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Burman SSR, Nance ML, Jeliazkov JR, Labonte JW, Lubin JH, Biswas N, Gray JJ. Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI rounds 37-45. Proteins 2020; 88:973-985. [PMID: 31742764 PMCID: PMC8589291 DOI: 10.1002/prot.25855] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 11/04/2019] [Accepted: 11/13/2019] [Indexed: 02/06/2023]
Abstract
Critical Assessment of PRediction of Interactions (CAPRI) rounds 37 through 45 introduced larger complexes, new macromolecules, and multistage assemblies. For these rounds, we used and expanded docking methods in Rosetta to model 23 target complexes. We successfully predicted 14 target complexes and recognized and refined near-native models generated by other groups for two further targets. Notably, for targets T110 and T136, we achieved the closest prediction of any CAPRI participant. We created several innovative approaches during these rounds. Since round 39 (target 122), we have used the new RosettaDock 4.0, which has a revamped coarse-grained energy function and the ability to perform conformer selection during docking with hundreds of pregenerated protein backbones. Ten of the complexes had some degree of symmetry in their interactions, so we tested Rosetta SymDock, realized its shortcomings, and developed the next-generation symmetric docking protocol, SymDock2, which includes docking of multiple backbones and induced-fit refinement. Since the last CAPRI assessment, we also developed methods for modeling and designing carbohydrates in Rosetta, and we used them to successfully model oligosaccharide-protein complexes in round 41. Although the results were broadly encouraging, they also highlighted the pressing need to invest in (a) flexible docking algorithms with the ability to model loop and linker motions and in (b) new sampling and scoring methods for oligosaccharide-protein interactions.
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Affiliation(s)
- Shourya S. Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Morgan L. Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland
| | | | - Jason W. Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Joseph H. Lubin
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Naireeta Biswas
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
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52
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Teraguchi S, Saputri DS, Llamas-Covarrubias MA, Davila A, Diez D, Nazlica SA, Rozewicki J, Ismanto HS, Wilamowski J, Xie J, Xu Z, Loza-Lopez MDJ, van Eerden FJ, Li S, Standley DM. Methods for sequence and structural analysis of B and T cell receptor repertoires. Comput Struct Biotechnol J 2020; 18:2000-2011. [PMID: 32802272 PMCID: PMC7366105 DOI: 10.1016/j.csbj.2020.07.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/08/2020] [Accepted: 07/08/2020] [Indexed: 02/07/2023] Open
Abstract
B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.
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Affiliation(s)
- Shunsuke Teraguchi
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Dianita S. Saputri
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Mara Anais Llamas-Covarrubias
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Departamento de Biología Molecular y Genómica, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Mexico
| | - Ana Davila
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Diego Diez
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Sedat Aybars Nazlica
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - John Rozewicki
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Hendra S. Ismanto
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Jan Wilamowski
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Jiaqi Xie
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Zichang Xu
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | | | - Floris J. van Eerden
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Songling Li
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Daron M. Standley
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
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Myung Y, Pires DEV, Ascher DB. mmCSM-AB: guiding rational antibody engineering through multiple point mutations. Nucleic Acids Res 2020; 48:W125-W131. [PMID: 32432715 PMCID: PMC7319589 DOI: 10.1093/nar/gkaa389] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 04/18/2020] [Accepted: 05/16/2020] [Indexed: 12/15/2022] Open
Abstract
While antibodies are becoming an increasingly important therapeutic class, especially in personalized medicine, their development and optimization has been largely through experimental exploration. While there have been many efforts to develop computational tools to guide rational antibody engineering, most approaches are of limited accuracy when applied to antibody design, and have largely been limited to analysing a single point mutation at a time. To overcome this gap, we have curated a dataset of 242 experimentally determined changes in binding affinity upon multiple point mutations in antibody-target complexes (89 increasing and 153 decreasing binding affinity). Here, we have shown that by using our graph-based signatures and atomic interaction information, we can accurately analyse the consequence of multi-point mutations on antigen binding affinity. Our approach outperformed other available tools across cross-validation and two independent blind tests, achieving Pearson's correlations of up to 0.95. We have implemented our new approach, mmCSM-AB, as a web-server that can help guide the process of affinity maturation in antibody design. mmCSM-AB is freely available at http://biosig.unimelb.edu.au/mmcsm_ab/.
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Affiliation(s)
- Yoochan Myung
- Computational Biology and Clinical Informatics, Baker Institute, Melbourne, VIC 3004, Australia
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia
| | - Douglas E V Pires
- Computational Biology and Clinical Informatics, Baker Institute, Melbourne, VIC 3004, Australia
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia
- School of Computing and Information Systems, University of Melbourne, Parkville, VIC 3052, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Institute, Melbourne, VIC 3004, Australia
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
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Pittala S, Bailey-Kellogg C. Learning context-aware structural representations to predict antigen and antibody binding interfaces. Bioinformatics 2020; 36:3996-4003. [PMID: 32321157 PMCID: PMC7332568 DOI: 10.1093/bioinformatics/btaa263] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Revised: 04/10/2020] [Accepted: 04/15/2020] [Indexed: 01/19/2023] Open
Abstract
MOTIVATION Understanding how antibodies specifically interact with their antigens can enable better drug and vaccine design, as well as provide insights into natural immunity. Experimental structural characterization can detail the 'ground truth' of antibody-antigen interactions, but computational methods are required to efficiently scale to large-scale studies. To increase prediction accuracy as well as to provide a means to gain new biological insights into these interactions, we have developed a unified deep learning-based framework to predict binding interfaces on both antibodies and antigens. RESULTS Our framework leverages three key aspects of antibody-antigen interactions to learn predictive structural representations: (i) since interfaces are formed from multiple residues in spatial proximity, we employ graph convolutions to aggregate properties across local regions in a protein; (ii) since interactions are specific between antibody-antigen pairs, we employ an attention layer to explicitly encode the context of the partner; (iii) since more data are available for general protein-protein interactions, we employ transfer learning to leverage this data as a prior for the specific case of antibody-antigen interactions. We show that this single framework achieves state-of-the-art performance at predicting binding interfaces on both antibodies and antigens, and that each of its three aspects drives additional improvement in the performance. We further show that the attention layer not only improves performance, but also provides a biologically interpretable perspective into the mode of interaction. AVAILABILITY AND IMPLEMENTATION The source code is freely available on github at https://github.com/vamships/PECAN.git.
