of binding proteins to 12 diverse protein targets with different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and, following experimental optimization, bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of five of the binder-target complexes, and all five closely match the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein-protein interactions, and should guide improvements of both. Our approach enables the targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications.
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Affiliation(s)
- Longxing Cao
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA
| | - Inna Goreshnik
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Buwei Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - William Sheffler
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Joon Sung Park
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
| | - Kevin M Jude
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Iva Marković
- VIB-UGent Center for Inflammation Research, Ghent, Belgium
- Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
| | - Rameshwar U Kadam
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Koen H G Verschueren
- VIB-UGent Center for Inflammation Research, Ghent, Belgium
- Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
| | - Kenneth Verstraete
- VIB-UGent Center for Inflammation Research, Ghent, Belgium
- Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
| | - Scott Thomas Russell Walsh
- Chemical Biology Laboratory, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
- J.A.M.E.S. Farm, Clarksville, MD, USA
| | - Nathaniel Bennett
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA
| | - Ashish Phal
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Aerin Yang
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Lisa Kozodoy
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Michelle DeWitt
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lora Picton
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Lauren Miller
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Eva-Maria Strauch
- Deptartment of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, GA, USA
| | - Nicholas D DeBouver
- UCB Pharma, Bainbridge Island, WA, USA
- Seattle Structural Genomics Center for Infectious Disease (SSGCID), Seattle, WA, USA
| | - Allison Pires
- Seattle Structural Genomics Center for Infectious Disease (SSGCID), Seattle, WA, USA
- Seattle Children's Center for Global Infectious Disease Research, Seattle, WA, USA
| | - Asim K Bera
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Samer Halabiya
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Bradley Hammerson
- Seattle Structural Genomics Center for Infectious Disease (SSGCID), Seattle, WA, USA
| | - Wei Yang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Steffen Bernard
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Lance Stewart
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Ian A Wilson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
- The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Hannele Ruohola-Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Joseph Schlessinger
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
| | - Sangwon Lee
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
| | - Savvas N Savvides
- VIB-UGent Center for Inflammation Research, Ghent, Belgium
- Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
| | - K Christopher Garcia
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
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29
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Hummer AM, Abanades B, Deane CM. Advances in computational structure-based antibody design. Curr Opin Struct Biol 2022; 74:102379. [PMID: 35490649 DOI: 10.1016/j.sbi.2022.102379] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/28/2022] [Accepted: 03/17/2022] [Indexed: 12/12/2022]
Abstract
Antibodies are currently the most important class of biotherapeutics and are used to treat numerous diseases. Recent advances in computational methods are ushering in a new era of antibody design, driven in part by accurate structure prediction. Previously, structure-based antibody design has been limited to a relatively small number of cases where accurate structures or models of both the target antigen and antibody were available. As we move towards a time where it is possible to accurately model most antibodies and antigens, and to reliably predict their binding site, there is vast potential for true computational antibody design. In this review, we describe the latest methods that promise to launch a paradigm shift towards entirely in silico structure-based antibody design.
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Affiliation(s)
- Alissa M Hummer
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, UK. https://twitter.com/@AlissaHummer
| | - Brennan Abanades
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, UK. https://twitter.com/@brennanaba
| | - Charlotte M Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
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30
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Khersonsky O, Fleishman SJ. What Have We Learned from Design of Function in Large Proteins? BIODESIGN RESEARCH 2022; 2022:9787581. [PMID: 37850148 PMCID: PMC10521758 DOI: 10.34133/2022/9787581] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 02/21/2022] [Indexed: 10/19/2023] Open
Abstract
The overarching goal of computational protein design is to gain complete control over protein structure and function. The majority of sophisticated binders and enzymes, however, are large and exhibit diverse and complex folds that defy atomistic design calculations. Encouragingly, recent strategies that combine evolutionary constraints from natural homologs with atomistic calculations have significantly improved design accuracy. In these approaches, evolutionary constraints mitigate the risk from misfolding and aggregation, focusing atomistic design calculations on a small but highly enriched sequence subspace. Such methods have dramatically optimized diverse proteins, including vaccine immunogens, enzymes for sustainable chemistry, and proteins with therapeutic potential. The new generation of deep learning-based ab initio structure predictors can be combined with these methods to extend the scope of protein design, in principle, to any natural protein of known sequence. We envision that protein engineering will come to rely on completely computational methods to efficiently discover and optimize biomolecular activities.