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Affiliation(s)
- Srivamshi Pittala
- Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA
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55
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Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, Aprahamian M, Baker D, Barlow KA, Barth P, Basanta B, Bender BJ, Blacklock K, Bonet J, Boyken SE, Bradley P, Bystroff C, Conway P, Cooper S, Correia BE, Coventry B, Das R, De Jong RM, DiMaio F, Dsilva L, Dunbrack R, Ford AS, Frenz B, Fu DY, Geniesse C, Goldschmidt L, Gowthaman R, Gray JJ, Gront D, Guffy S, Horowitz S, Huang PS, Huber T, Jacobs TM, Jeliazkov JR, Johnson DK, Kappel K, Karanicolas J, Khakzad H, Khar KR, Khare SD, Khatib F, Khramushin A, King IC, Kleffner R, Koepnick B, Kortemme T, Kuenze G, Kuhlman B, Kuroda D, Labonte JW, Lai JK, Lapidoth G, Leaver-Fay A, Lindert S, Linsky T, London N, Lubin JH, Lyskov S, Maguire J, Malmström L, Marcos E, Marcu O, Marze NA, Meiler J, Moretti R, Mulligan VK, Nerli S, Norn C, Ó'Conchúir S, Ollikainen N, Ovchinnikov S, Pacella MS, Pan X, Park H, Pavlovicz RE, Pethe M, Pierce BG, Pilla KB, Raveh B, Renfrew PD, Burman SSR, Rubenstein A, Sauer MF, Scheck A, Schief W, Schueler-Furman O, Sedan Y, Sevy AM, Sgourakis NG, Shi L, Siegel JB, Silva DA, Smith S, Song Y, Stein A, Szegedy M, Teets FD, Thyme SB, Wang RYR, Watkins A, Zimmerman L, Bonneau R. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods 2020; 17:665-680. [PMID: 32483333 PMCID: PMC7603796 DOI: 10.1038/s41592-020-0848-2] [Citation(s) in RCA: 427] [Impact Index Per Article: 106.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 04/22/2020] [Indexed: 12/12/2022]
Abstract
The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
| | - Brian D Weitzner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Steven M Lewis
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biochemistry, Duke University, Durham, NC, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Aprahamian
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Kyle A Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, USA
| | - Patrick Barth
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Benjamin Basanta
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Biological Physics Structure and Design PhD Program, University of Washington, Seattle, WA, USA
| | - Brian J Bender
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Kristin Blacklock
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Scott E Boyken
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Phil Bradley
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Bystroff
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Patrick Conway
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Seth Cooper
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lorna Dsilva
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Alexander S Ford
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Brandon Frenz
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Darwin Y Fu
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Caleb Geniesse
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland
| | - Sharon Guffy
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott Horowitz
- Department of Chemistry & Biochemistry, University of Denver, Denver, CO, USA
- The Knoebel Institute for Healthy Aging, University of Denver, Denver, CO, USA
| | - Po-Ssu Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Thomas Huber
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Tim M Jacobs
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - David K Johnson
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - John Karanicolas
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Hamed Khakzad
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
| | - Karen R Khar
- Cyrus Biotechnology, Seattle, WA, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Sagar D Khare
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Firas Khatib
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA
| | - Alisa Khramushin
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Indigo C King
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Robert Kleffner
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Brian Koepnick
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daisuke Kuroda
- Medical Device Development and Regulation Research Center, School of Engineering, University of Tokyo, Tokyo, Japan
- Department of Bioengineering, School of Engineering, University of Tokyo, Tokyo, Japan
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Chemistry, Franklin & Marshall College, Lancaster, PA, USA
| | - Jason K Lai
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Gideon Lapidoth
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - Thomas Linsky
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Nir London
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Joseph H Lubin
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lars Malmström
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Enrique Marcos
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Research in Biomedicine Barcelona, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Orly Marcu
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Nicholas A Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Departments of Chemistry, Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
- Institute for Chemical Biology, Vanderbilt University, Nashville, TN, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Santrupti Nerli
- Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Christoffer Norn
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Shane Ó'Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Noah Ollikainen
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Michael S Pacella
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ryan E Pavlovicz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Manasi Pethe
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Kala Bharath Pilla
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Barak Raveh
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Aliza Rubenstein
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Marion F Sauer
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Andreas Scheck
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Sedan
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alexander M Sevy
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Lei Shi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justin B Siegel
- Department of Chemistry, University of California, Davis, Davis, CA, USA
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, California, USA
- Genome Center, University of California, Davis, Davis, CA, USA
| | | | - Shannon Smith
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Yifan Song
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Amelie Stein
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Maria Szegedy
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Frank D Teets
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Summer B Thyme
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ray Yu-Ruei Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Lior Zimmerman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
- Department of Computer Science, New York University, New York, NY, USA.
- Center for Data Science, New York University, New York, NY, USA.
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56
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Padilla-Sanchez V. In silico analysis of SARS-CoV-2 spike glycoprotein and insights into antibody binding. RESEARCH IDEAS AND OUTCOMES 2020. [DOI: 10.3897/rio.6.e55281] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China in December 2019. Since then, COVID-19, the disease caused by SARS-CoV-2, has become a rapidly spreading pandemic that has reached most countries in the world. So far, there are no vaccines or therapeutics to fight this virus. Here, I present an in silico analysis of the virus spike glycoprotein (recently determined at atomic resolution) and provide insights into how antibodies against the 2002 virus SARS-CoV might be modified to neutralize SARS-CoV-2. I ran docking experiments with Rosetta Dock to determine which substitutions in the 80R and m396 antibodies might improve the binding of these to SARS-CoV-2 and used molecular visualization and analysis software, including UCSF Chimera and Rosetta Dock, as well as other bioinformatics tools, including SWISS-MODEL. Supercomputers, including Bridges Large, Stampede and Frontera, were used for macromolecular assemblies and large scale analysis and visualization.