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Affiliation(s)
- Olga Khersonsky
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Sarel J. Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
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31
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Barkhordari F, Rismani E, Tabasinezhad M, Asgari S, Nematollahi L, Talebkhan Y. Computational analysis of fusion protein of anti-HER2 scFv and alpha luffin: A new immunotoxin protein for HER2 positive cancers. BRAZ J PHARM SCI 2022. [DOI: 10.1590/s2175-97902022e20527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
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32
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Vander Mause ER, Atanackovic D, Lim CS, Luetkens T. Roadmap to affinity-tuned antibodies for enhanced chimeric antigen receptor T cell function and selectivity. Trends Biotechnol 2022; 40:875-890. [DOI: 10.1016/j.tibtech.2021.12.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 12/16/2021] [Accepted: 12/17/2021] [Indexed: 12/29/2022]
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33
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Zahradník J, Dey D, Marciano S, Kolářová L, Charendoff CI, Subtil A, Schreiber G. A Protein-Engineered, Enhanced Yeast Display Platform for Rapid Evolution of Challenging Targets. ACS Synth Biol 2021; 10:3445-3460. [PMID: 34809429 PMCID: PMC8689690 DOI: 10.1021/acssynbio.1c00395] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Indexed: 02/08/2023]
Abstract
Here, we enhanced the popular yeast display method by multiple rounds of DNA and protein engineering. We introduced surface exposure-tailored reporters, eUnaG2 and DnbALFA, creating a new platform of C and N terminal fusion vectors. The optimization of eUnaG2 resulted in five times brighter fluorescence and 10 °C increased thermostability than UnaG. The optimized DnbALFA has 10-fold the level of expression of the starting protein. Following this, different plasmids were developed to create a complex platform allowing a broad range of protein expression organizations and labeling strategies. Our platform showed up to five times better separation between nonexpressing and expressing cells compared with traditional pCTcon2 and c-myc labeling, allowing for fewer rounds of selection and achieving higher binding affinities. Testing 16 different proteins, the enhanced system showed consistently stronger expression signals over c-myc labeling. In addition to gains in simplicity, speed, and cost-effectiveness, new applications were introduced to monitor protein surface exposure and protein retention in the secretion pathway that enabled successful protein engineering of hard-to-express proteins. As an example, we show how we optimized the WD40 domain of the ATG16L1 protein for yeast surface and soluble bacterial expression, starting from a nonexpressing protein. As a second example, we show how using the here-presented enhanced yeast display method we rapidly selected high-affinity binders toward two protein targets, demonstrating the simplicity of generating new protein-protein interactions. While the methodological changes are incremental, it results in a qualitative enhancement in the applicability of yeast display for many applications.
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Affiliation(s)
- Jiří Zahradník
- Weizmann
Institute of Science, Herzl St. 234, Rehovot 7610001, Israel
| | - Debabrata Dey
- Weizmann
Institute of Science, Herzl St. 234, Rehovot 7610001, Israel
| | - Shir Marciano
- Weizmann
Institute of Science, Herzl St. 234, Rehovot 7610001, Israel
| | - Lucie Kolářová
- Institute
of Biotechnology, CAS v.v.i., Prumyslova 595, Vestec 252 50 Prague region, Czech Republic
| | - Chloé I. Charendoff
- Institut
Pasteur, Unité de Biologie cellulaire de l’infection
microbienne, 25 rue du Dr Roux, Paris 75015, France
| | - Agathe Subtil
- Institut
Pasteur, Unité de Biologie cellulaire de l’infection
microbienne, 25 rue du Dr Roux, Paris 75015, France
| | - Gideon Schreiber
- Weizmann
Institute of Science, Herzl St. 234, Rehovot 7610001, Israel
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34
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Tomar DS, Licari G, Bauer J, Singh SK, Li L, Kumar S. Stress-dependent flexibility of a full-length human monoclonal antibody: Insights from molecular dynamics to support biopharmaceutical development. J Pharm Sci 2021; 111:628-637. [PMID: 34742728 DOI: 10.1016/j.xphs.2021.10.039] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/30/2021] [Accepted: 10/30/2021] [Indexed: 01/15/2023]
Abstract
After several decades of advancements in drug discovery, product development of biopharmaceuticals remains a time- and resource-consuming endeavor. One of the main reasons is associated to the lack of fundamental understanding of conformational dynamics of such biologic entities, and how they respond to various stresses encountered during manufacturing. In this work, we have studied the conformational dynamics of human IgG1κ b12 monoclonal antibody (mAb) using molecular dynamics simulations. The hundreds of nanoseconds long trajectories reveal that b12 mAb is highly flexible. Its variable domains show greater conformational fluctuations than the constant domains. Additionally, it collapses towards a more globular shape in response to thermal stress, leading to decrease in the total solvent exposed surface area and radius of gyration. This behavior is more pronounced for the deglycosylated b12 mAb, and it appears to correlate with increase in inter-domain contacts between specific regions of the antibody. Conformational fluctuations also cause temporary formation and disruption of hydrophobic and charged patches on the antibody surface, which is particularly important for the prediction of CMC properties during development phases of antibody-based biotherapeutics. The insights gained through these simulations may help the development of biologic drugs, especially with regards to manufacturing processes where antibodies may undergo significant thermal stress.