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57
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Khorvash M, Blinov N, Ladner-Keay C, Lu J, Silverman JM, Gibbs E, Wang YT, Kovalenko A, Wishart D, Cashman NR. Molecular interactions between monoclonal oligomer-specific antibody 5E3 and its amyloid beta cognates. PLoS One 2020; 15:e0232266. [PMID: 32469918 PMCID: PMC7259632 DOI: 10.1371/journal.pone.0232266] [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: 10/23/2019] [Accepted: 04/12/2020] [Indexed: 11/30/2022] Open
Abstract
Oligomeric amyloid β (Aβ) is currently considered the most neurotoxic form of the Aβ peptide implicated in Alzheimer’s disease (AD). The molecular structures of the oligomers have remained mostly unknown due to their transient nature. As a result, the molecular mechanisms of interactions between conformation-specific antibodies and their Aβ oligomer (AβO) cognates are not well understood. A monoclonal conformation-specific antibody, m5E3, was raised against a structural epitope of Aβ oligomers. m5E3 binds to AβOs with high affinity, but not to Aβ monomers or fibrils. In this study, a computational model of the variable fragment (Fv) of the m5E3 antibody (Fv5E3) is introduced. We further employ docking and molecular dynamics simulations to determine the molecular details of the antibody-oligomer interactions, and to classify the AβOs as Fv5E3-positives and negatives, and to provide a rationale for the low affinity of Fv5E3 for fibrils. This information will help us to perform site-directed mutagenesis on the m5E3 antibody to improve its specificity and affinity toward oligomeric Aβ species. We also provide evidence for the possible capability of the m5E3 antibody to disaggregate AβOs and to fragment protofilaments.
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Affiliation(s)
- Massih Khorvash
- Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Vancouver, British Columbia, Canada
| | - Nick Blinov
- Department of Mechanical Engineering, Edmonton, Alberta, Canada
- National Research Council of Canada, Edmonton, Alberta, Canada
| | - Carol Ladner-Keay
- National Research Council of Canada, Edmonton, Alberta, Canada
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Jie Lu
- University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Vancouver, British Columbia, Canada
| | - Judith M. Silverman
- University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Vancouver, British Columbia, Canada
| | - Ebrima Gibbs
- University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Vancouver, British Columbia, Canada
| | - Yu Tian Wang
- University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Vancouver, British Columbia, Canada
| | - Andriy Kovalenko
- Department of Mechanical Engineering, Edmonton, Alberta, Canada
- National Research Council of Canada, Edmonton, Alberta, Canada
| | - David Wishart
- National Research Council of Canada, Edmonton, Alberta, Canada
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Neil R. Cashman
- Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Vancouver, British Columbia, Canada
- * E-mail:
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58
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Marks C, Deane CM. How repertoire data are changing antibody science. J Biol Chem 2020; 295:9823-9837. [PMID: 32409582 DOI: 10.1074/jbc.rev120.010181] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 04/28/2020] [Indexed: 12/13/2022] Open
Abstract
Antibodies are vital proteins of the immune system that recognize potentially harmful molecules and initiate their removal. Mammals can efficiently create vast numbers of antibodies with different sequences capable of binding to any antigen with high affinity and specificity. Because they can be developed to bind to many disease agents, antibodies can be used as therapeutics. In an organism, after antigen exposure, antibodies specific to that antigen are enriched through clonal selection, expansion, and somatic hypermutation. The antibodies present in an organism therefore report on its immune status, describe its innate ability to deal with harmful substances, and reveal how it has previously responded. Next-generation sequencing technologies are being increasingly used to query the antibody, or B-cell receptor (BCR), sequence repertoire, and the amount of BCR data in public repositories is growing. The Observed Antibody Space database, for example, currently contains over a billion sequences from 68 different studies. Repertoires are available that represent both the naive state (i.e. antigen-inexperienced) and that after immunization. This wealth of data has created opportunities to learn more about our immune system. In this review, we discuss the many ways in which BCR repertoire data have been or could be exploited. We highlight its utility for providing insights into how the naive immune repertoire is generated and how it responds to antigens. We also consider how structural information can be used to enhance these data and may lead to more accurate depictions of the sequence space and to applications in the discovery of new therapeutics.
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Affiliation(s)
- Claire Marks
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Charlotte M Deane
- Department of Statistics, University of Oxford, Oxford, United Kingdom
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59
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Choi JH, Woo HM, Lee TY, Lee SY, Shim SM, Park WJ, Yang JS, Kim JA, Yun MR, Kim DW, Kim SS, Zhang Y, Shi W, Wang L, Graham BS, Mascola JR, Wang N, McLellan JS, Lee JY, Lee H. Characterization of a human monoclonal antibody generated from a B-cell specific for a prefusion-stabilized spike protein of Middle East respiratory syndrome coronavirus. PLoS One 2020; 15:e0232757. [PMID: 32384116 PMCID: PMC7209324 DOI: 10.1371/journal.pone.0232757] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 04/21/2020] [Indexed: 12/19/2022] Open
Abstract
Middle East respiratory syndrome coronavirus (MERS-CoV) causes severe respiratory infection and continues to infect humans, thereby contributing to a high mortality rate (34.3% in 2019). In the absence of an available licensed vaccine and antiviral agent, therapeutic human antibodies have been suggested as candidates for treatment. In this study, human monoclonal antibodies were isolated by sorting B cells from patient's PBMC cells with prefusion stabilized spike (S) probes and a direct immunoglobulin cloning strategy. We identified six receptor-binding domain (RBD)-specific and five S1 (non-RBD)-specific antibodies, among which, only the RBD-specific antibodies showed high neutralizing potency (IC50 0.006-1.787 μg/ml) as well as high affinity to RBD. Notably, passive immunization using a highly potent antibody (KNIH90-F1) at a relatively low dose (2 mg/kg) completely protected transgenic mice expressing human DPP4 against MERS-CoV lethal challenge. These results suggested that human monoclonal antibodies isolated by using the rationally designed prefusion MERS-CoV S probe could be considered potential candidates for the development of therapeutic and/or prophylactic antiviral agents for MERS-CoV human infection.