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Affiliation(s)
- Dheeraj S Tomar
- Biotherapeutics Pharmaceutical Sciences Research and Development, Pfizer Inc., 700 Chesterfield Parkway West, Chesterfield, MO, 63017, USA
| | - Giuseppe Licari
- Pharmaceuticals Development Biologicals, Boehringer Ingelheim Pharmaceuticals, Inc., D-88397 Biberach an der Riss, Germany
| | - Joschka Bauer
- Pharmaceuticals Development Biologicals, Boehringer Ingelheim Pharmaceuticals, Inc., D-88397 Biberach an der Riss, Germany
| | - Satish K Singh
- Biotherapeutics Pharmaceutical Sciences Research and Development, Pfizer Inc., 700 Chesterfield Parkway West, Chesterfield, MO, 63017, USA
| | - Li Li
- Biotherapeutics Pharmaceutical Sciences Research and Development, Pfizer Inc., 1 Burtt Road, Andover, Massachusetts, 01810, USA
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals, Inc., 900 Ridgebury Road, Ridgefield, CT 06877.
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35
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Redesigning an antibody H3 loop by virtual screening of a small library of human germline-derived sequences. Sci Rep 2021; 11:21362. [PMID: 34725391 PMCID: PMC8560851 DOI: 10.1038/s41598-021-00669-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 10/05/2021] [Indexed: 01/01/2023] Open
Abstract
The design of superior biologic therapeutics, including antibodies and engineered proteins, involves optimizing their specific ability to bind to disease-related molecular targets. Previously, we developed and applied the Assisted Design of Antibody and Protein Therapeutics (ADAPT) platform for virtual affinity maturation of antibodies (Vivcharuk et al. in PLoS One 12(7):e0181490, 10.1371/journal.pone.0181490, 2017). However, ADAPT is limited to point mutations of hot-spot residues in existing CDR loops. In this study, we explore the possibility of wholesale replacement of the entire H3 loop with no restriction to maintain the parental loop length. This complements other currently published studies that sample replacements for the CDR loops L1, L2, L3, H1 and H2. Given the immense sequence space theoretically available to H3, we focused on the virtual grafting of over 5000 human germline-derived H3 sequences from the IGMT/LIGM database increasing the diversity of the sequence space when compared to using crystalized H3 loop sequences. H3 loop conformations are generated and scored to identify optimized H3 sequences. Experimental testing of high-ranking H3 sequences grafted into the framework of the bH1 antibody against human VEGF-A led to the discovery of multiple hits, some of which had similar or better affinities relative to the parental antibody. In over 75% of the tested designs, the re-designed H3 loop contributed favorably to overall binding affinity. The hits also demonstrated good developability attributes such as high thermal stability and no aggregation. Crystal structures of select re-designed H3 variants were solved and indicated that although some deviations from predicted structures were seen in the more solvent accessible regions of the H3 loop, they did not significantly affect predicted affinity scores.
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36
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Vimer S, Ben-Nissan G, Marty M, Fleishman SJ, Sharon M. Direct-MS analysis of antibody-antigen complexes. Proteomics 2021; 21:e2000300. [PMID: 34310051 PMCID: PMC8595693 DOI: 10.1002/pmic.202000300] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 07/04/2021] [Accepted: 07/15/2021] [Indexed: 11/05/2022]
Abstract
In recent decades, antibodies (Abs) have attracted the attention of academia and the biopharmaceutical industry due to their therapeutic properties and versatility in binding a vast spectrum of antigens. Different engineering strategies have been developed for optimizing Ab specificity, efficacy, affinity, stability and production, enabling systematic screening and analysis procedures for selecting lead candidates. This quality assessment is critical but usually demands time-consuming and labor-intensive purification procedures. Here, we harnessed the direct-mass spectrometry (direct-MS) approach, in which the analysis is carried out directly from the crude growth media, for the rapid, structural characterization of designed Abs. We demonstrate that properties such as stability, specificity and interactions with antigens can be defined, without the need for prior purification.
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Affiliation(s)
- Shay Vimer
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Gili Ben-Nissan
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Michael Marty
- Department of Chemistry & Biochemistry, University of Arizona, Tucson, AZ, USA
| | - Sarel J. Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Michal Sharon
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
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37
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Lengfeld J, Zhang H, Stoesz S, Murali R, Pass F, Greene MI, Goel PN, Grover P. Challenges in Detection of Serum Oncoprotein: Relevance to Breast Cancer Diagnostics. BREAST CANCER-TARGETS AND THERAPY 2021; 13:575-593. [PMID: 34703307 PMCID: PMC8524259 DOI: 10.2147/bctt.s331844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/02/2021] [Indexed: 11/23/2022]
Abstract
Breast cancer is a highly prevalent malignancy that shows improved outcomes with earlier diagnosis. Current screening and monitoring methods have improved survival rates, but the limitations of these approaches have led to the investigation of biomarker evaluation to improve early diagnosis and treatment monitoring. The enzyme-linked immunosorbent assay (ELISA) is a specific and robust technique ideally suited for the quantification of protein biomarkers from blood or its constituents. The continued clinical relevancy of this assay format will require overcoming specific technical challenges, including the ultra-sensitive detection of trace biomarkers and the circumventing of potential assay interference due to the expanding use of monoclonal antibody (mAb) therapeutics. Approaches to increasing the sensitivity of ELISA have been numerous and include employing more sensitive substrates, combining ELISA with the polymerase chain reaction (PCR), and incorporating nanoparticles as shuttles for detection antibodies and enzymes. These modifications have resulted in substantial boosts in the ability to detect extremely low levels of protein biomarkers, with some systems reliably detecting antigen at sub-femtomolar concentrations. Extensive utilization of mAb therapies in oncology has presented an additional contemporary challenge for ELISA, particularly when both therapeutic and assay antibodies target the same protein antigen. Resolution of issues such as epitope overlap and steric hindrance requires a rational approach to the design of diagnostic antibodies that takes advantage of modern antibody generation pipelines, epitope binning techniques and computational methods to strategically target biomarker epitopes. This review discusses technical strategies in ELISA implemented to date and their feasibility to address current constraints on sensitivity and problems with interference in the clinical setting. The impact of these recent advancements will depend upon their transformation from research laboratory protocols into facile, reliable detection systems that can ideally be replicated in point-of-care devices to maximize utilization and transform both the diagnostic and therapeutic monitoring landscape.
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Affiliation(s)
- Justin Lengfeld
- Martell Diagnostic Laboratories, Inc., Roseville, MN, 55113, USA
| | - Hongtao Zhang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Steven Stoesz
- Martell Diagnostic Laboratories, Inc., Roseville, MN, 55113, USA
| | - Ramachandran Murali
- Department of Biomedical Sciences, Research Division of Immunology; Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Franklin Pass
- Martell Diagnostic Laboratories, Inc., Roseville, MN, 55113, USA
| | - Mark I Greene
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Peeyush N Goel
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Payal Grover
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
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38
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Ikeuchi E, Kuroda D, Nakakido M, Murakami A, Tsumoto K. Delicate balance among thermal stability, binding affinity, and conformational space explored by single-domain V HH antibodies. Sci Rep 2021; 11:20624. [PMID: 34663870 PMCID: PMC8523659 DOI: 10.1038/s41598-021-98977-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 09/08/2021] [Indexed: 11/29/2022] Open
Abstract
The high binding affinities and specificities of antibodies have led to their use as drugs and biosensors. Single-domain VHH antibodies exhibit high specificity and affinity but have higher stability and solubility than conventional antibodies as they are single-domain proteins. In this work, based on physicochemical measurements and molecular dynamics (MD) simulations, we have gained insight that will facilitate rational design of single-chain VHH antibodies. We first assessed two homologous VHH antibodies by differential scanning calorimetry (DSC); one had a high (64.8 °C) and the other a low (58.6 °C) melting temperature. We then generated a series of the variants of the low stability antibody and analyzed their thermal stabilities by DSC and characterized their structures through MD simulations. We found that a single mutation that resulted in 8.2 °C improvement in melting temperature resulted in binding affinity an order of magnitude lower than the parent antibody, likely due to a shift of conformational space explored by the single-chain VHH antibody. These results suggest that the delicate balance among conformational stability, binding capability, and conformational space explored by antibodies must be considered in design of fully functional single-chain VHH antibodies.
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Affiliation(s)
- Emina Ikeuchi
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, 108-8639, Japan.,Panasonic Corporation Technology Division, Kyoto, 619-0237, Japan
| | - Daisuke Kuroda
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, 108-8639, Japan.,Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, Tokyo, 108-8639, Japan.,Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Makoto Nakakido
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, 108-8639, Japan.,Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Akikazu Murakami
- Department of Parasitology and Immunopathoetiology, Graduate School of Medicine, University of the Ryukyus, Okinawa, 903-0215, Japan
| | - Kouhei Tsumoto
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, 108-8639, Japan. .,Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, Tokyo, 108-8639, Japan. .,Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan. .,Laboratory of Medical Proteomics, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan.