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MESH Headings
- Animals
- Antibodies, Monoclonal/immunology
- Antibodies, Monoclonal/pharmacology
- Antibodies, Neutralizing/immunology
- Antibodies, Neutralizing/pharmacology
- Antibodies, Viral/immunology
- Antibodies, Viral/pharmacology
- Antiviral Agents/pharmacology
- Cell Line
- Chlorocebus aethiops
- Coronavirus Infections/drug therapy
- Dipeptidyl Peptidase 4/genetics
- Humans
- Leukocytes, Mononuclear/immunology
- Mice
- Mice, Inbred C57BL
- Mice, Transgenic
- Middle East Respiratory Syndrome Coronavirus/immunology
- Republic of Korea
- Spike Glycoprotein, Coronavirus/immunology
- Vero Cells
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Affiliation(s)
- Jang-Hoon Choi
- Division of Viral Disease Research, Center for Infectious Diseases Research, Korea National Institute of Health, Korea Centers for Disease Control and Prevention, Cheongju-si, Republic of Korea
| | - Hye-Min Woo
- Division of Emerging Infectious Disease and Vector Research, Center for Infectious Diseases Research, Korea National Institute of Health, Korea Centers for Disease Control and Prevention, Cheongju-si, Republic of Korea
| | - Tae-young Lee
- Division of Emerging Infectious Disease and Vector Research, Center for Infectious Diseases Research, Korea National Institute of Health, Korea Centers for Disease Control and Prevention, Cheongju-si, Republic of Korea
| | - So-young Lee
- Division of Emerging Infectious Disease and Vector Research, Center for Infectious Diseases Research, Korea National Institute of Health, Korea Centers for Disease Control and Prevention, Cheongju-si, Republic of Korea
| | - Sang-Mu Shim
- Division of Emerging Infectious Disease and Vector Research, Center for Infectious Diseases Research, Korea National Institute of Health, Korea Centers for Disease Control and Prevention, Cheongju-si, Republic of Korea
| | - Woo-Jung Park
- Division of Emerging Infectious Disease and Vector Research, Center for Infectious Diseases Research, Korea National Institute of Health, Korea Centers for Disease Control and Prevention, Cheongju-si, Republic of Korea
| | - Jeong-Sun Yang
- Division of Emerging Infectious Disease and Vector Research, Center for Infectious Diseases Research, Korea National Institute of Health, Korea Centers for Disease Control and Prevention, Cheongju-si, Republic of Korea
| | - Joo Ae Kim
- Division of Vaccine Research, Center for Infectious Diseases Research, Korea National Institute of Health, Korea Centers for Disease Control and Prevention, Cheongju-si, Republic of Korea
| | - Mi-Ran Yun
- Division of Vaccine Research, Center for Infectious Diseases Research, Korea National Institute of Health, Korea Centers for Disease Control and Prevention, Cheongju-si, Republic of Korea
| | - Dae-Won Kim
- Division of Vaccine Research, Center for Infectious Diseases Research, Korea National Institute of Health, Korea Centers for Disease Control and Prevention, Cheongju-si, Republic of Korea
| | - Sung Soon Kim
- Division of Bacterial Disease Research, Center for Infectious Diseases Research, Korea National Institute of Health, Korea Centers for Disease Control and Prevention, Cheongju-si, Republic of Korea
| | - Yi Zhang
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States of America
| | - Wei Shi
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States of America
| | - Lingshu Wang
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States of America
| | - Barney S. Graham
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States of America
| | - John R. Mascola
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States of America
| | - Nanshuang Wang
- Department of Molecular Biosciences, College of Natural Sciences, University of Texas, Austin, TX, United States of America
| | - Jason S. McLellan
- Department of Molecular Biosciences, College of Natural Sciences, University of Texas, Austin, TX, United States of America
| | - Joo-Yeon Lee
- Division of Emerging Infectious Disease and Vector Research, Center for Infectious Diseases Research, Korea National Institute of Health, Korea Centers for Disease Control and Prevention, Cheongju-si, Republic of Korea
| | - Hansaem Lee
- Division of Emerging Infectious Disease and Vector Research, Center for Infectious Diseases Research, Korea National Institute of Health, Korea Centers for Disease Control and Prevention, Cheongju-si, Republic of Korea
- * E-mail:
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60
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Kuroda D, Tsumoto K. Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design. J Pharm Sci 2020; 109:1631-1651. [DOI: 10.1016/j.xphs.2020.01.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 12/25/2019] [Accepted: 01/10/2020] [Indexed: 12/18/2022]
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61
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Ambrosetti F, Jiménez-García B, Roel-Touris J, Bonvin AMJJ. Modeling Antibody-Antigen Complexes by Information-Driven Docking. Structure 2019; 28:119-129.e2. [PMID: 31727476 DOI: 10.1016/j.str.2019.10.011] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 07/03/2019] [Accepted: 10/18/2019] [Indexed: 10/25/2022]
Abstract
Antibodies are Y-shaped proteins essential for immune response. Their capability to recognize antigens with high specificity makes them excellent therapeutic targets. Understanding the structural basis of antibody-antigen interactions is therefore crucial for improving our ability to design efficient biological drugs. Computational approaches such as molecular docking are providing a valuable and fast alternative to experimental structural characterization for these complexes. We investigate here how information about complementarity-determining regions and binding epitopes can be used to drive the modeling process, and present a comparative study of four different docking software suites (ClusPro, LightDock, ZDOCK, and HADDOCK) providing specific options for antibody-antigen modeling. Their performance on a dataset of 16 complexes is reported. HADDOCK, which includes information to drive the docking, is shown to perform best in terms of both success rate and quality of the generated models in both the presence and absence of information about the epitope on the antigen.
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Affiliation(s)
- Francesco Ambrosetti
- Department of Physics, Sapienza University, Piazzale Aldo Moro 5, 00184 Rome, Italy; Faculty of Science - Chemistry, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Brian Jiménez-García
- Faculty of Science - Chemistry, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Faculty of Science - Chemistry, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Alexandre M J J Bonvin
- Faculty of Science - Chemistry, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.
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62
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Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res 2019; 45:W24-W29. [PMID: 28472356 PMCID: PMC5570230 DOI: 10.1093/nar/gkx346] [Citation(s) in RCA: 902] [Impact Index Per Article: 180.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Accepted: 04/20/2017] [Indexed: 02/07/2023] Open
Abstract
Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community.
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Affiliation(s)
- Martin Closter Jespersen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby 2800, Denmark
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby 2800, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Paolo Marcatili
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby 2800, Denmark
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63
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Høydahl LS, Richter L, Frick R, Snir O, Gunnarsen KS, Landsverk OJB, Iversen R, Jeliazkov JR, Gray JJ, Bergseng E, Foss S, Qiao SW, Lundin KEA, Jahnsen J, Jahnsen FL, Sandlie I, Sollid LM, Løset GÅ. Plasma Cells Are the Most Abundant Gluten Peptide MHC-expressing Cells in Inflamed Intestinal Tissues From Patients With Celiac Disease. Gastroenterology 2019; 156:1428-1439.e10. [PMID: 30593798 PMCID: PMC6441630 DOI: 10.1053/j.gastro.2018.12.013] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2017] [Revised: 08/21/2018] [Accepted: 12/20/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND & AIMS Development of celiac disease is believed to involve the transglutaminase-dependent response of CD4+ T cells toward deamidated gluten peptides in the intestinal mucosa of individuals with specific HLA-DQ haplotypes. We investigated the antigen presentation process during this mucosal immune response. METHODS We generated monoclonal antibodies (mAbs) specific for the peptide-MHC (pMHC) complex of HLA-DQ2.5 and the immunodominant gluten epitope DQ2.5-glia-α1a using phage display. We used these mAbs to assess gluten peptide presentation and phenotypes of presenting cells by flow cytometry and enzyme-linked immune absorbent spot (ELISPOT) in freshly prepared single-cell suspensions from intestinal biopsies from 40 patients with celiac disease (35 untreated and 5 on a gluten-free diet) as well as 18 subjects with confirmed noninflamed gut mucosa (controls, 12 presumed healthy, 5 undergoing pancreatoduodenectomy, and 1 with potential celiac disease). RESULTS Using the mAbs, we detected MHC complexes on cells from intestinal biopsies from patients with celiac disease who consume gluten, but not from patients on gluten-free diets. We found B cells and plasma cells to be the most abundant cells that present DQ2.5-glia-α1a in the inflamed mucosa. We identified a subset of plasma cells that expresses B-cell receptors (BCR) specific for gluten peptides or the autoantigen transglutaminase 2 (TG2). Expression of MHC class II (MHCII) was not restricted to these specific plasma cells in patients with celiac disease but was observed in an average 30% of gut plasma cells from patients and controls. CONCLUSIONS A population of plasma cells from intestinal biopsies of patients with celiac disease express MHCII; this is the most abundant cell type presenting the immunodominant gluten peptide DQ2.5-glia-α1a in the tissues from these patients. These results indicate that plasma cells in the gut can function as antigen-presenting cells and might promote and maintain intestinal inflammation in patients with celiac disease or other inflammatory disorders.
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Affiliation(s)
- Lene Støkken Høydahl
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway; Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway; KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway.
| | - Lisa Richter
- Centre for Immune Regulation and Department of Pathology, University of Oslo and Oslo University Hospital, Oslo, Norway.,Present address: Core Facility Flow Cytometry, Biomedical Center Munich, Ludwig-Maximilians-Universität Munich, Planegg-Martinsried, Germany
| | - Rahel Frick
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway
| | - Omri Snir
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Kristin Støen Gunnarsen
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway
| | - Ole JB Landsverk
- Centre for Immune Regulation and Department of Pathology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Rasmus Iversen
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Jeliazko R Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeffrey J Gray
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Chemical and Biomolecular Engineering and Institute of NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland, USA.,Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Elin Bergseng
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Stian Foss
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway
| | - Shuo-Wang Qiao
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre and Department of Immunology, University of Oslo, Oslo, Norway
| | - Knut EA Lundin
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.,Dept of Gastroenterology, Oslo University Hospital-Rikshospitalet Oslo, Norway
| | - Jørgen Jahnsen
- Department of Gastroenterology, Akershus University Hospital, Lørenskog, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Frode L Jahnsen
- Centre for Immune Regulation and Department of Pathology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Inger Sandlie
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway
| | - Ludvig M Sollid
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre and Department of Immunology, University of Oslo, Oslo, Norway
| | - Geir Åge Løset
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway; Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway; Nextera AS, Oslo, Norway.
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64
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Fink K. Can We Improve Vaccine Efficacy by Targeting T and B Cell Repertoire Convergence? Front Immunol 2019; 10:110. [PMID: 30814993 PMCID: PMC6381292 DOI: 10.3389/fimmu.2019.00110] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 01/15/2019] [Indexed: 01/31/2023] Open
Abstract
Traditional vaccine development builds on the assumption that healthy individuals have virtually unlimited antigen recognition repertoires of receptors in B cells and T cells [the B cell receptor (BCR) and TCR respectively]. However, there are indications that there are "holes" in the breadth of repertoire diversity, where no or few B or T cell are able to bind to a given antigen. Repertoire diversity may in these cases be a limiting factor for vaccine efficacy. Assuming that it is possible to predict which B and T cell receptors will respond to a given immunogen, vaccine strategies could be optimized and personalized. In addition, vaccine testing could be simplified if we could predict responses through sequencing BCR and TCRs. Bulk sequencing has shown putatively specific converging sequences after infection or vaccination. However, only single cell technologies have made it possible to capture the sequence of both heavy and light chains of a BCR or the alpha and beta chains the TCR. This has enabled the cloning of receptors and the functional validation of a predicted specificity. This review summarizes recent evidence of converging sequences in infectious diseases. Current and potential future applications of single cell technology in immune repertoire analysis are then discussed. Finally, possible short- and long- term implications for vaccine research are highlighted.
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Affiliation(s)
- Katja Fink
- Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore
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65
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Wollacott AM, Robinson LN, Ramakrishnan B, Tissire H, Viswanathan K, Shriver Z, Babcock GJ. Structural prediction of antibody-APRIL complexes by computational docking constrained by antigen saturation mutagenesis library data. J Mol Recognit 2019; 32:e2778. [DOI: 10.1002/jmr.2778] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 11/21/2018] [Accepted: 12/06/2018] [Indexed: 12/29/2022]
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66
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Bansia H, Bagaria S, Karande AA, Ramakumar S. Structural basis for neutralization of cytotoxic abrin by monoclonal antibody D6F10. FEBS J 2019; 286:1003-1029. [DOI: 10.1111/febs.14716] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 11/02/2018] [Accepted: 11/30/2018] [Indexed: 11/26/2022]
Affiliation(s)
- Harsh Bansia
- Department of Physics Indian Institute of Science Bengaluru India
| | - Shradha Bagaria
- Department of Biochemistry Indian Institute of Science Bengaluru India
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67
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Vahedi S, Lusvarghi S, Pluchino K, Shafrir Y, Durell SR, Gottesman MM, Ambudkar SV. Mapping discontinuous epitopes for MRK-16, UIC2 and 4E3 antibodies to extracellular loops 1 and 4 of human P-glycoprotein. Sci Rep 2018; 8:12716. [PMID: 30143707 PMCID: PMC6109178 DOI: 10.1038/s41598-018-30984-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 07/30/2018] [Indexed: 12/14/2022] Open
Abstract
P-glycoprotein (P-gp), an ATP-dependent efflux pump, is associated with the development of multidrug resistance in cancer cells. Antibody-mediated blockade of human P-gp activity has been shown to overcome drug resistance by re-sensitizing resistant cancer cells to anticancer drugs. Despite the potential clinical application of this finding, the epitopes of the three human P-gp-specific monoclonal antibodies MRK-16, UIC2 and 4E3, which bind to the extracellular loops (ECLs) have not yet been mapped. By generating human-mouse P-gp chimeras, we mapped the epitopes of these antibodies to ECLs 1 and 4. We then identified key amino acids in these regions by replacing mouse residues with homologous human P-gp residues to recover binding of antibodies to the mouse P-gp. We found that changing a total of ten residues, five each in ECL1 and ECL4, was sufficient to recover binding of both MRK-16 and 4E3 antibodies, suggesting a common epitope. However, recovery of the conformation-sensitive UIC2 epitope required replacement of thirteen residues in ECL1 and the same five residues replaced in the ECL4 for MRK-16 and 4E3 binding. These results demonstrate that discontinuous epitopes for MRK-16, UIC2 and 4E3 are located in the same regions of ECL1 and 4 of the multidrug transporter.