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39
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Rangnoi K, Rüker F, Wozniak-Knopp G, Cvak B, O’Kennedy R, Yamabhai M. Binding Characteristic of Various Antibody Formats Against Aflatoxins. ACS OMEGA 2021; 6:25258-25268. [PMID: 34632185 PMCID: PMC8495687 DOI: 10.1021/acsomega.1c03044] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 09/10/2021] [Indexed: 06/13/2023]
Abstract
The application of recombinant antibodies for the analysis of foods and food contaminants is now a major focus, given their capacity to be engineered to tailor their specificity, enhance their stability, and modify their structural formats to fit the desired analytical platform. In this study, human scFv antibody fragments generated against aflatoxin B1 (AFB1) were selected as the model antibody to explore the effect of antibody formats on their binding activity and to evaluate their potential use as immunoreagents for food contaminant analysis. Four human scFv antibody fragments against aflatoxin B1 (AFB1), previously isolated and engineered by chain shuffling, were converted into various formats, that is, scFv-AP fusions, scFv-Fc, and whole IgG molecules. The result indicated that the effects of the antibody format on the binding property varied, depending on the sequence of scFv. For all of the scFv clones, the scFv-AP fusion format showed the highest sensitivity by competitive ELISA, while the effects on the binding activity after conversion to scFv-Fc or IgG format varied, depending on the amino acid sequence of the antibodies. The sAFH-3e3 antibodies that showed the best performance by competitive ELISA were selected for further investigation. The sAFH-3e3 was converted to the scFv-GFP format and tested by fluorescence-linked immunosorbent assay (FLISA), which showed that its binding property was equivalent to those of scFv-Fc and IgG formats. The potential applications of the sAFH-3e3 in a rapid test kit format based on ELISA (scFv-AP) and in a lateral flow immunochromatography assay (LFIA) (IgG) were demonstrated. A comparison of methods for the extraction of AFB1 from matrices for use with these assay formats indicated that PBS and TBST are better than 70% methanol.
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Affiliation(s)
- Kuntalee Rangnoi
- Molecular
Biotechnology Laboratory, School of Biotechnology, Institute of Agriculture
Technology, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Florian Rüker
- Institute
of Molecular Biotechnology, Department of Biotechnology, University of Natural Resources and Life Sciences,
Vienna (BOKU), Muthgasse 18, Vienna A-1190, Austria
| | - Gordana Wozniak-Knopp
- Institute
of Molecular Biotechnology, Department of Biotechnology, University of Natural Resources and Life Sciences,
Vienna (BOKU), Muthgasse 18, Vienna A-1190, Austria
| | - Barbara Cvak
- Romer
Labs Division Holding GmbH, Technopark 5, Tulln 3430, Austria
| | - Richard O’Kennedy
- School
of Biotechnology and National Centre for Sensor Research, Dublin City University, Dublin 9 D09 DX63, Ireland
| | - Montarop Yamabhai
- Molecular
Biotechnology Laboratory, School of Biotechnology, Institute of Agriculture
Technology, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
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40
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Computational and Rational Design of Single-Chain Antibody against Tick-Borne Encephalitis Virus for Modifying Its Specificity. Viruses 2021; 13:v13081494. [PMID: 34452359 PMCID: PMC8402911 DOI: 10.3390/v13081494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/09/2021] [Accepted: 06/23/2021] [Indexed: 12/27/2022] Open
Abstract
Tick-borne encephalitis virus (TBEV) causes 5−7 thousand cases of human meningitis and encephalitis annually. The neutralizing and protective antibody ch14D5 is a potential therapeutic agent. This antibody exhibits a high affinity for binding with the D3 domain of the glycoprotein E of the Far Eastern subtype of the virus, but a lower affinity for the D3 domains of the Siberian and European subtypes. In this study, a 2.2-fold increase in the affinity of single-chain antibody sc14D5 to D3 proteins of the Siberian and European subtypes of the virus was achieved using rational design and computational modeling. This improvement can be further enhanced in the case of the bivalent binding of the full-length chimeric antibody containing the identified mutation.
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41
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Rezaei S, Sefidbakht Y, Uskoković V. Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design. Brief Bioinform 2021; 22:6313266. [PMID: 34219142 DOI: 10.1093/bib/bbab241] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 04/23/2021] [Accepted: 06/04/2021] [Indexed: 12/23/2022] Open
Abstract
With the onset of the COVID-19 pandemic, the amount of data on genomic and proteomic sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stored in various databases has exponentially grown. A large volume of these data has led to the production of equally immense sets of immunological data, which require rigorous computational approaches to sort through and make sense of. Immunoinformatics has emerged in the recent decades as a field capable of offering this approach by bridging experimental and theoretical immunology with state-of-the-art computational tools. Here, we discuss how immunoinformatics can assist in the development of high-performance vaccines and drug discovery needed to curb the spread of SARS-CoV-2. Immunoinformatics can provide a set of computational tools to extract meaningful connections from the large sets of COVID-19 patient data, which can be implemented in the design of effective vaccines. With this in mind, we represent a pipeline to identify the role of immunoinformatics in COVID-19 treatment and vaccine development. In this process, a number of free databases of protein sequences, structures and mutations are introduced, along with docking web servers for assessing the interaction between antibodies and the SARS-CoV-2 spike protein segments as most commonly considered antigens in vaccine design.
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Affiliation(s)
- Shokouh Rezaei
- Protein Research Center at Shahid Beheshti University, Tehran, Iran
| | - Yahya Sefidbakht
- Protein Research Center at Shahid Beheshti University, Tehran, Iran
| | - Vuk Uskoković
- Founder of the biotech startup, TardigradeNano, and formerly a Professor at University of Illinois in Chicago, Chapman University, and University of California in Irvine
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42
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Affiliation(s)
- Amir Aharoni
- Department of Life Sciences Ben‐Gurion University of the Negev Be’er Sheva Israel
| | - Sarel J. Fleishman
- Department of Biomolecular Sciences Weizmann Institute of Science Rehovot Israel
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43
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Lee DCP, Raman R, Ghafar NA, Budigi Y. An antibody engineering platform using amino acid networks: A case study in development of antiviral therapeutics. Antiviral Res 2021; 192:105105. [PMID: 34111505 DOI: 10.1016/j.antiviral.2021.105105] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 05/27/2021] [Accepted: 05/28/2021] [Indexed: 11/29/2022]
Abstract
We present here a case study of an antibody-engineering platform that selects, modifies, and assembles antibody parts to construct novel antibodies. A salient feature of this platform includes the role of amino acid networks in optimizing framework regions (FRs) and complementarity determining regions (CDRs) to engineer new antibodies with desired structure-function relationships. The details of this approach are described in the context of its utility in engineering ZAb_FLEP, a potent anti-Zika virus antibody. ZAb_FLEP comprises of distinct parts, including heavy chain and light chain FRs and CDRs, with engineered features such as loop lengths and optimal epitope-paratope contacts. We demonstrate, with different test antibodies derived from different FR-CDR combinations, that despite these test antibodies sharing high overall sequence similarity, they yield diverse functional readouts. Furthermore, we show that strategies relying on one dimensional sequence similarity-based analyses of antibodies miss important structural nuances of the FR-CDR relationship, which is effectively addressed by the amino acid networks approach of this platform.
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Affiliation(s)
| | - Rahul Raman
- Department of Biological Engineering, And Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
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44
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Kasahara K, Kuroda D, Tanabe A, Kawade R, Nagatoishi S, Tsumoto K. Anion solvation enhanced by positive supercharging mutations preserves thermal stability of an antibody in a wide pH range. Biochem Biophys Res Commun 2021; 563:54-59. [PMID: 34058475 DOI: 10.1016/j.bbrc.2021.05.053] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 05/16/2021] [Indexed: 12/27/2022]
Abstract
Proteins function through interactions with other molecules. In protein engineering, scientists often engineer proteins by mutating their amino acid sequences on the protein surface to improve various physicochemical properties. "Supercharging" is a method to design proteins by mutating surface residues with charged amino acids. Previous studies demonstrated that supercharging mutations conferred better thermal resistance, solubility, and cell penetration to proteins. Likewise, antibodies recognize antigens through the antigen-binding site on the surface. The genetic and structural diversity of antibodies leads to high specificity and affinity toward antigens, enabling antibodies to be versatile tools in various applications. When assessing therapeutic antibodies, surface charge is an important factor to consider because the isoelectric point plays a role in protein clearance inside the body. In this study, we explored how supercharging mutations affect physicochemical properties of antibodies. Starting from a crystal structure of an antibody with the net charge of -4, we computationally designed a supercharged variant possessing the net charge of +10. The positive-supercharged antibody exhibited marginal improvement in thermal stability, but the secondary structure and the binding affinity to the antigen (net charge of +8) were preserved. We also used physicochemical measurements and molecular dynamics simulations to analyze the effects of supercharging mutations in sodium phosphate buffer with different pH and ion concentrations, which revealed preferential solvation of phosphate ions to the supercharged surface relative to the wild-type surface. These results suggest that supercharging would be a useful approach to preserving thermal stability of antibodies in a wide range of pH, which may enable further diversification of antibody repertoires beyond natural evolution.
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Affiliation(s)
- Keisuke Kasahara
- Department of Bioengineering, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
| | - Daisuke Kuroda
- Department of Bioengineering, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan; Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan; Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.
| | - Aki Tanabe
- Department of Bioengineering, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
| | - Raiji Kawade
- Department of Bioengineering, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
| | - Satoru Nagatoishi
- The Institute of Medical Science, The University of Tokyo, 4-6-1, Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Kouhei Tsumoto
- Department of Bioengineering, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan; Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan; Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan; The Institute of Medical Science, The University of Tokyo, 4-6-1, Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.