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Affiliation(s)
- Shahrooz Vahedi
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, 20892-4256, USA
| | - Sabrina Lusvarghi
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, 20892-4256, USA
| | - Kristen Pluchino
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, 20892-4256, USA
| | - Yinon Shafrir
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, 20892-4256, USA
| | - Stewart R Durell
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, 20892-4256, USA
| | - Michael M Gottesman
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, 20892-4256, USA
| | - Suresh V Ambudkar
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, 20892-4256, USA.
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68
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Zhao J, Nussinov R, Wu WJ, Ma B. In Silico Methods in Antibody Design. Antibodies (Basel) 2018; 7:E22. [PMID: 31544874 PMCID: PMC6640671 DOI: 10.3390/antib7030022] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 06/28/2018] [Accepted: 06/28/2018] [Indexed: 01/10/2023] Open
Abstract
Antibody therapies with high efficiency and low toxicity are becoming one of the major approaches in antibody therapeutics. Based on high-throughput sequencing and increasing experimental structures of antibodies/antibody-antigen complexes, computational approaches can predict antibody/antigen structures, engineering the function of antibodies and design antibody-antigen complexes with improved properties. This review summarizes recent progress in the field of in silico design of antibodies, including antibody structure modeling, antibody-antigen complex prediction, antibody stability evaluation, and allosteric effects in antibodies and functions. We listed the cases in which these methods have helped experimental studies to improve the affinities and physicochemical properties of antibodies. We emphasized how the molecular dynamics unveiled the allosteric effects during antibody-antigen recognition and antibody-effector recognition.
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Affiliation(s)
- Jun Zhao
- Division of Biotechnology Review and Research I, Office of Biotechnology Products, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.
- Interagency Oncology Task Force (IOTF) Fellowship: Oncology Product Research/Review Fellow, National Cancer Institute, Bethesda, MD 20892, USA.
- Cancer and Inflammation Program, National Cancer Institute, Frederick, MD 21702, USA.
| | - Ruth Nussinov
- Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, National Cancer Institute, Frederick, MD 21702, USA.
- Sackler Inst. of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Wen-Jin Wu
- Division of Biotechnology Review and Research I, Office of Biotechnology Products, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.
| | - Buyong Ma
- Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, National Cancer Institute, Frederick, MD 21702, USA.
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69
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Tanner JE, Hu J, Alfieri C. Construction and Characterization of a Humanized Anti-Epstein-Barr Virus gp350 Antibody with Neutralizing Activity in Cell Culture. Cancers (Basel) 2018; 10:cancers10040112. [PMID: 29642526 PMCID: PMC5923367 DOI: 10.3390/cancers10040112] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 03/30/2018] [Accepted: 04/04/2018] [Indexed: 12/16/2022] Open
Abstract
Acute Epstein-Barr virus (EBV) infection in immunosuppressed transplant patients can give rise to a malignant B-cell proliferation known as post-transplant lymphoproliferative disease (PTLD). The EBV major virion surface glycoprotein (gp)350 is a principal target of naturally occurring neutralizing antibodies and is viewed as the best target to prevent acute infection and PTLD in at-risk transplant recipients. We have constructed a humanized (hu) version of the murine anti-gp350 neutralizing monoclonal antibody 72a1. The hu72a1 IgG1 antibody displayed no significant anti-mouse activity, recognized both gp350 and its splice variant gp220 as well as a gp350 peptide that was shown to constitute the principal EBV gp350 neutralizing epitope when tested in immunoassays. Hu72a1 antibody blocked in vitro EBV infection of B cells at a level which equaled that of a mouse-human chimeric 72a1 antibody construct. This work provides a further structural and immunological understanding of the 72a1 antibody interaction with EBV gp350, and constitutes a launch point for future anti-EBV therapeutic antibodies designed to block EBV infection and prevent PTLD while eliminating the deleterious antigenic murine features of the original 72a1 antibody.
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Affiliation(s)
- Jerome E Tanner
- Laboratory of Viral Pathogenesis, Research Centre, CHU Sainte-Justine, Montréal, QC H3T 1C5, Canada.
| | - Jing Hu
- Laboratory of Viral Pathogenesis, Research Centre, CHU Sainte-Justine, Montréal, QC H3T 1C5, Canada.
| | - Caroline Alfieri
- Laboratory of Viral Pathogenesis, Research Centre, CHU Sainte-Justine, Montréal, QC H3T 1C5, Canada.
- Department of Microbiology, Infectiology and Immunology, University of Montreal, 3175 Côte Ste-Catherine Road, Montreal, QC H3T 1C5, Canada.
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70
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Opuni KFM, Al-Majdoub M, Yefremova Y, El-Kased RF, Koy C, Glocker MO. Mass spectrometric epitope mapping. MASS SPECTROMETRY REVIEWS 2018; 37:229-241. [PMID: 27403762 DOI: 10.1002/mas.21516] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 06/23/2016] [Indexed: 06/06/2023]
Abstract
Mass spectrometric epitope mapping has become a versatile method to precisely determine a soluble antigen's partial structure that directly interacts with an antibody in solution. Typical lengths of investigated antigens have increased up to several 100 amino acids while experimentally determined epitope peptides have decreased in length to on average 10-15 amino acids. Since the early 1990s more and more sophisticated methods have been developed and have forwarded a bouquet of suitable approaches for epitope mapping with immobilized, temporarily immobilized, and free-floating antibodies. While up to now monoclonal antibodies have been mostly used in epitope mapping experiments, the applicability of polyclonal antibodies has been proven. The antibody's resistance towards enzymatic proteolysis has been of key importance for the two mostly applied methods: epitope excision and epitope extraction. Sample consumption has dropped to low pmol amounts on both, the antigen and the antibody. While adequate in-solution sample handling has been most important for successful epitope mapping, mass spectrometric analysis has been found the most suitable read-out method from early on. The rapidity by which mass spectrometric epitope mapping nowadays is executed outperforms all alternative methods. Thus, it can be asserted that mass spectrometric epitope mapping has reached a state of maturity, which allows it to be used in any mass spectrometry laboratory. After 25 years of constant and steady improvements, its application to clinical samples, for example, for patient characterization and stratification, is anticipated in the near future. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:229-241, 2018.