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45
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Frappier V, Keating AE. Data-driven computational protein design. Curr Opin Struct Biol 2021; 69:63-69. [PMID: 33910104 DOI: 10.1016/j.sbi.2021.03.009] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 03/18/2021] [Accepted: 03/19/2021] [Indexed: 01/28/2023]
Abstract
Computational protein design can generate proteins not found in nature that adopt desired structures and perform novel functions. Although proteins could, in theory, be designed with ab initio methods, practical success has come from using large amounts of data that describe the sequences, structures, and functions of existing proteins and their variants. We present recent creative uses of multiple-sequence alignments, protein structures, and high-throughput functional assays in computational protein design. Approaches range from enhancing structure-based design with experimental data to building regression models to training deep neural nets that generate novel sequences. Looking ahead, deep learning will be increasingly important for maximizing the value of data for protein design.
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Affiliation(s)
- Vincent Frappier
- Generate Biomedicines, 26 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Amy E Keating
- MIT Departments of Biology and Biological Engineering, 77 Massachusetts Ave., Cambridge, MA, 02139, USA.
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46
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Jin Y, Vadukul DM, Gialama D, Ge Y, Thrush R, White JT, Aprile FA. The Diagnostic Potential of Amyloidogenic Proteins. Int J Mol Sci 2021; 22:4128. [PMID: 33923609 PMCID: PMC8074075 DOI: 10.3390/ijms22084128] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/12/2021] [Accepted: 04/13/2021] [Indexed: 12/12/2022] Open
Abstract
Neurodegenerative disorders are a highly prevalent class of diseases, whose pathological mechanisms start before the appearance of any clear symptoms. This fact has prompted scientists to search for biomarkers that could aid early treatment. These currently incurable pathologies share the presence of aberrant aggregates called amyloids in the nervous system, which are composed of specific proteins. In this review, we discuss how these proteins, their conformations and modifications could be exploited as biomarkers for diagnostic purposes. We focus on proteins that are associated with the most prevalent neurodegenerative disorders, including Alzheimer's and Parkinson's diseases, amyotrophic lateral sclerosis, and frontotemporal dementia. We also describe current challenges in detection, the most recent techniques with diagnostic potentials and possible future developments in diagnosis.
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Affiliation(s)
| | | | | | | | | | | | - Francesco Antonio Aprile
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, UK; (Y.J.); (D.M.V.); (D.G.); (Y.G.); (R.T.); (J.T.W.)
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47
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Pertseva M, Gao B, Neumeier D, Yermanos A, Reddy ST. Applications of Machine and Deep Learning in Adaptive Immunity. Annu Rev Chem Biomol Eng 2021; 12:39-62. [PMID: 33852352 DOI: 10.1146/annurev-chembioeng-101420-125021] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs), can recognize and respond to pathogens and diseased cells. In recent years, advances in deep sequencing have led to a massive increase in the amount of adaptive immune receptor repertoire data; additionally, proteomics techniques have led to a wealth of data on peptide-MHC presentation. These large-scale data sets are now making it possible to train machine and deep learning models, which can be used to identify complex and high-dimensional patterns in immune repertoires. This article introduces adaptive immune repertoires and machine and deep learning related to biological sequence data and then summarizes the many applications in this field, which span from predicting the immunological status of a host to the antigen specificity of individual receptors and the engineering of immunotherapeutics.
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Affiliation(s)
- Margarita Pertseva
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; .,Life Science Zurich Graduate School, ETH Zurich and University of Zurich, 8006 Zurich, Switzerland
| | - Beichen Gao
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland;
| | - Daniel Neumeier
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland;
| | - Alexander Yermanos
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; .,Department of Pathology and Immunology, University of Geneva, 1205 Geneva, Switzerland.,Department of Biology, Institute of Microbiology and Immunology, ETH Zurich, 8093 Zurich, Switzerland
| | - Sai T Reddy
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland;
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48
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Direct single-molecule imaging for diagnostic and blood screening assays. Proc Natl Acad Sci U S A 2021; 118:2025033118. [PMID: 33790018 DOI: 10.1073/pnas.2025033118] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Every year, over 100 million units of donated blood undergo mandatory screening for HIV, hepatitis B, hepatitis C, and syphilis worldwide. Often, donated blood is also screened for human T cell leukemia-lymphoma virus, Chagas, dengue, Babesia, cytomegalovirus, malaria, and other infections. Several billion diagnostic tests are performed annually around the world to measure more than 400 biomarkers for cardiac, cancer, infectious, and other diseases. Considering such volumes, every improvement in assay performance and/or throughput has a major impact. Here, we show that medically relevant assay sensitivities and specificities can be fundamentally improved by direct single-molecule imaging using regular epifluorescence microscopes. In current microparticle-based assays, an ensemble of bound signal-generating molecules is measured as a whole. By contrast, we acquire intensity profiles to identify and then count individual fluorescent complexes bound to targets on antibody-coated microparticles. This increases the signal-to-noise ratio and provides better discrimination over nonspecific effects. It brings the detection sensitivity down to the attomolar (10-18 M) for model assay systems and to the low femtomolar (10-16 M) for measuring analyte in human plasma. Transitioning from counting single-molecule peaks to averaging pixel intensities at higher analyte concentrations enables a continuous linear response from 10-18 to 10-5 M. Additionally, our assays are insensitive to microparticle number and volume variations during the binding reaction, eliminating the main source of uncertainties in standard assays. Altogether, these features allow for increased assay sensitivity, wide linear detection ranges, shorter incubation times, simpler assay protocols, and minimal reagent consumption.
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49
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Schoeder C, Schmitz S, Adolf-Bryfogle J, Sevy AM, Finn JA, Sauer MF, Bozhanova NG, Mueller BK, Sangha AK, Bonet J, Sheehan JH, Kuenze G, Marlow B, Smith ST, Woods H, Bender BJ, Martina CE, del Alamo D, Kodali P, Gulsevin A, Schief WR, Correia BE, Crowe JE, Meiler J, Moretti R. Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design. Biochemistry 2021; 60:825-846. [PMID: 33705117 PMCID: PMC7992133 DOI: 10.1021/acs.biochem.0c00912] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 03/02/2021] [Indexed: 01/16/2023]
Abstract
Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.
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Affiliation(s)
- Clara
T. Schoeder
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Samuel Schmitz
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Jared Adolf-Bryfogle
- Department
of Immunology and Microbiology, The Scripps
Research Institute, La Jolla, California 92037, United States
- IAVI
Neutralizing Antibody Center, The Scripps
Research Institute, La Jolla, California 92037, United States
| | - Alexander M. Sevy
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
| | - Jessica A. Finn
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
- Department
of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Marion F. Sauer
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
| | - Nina G. Bozhanova
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Benjamin K. Mueller
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Amandeep K. Sangha
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Jaume Bonet
- Institute
of Bioengineering, École Polytechnique
Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Jonathan H. Sheehan
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Georg Kuenze
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Institute
for Drug Discovery, University Leipzig Medical
School, 04103 Leipzig, Germany
| | - Brennica Marlow
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Shannon T. Smith
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Hope Woods
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Brian J. Bender
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37212, United States
| | - Cristina E. Martina
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Diego del Alamo
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Pranav Kodali
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Alican Gulsevin
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - William R. Schief
- Department
of Immunology and Microbiology, The Scripps
Research Institute, La Jolla, California 92037, United States
- IAVI
Neutralizing Antibody Center, The Scripps
Research Institute, La Jolla, California 92037, United States
| | - Bruno E. Correia
- Institute
of Bioengineering, École Polytechnique
Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - James E. Crowe
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
- Department
of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
- Department
of Pediatrics, Vanderbilt University Medical
Center, Nashville, Tennessee 37232, United States
| | - Jens Meiler
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Institute
for Drug Discovery, University Leipzig Medical
School, 04103 Leipzig, Germany
| | - Rocco Moretti
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
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Xiang Y, Sang Z, Bitton L, Xu J, Liu Y, Schneidman-Duhovny D, Shi Y. Integrative proteomics identifies thousands of distinct, multi-epitope, and high-affinity nanobodies. Cell Syst 2021; 12:220-234.e9. [PMID: 33592195 PMCID: PMC7979497 DOI: 10.1016/j.cels.2021.01.003] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 10/13/2020] [Accepted: 01/20/2021] [Indexed: 12/15/2022]
Abstract
The antibody immune response is essential for the survival of mammals. However, we still lack a systematic understanding of the antibody repertoire. Here, we developed a proteomic strategy to survey, at an unprecedented scale, the landscape of antigen-engaged, circulating camelid heavy-chain antibodies, whose minimal binding fragments are called VHH antibodies or nanobodies. The sensitivity and robustness of this approach were validated with three antigens spanning orders of magnitude in immune responses; thousands of distinct, high-affinity nanobody families were reliably identified and quantified. Using high-throughput structural modeling, cross-linking mass spectrometry, mutagenesis, and deep learning, we mapped and analyzed the epitopes of >100,000 antigen-nanobody complexes. Our results revealed a surprising diversity of ultrahigh-affinity camelid nanobodies for specific antigen binding on various dominant epitope clusters. Nanobodies utilize both shape and charge complementarity to enable highly selective antigen binding. Interestingly, we found that nanobody-antigen binding can mimic conserved intracellular protein-protein interactions. A record of this paper's Transparent Peer Review process is included in the Supplemental information.
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Affiliation(s)
- Yufei Xiang
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zhe Sang
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, USA; University of Pittsburgh, Carnegie Mellon University Program for Computational Biology, Pittsburgh, PA, USA
| | - Lirane Bitton
- School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel
| | - Jianquan Xu
- Departments of Medicine and Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yang Liu
- Departments of Medicine and Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Dina Schneidman-Duhovny
- School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel.
| | - Yi Shi
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, USA; University of Pittsburgh, Carnegie Mellon University Program for Computational Biology, Pittsburgh, PA, USA.
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