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Affiliation(s)
- Kwabena F M Opuni
- Proteome Center Rostock, University Medicine and Natural Science Faculty, University of Rostock, Rostock, Germany
| | - Mahmoud Al-Majdoub
- Proteome Center Rostock, University Medicine and Natural Science Faculty, University of Rostock, Rostock, Germany
| | - Yelena Yefremova
- Proteome Center Rostock, University Medicine and Natural Science Faculty, University of Rostock, Rostock, Germany
| | - Reham F El-Kased
- Microbiology and Immunology Faculty of Pharmacy, The British University in Egypt, Cairo, Egypt
| | - Cornelia Koy
- Proteome Center Rostock, University Medicine and Natural Science Faculty, University of Rostock, Rostock, Germany
| | - Michael O Glocker
- Proteome Center Rostock, University Medicine and Natural Science Faculty, University of Rostock, Rostock, Germany
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71
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Kovaltsuk A, Krawczyk K, Galson JD, Kelly DF, Deane CM, Trück J. How B-Cell Receptor Repertoire Sequencing Can Be Enriched with Structural Antibody Data. Front Immunol 2017; 8:1753. [PMID: 29276518 PMCID: PMC5727015 DOI: 10.3389/fimmu.2017.01753] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 11/27/2017] [Indexed: 12/24/2022] Open
Abstract
Next-generation sequencing of immunoglobulin gene repertoires (Ig-seq) allows the investigation of large-scale antibody dynamics at a sequence level. However, structural information, a crucial descriptor of antibody binding capability, is not collected in Ig-seq protocols. Developing systematic relationships between the antibody sequence information gathered from Ig-seq and low-throughput techniques such as X-ray crystallography could radically improve our understanding of antibodies. The mapping of Ig-seq datasets to known antibody structures can indicate structurally, and perhaps functionally, uncharted areas. Furthermore, contrasting naïve and antigenically challenged datasets using structural antibody descriptors should provide insights into antibody maturation. As the number of antibody structures steadily increases and more and more Ig-seq datasets become available, the opportunities that arise from combining the two types of information increase as well. Here, we review how these data types enrich one another and show potential for advancing our knowledge of the immune system and improving antibody engineering.
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Affiliation(s)
| | - Konrad Krawczyk
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Jacob D Galson
- Division of Immunology and the Children's Research Center, University Children's Hospital, University of Zürich, Zürich, Switzerland
| | - Dominic F Kelly
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford and the NIHR Oxford Biomedical Research Center, Oxford, United Kingdom
| | - Charlotte M Deane
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Johannes Trück
- Division of Immunology and the Children's Research Center, University Children's Hospital, University of Zürich, Zürich, Switzerland
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72
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Hua CK, Gacerez AT, Sentman CL, Ackerman ME, Choi Y, Bailey-Kellogg C. Computationally-driven identification of antibody epitopes. eLife 2017; 6:29023. [PMID: 29199956 PMCID: PMC5739537 DOI: 10.7554/elife.29023] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 12/02/2017] [Indexed: 12/21/2022] Open
Abstract
Understanding where antibodies recognize antigens can help define mechanisms of action and provide insights into progression of immune responses. We investigate the extent to which information about binding specificity implicitly encoded in amino acid sequence can be leveraged to identify antibody epitopes. In computationally-driven epitope localization, possible antibody–antigen binding modes are modeled, and targeted panels of antigen variants are designed to experimentally test these hypotheses. Prospective application of this approach to two antibodies enabled epitope localization using five or fewer variants per antibody, or alternatively, a six-variant panel for both simultaneously. Retrospective analysis of a variety of antibodies and antigens demonstrated an almost 90% success rate with an average of three antigen variants, further supporting the observation that the combination of computational modeling and protein design can reveal key determinants of antibody–antigen binding and enable efficient studies of collections of antibodies identified from polyclonal samples or engineered libraries.
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Affiliation(s)
- Casey K Hua
- Thayer School of Engineering, Dartmouth College, Hanover, United States.,Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Albert T Gacerez
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Charles L Sentman
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Margaret E Ackerman
- Thayer School of Engineering, Dartmouth College, Hanover, United States.,Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Yoonjoo Choi
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
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73
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Moretti R, Lyskov S, Das R, Meiler J, Gray JJ. Web-accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE). Protein Sci 2017; 27:259-268. [PMID: 28960691 DOI: 10.1002/pro.3313] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Revised: 09/21/2017] [Accepted: 09/25/2017] [Indexed: 12/12/2022]
Abstract
The Rosetta molecular modeling software package provides a large number of experimentally validated tools for modeling and designing proteins, nucleic acids, and other biopolymers, with new protocols being added continually. While freely available to academic users, external usage is limited by the need for expertise in the Unix command line environment. To make Rosetta protocols available to a wider audience, we previously created a web server called Rosetta Online Server that Includes Everyone (ROSIE), which provides a common environment for hosting web-accessible Rosetta protocols. Here we describe a simplification of the ROSIE protocol specification format, one that permits easier implementation of Rosetta protocols. Whereas the previous format required creating multiple separate files in different locations, the new format allows specification of the protocol in a single file. This new, simplified protocol specification has more than doubled the number of Rosetta protocols available under ROSIE. These new applications include pKa determination, lipid accessibility calculation, ribonucleic acid redesign, protein-protein docking, protein-small molecule docking, symmetric docking, antibody docking, cyclic toxin docking, critical binding peptide determination, and mapping small molecule binding sites. ROSIE is freely available to academic users at http://rosie.rosettacommons.org.
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Affiliation(s)
- Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland
| | - Rhiju Das
- Department of Biochemistry, Stanford University, Stanford, California.,Department of Physics, Stanford University, Stanford, California
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland.,Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, Maryland
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74
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In silico methods for design of biological therapeutics. Methods 2017; 131:33-65. [PMID: 28958951 DOI: 10.1016/j.ymeth.2017.09.008] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 09/21/2017] [Accepted: 09/23/2017] [Indexed: 12/18/2022] Open
Abstract
It has been twenty years since the first rationally designed small molecule drug was introduced into the market. Since then, we have progressed from designing small molecules to designing biotherapeutics. This class of therapeutics includes designed proteins, peptides and nucleic acids that could more effectively combat drug resistance and even act in cases where the disease is caused because of a molecular deficiency. Computational methods are crucial in this design exercise and this review discusses the various elements of designing biotherapeutic proteins and peptides. Many of the techniques discussed here, such as the deterministic and stochastic design methods, are generally used in protein design. We have devoted special attention to the design of antibodies and vaccines. In addition to the methods for designing these molecules, we have included a comprehensive list of all biotherapeutics approved for clinical use. Also included is an overview of methods that predict the binding affinity, cell penetration ability, half-life, solubility, immunogenicity and toxicity of the designed therapeutics. Biotherapeutics are only going to grow in clinical importance and are set to herald a new generation of disease management and cure.
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75
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Structure-based cross-docking analysis of antibody-antigen interactions. Sci Rep 2017; 7:8145. [PMID: 28811664 PMCID: PMC5557897 DOI: 10.1038/s41598-017-08414-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 07/10/2017] [Indexed: 12/02/2022] Open
Abstract
Antibody–antigen interactions are critical to our immune response, and understanding the structure-based biophysical determinants for their binding specificity and affinity is of fundamental importance. We present a computational structure-based cross-docking study to test the identification of native antibody–antigen interaction pairs among cognate and non-cognate complexes. We picked a dataset of 17 antibody–antigen complexes of which 11 have both bound and unbound structures available, and we generated a representative ensemble of cognate and non-cognate complexes. Using the Rosetta interface score as a classifier, the cognate pair was the top-ranked model in 80% (14/17) of the antigen targets using bound monomer structures in docking, 35% (6/17) when using unbound, and 12% (2/17) when using the homology-modeled backbones to generate the complexes. Increasing rigid-body diversity of the models using RosettaDock’s local dock routine lowers the discrimination accuracy with the cognate antibody–antigen pair ranking in bound and unbound models but recovers additional top-ranked cognate complexes when using homology models. The study is the first structure-based cross-docking attempt aimed at distinguishing antibody–antigen binders from non-binders and demonstrates the challenges to address for the methods to be widely applicable to supplement high-throughput experimental antibody sequencing workflows.
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76
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Fisher CR, Sutton HJ, Kaczmarski JA, McNamara HA, Clifton B, Mitchell J, Cai Y, Dups JN, D'Arcy NJ, Singh M, Chuah A, Peat TS, Jackson CJ, Cockburn IA. T-dependent B cell responses to Plasmodium induce antibodies that form a high-avidity multivalent complex with the circumsporozoite protein. PLoS Pathog 2017; 13:e1006469. [PMID: 28759640 PMCID: PMC5552345 DOI: 10.1371/journal.ppat.1006469] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Revised: 08/10/2017] [Accepted: 06/13/2017] [Indexed: 11/18/2022] Open
Abstract
The repeat region of the Plasmodium falciparum circumsporozoite protein (CSP) is a major vaccine antigen because it can be targeted by parasite neutralizing antibodies; however, little is known about this interaction. We used isothermal titration calorimetry, X-ray crystallography and mutagenesis-validated modeling to analyze the binding of a murine neutralizing antibody to Plasmodium falciparum CSP. Strikingly, we found that the repeat region of CSP is bound by multiple antibodies. This repeating pattern allows multiple weak interactions of single FAB domains to accumulate and yield a complex with a dissociation constant in the low nM range. Because the CSP protein can potentially cross-link multiple B cell receptors (BCRs) we hypothesized that the B cell response might be T cell independent. However, while there was a modest response in mice deficient in T cell help, the bulk of the response was T cell dependent. By sequencing the BCRs of CSP-repeat specific B cells in inbred mice we found that these cells underwent somatic hypermutation and affinity maturation indicative of a T-dependent response. Last, we found that the BCR repertoire of responding B cells was limited suggesting that the structural simplicity of the repeat may limit the breadth of the immune response. Vaccines aim to protect by inducing the immune system to make molecules called antibodies that can recognize molecules on the surface of invading pathogens. In the case of malaria, our most advanced vaccine candidates aim to promote the production of antibodies that recognize the circumsporozoite protein (CSP) molecule on the surface of the invasive parasite stage called the sporozoite. In this report we use X-ray crystallography to determine the structure of CSP-binding antibodies at the atomic level. We use other techniques such as isothermal titration calorimetry and structural modeling to examine how this antibody interacts with the CSP molecule. Strikingly, we found that each CSP molecule could bind 6 antibodies. This finding has implications for the immune response and may explain why high titers of antibody are needed for protection. Moreover, because the structure of the CSP repeat is quite simple we determined that the number of different kinds of antibodies that could bind this molecule are quite small. However a high avidity interaction between those antibodies and CSP can result from a process called affinity maturation that allows the body to learn how to make improved antibodies specific for pathogen molecules. These data show that while it is challenging for the immune system to recognize and neutralize CSP, it should be possible to generate viable vaccines targeting this molecule.
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Affiliation(s)
- Camilla R. Fisher
- Research School of Chemistry, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Henry J. Sutton
- John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Joe A. Kaczmarski
- Research School of Chemistry, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Hayley A. McNamara
- John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Ben Clifton
- Research School of Chemistry, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Joshua Mitchell
- Research School of Chemistry, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Yeping Cai
- John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Johanna N. Dups
- John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Nicholas J. D'Arcy
- John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Mandeep Singh
- John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Aaron Chuah
- John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Thomas S. Peat
- CSIRO Biomedical Manufacturing Program, Parkville, Victoria, Australia
| | - Colin J. Jackson
- Research School of Chemistry, The Australian National University, Canberra, Australian Capital Territory, Australia
- * E-mail: (CJJ); (IAC)
| | - Ian A. Cockburn
- John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia
- * E-mail: (CJJ); (IAC)
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77
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Marze NA, Jeliazkov JR, Roy Burman SS, Boyken SE, DiMaio F, Gray JJ. Modeling oblong proteins and water-mediated interfaces with RosettaDock in CAPRI rounds 28-35. Proteins 2017; 85:479-486. [PMID: 27667482 PMCID: PMC5710743 DOI: 10.1002/prot.25168] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Revised: 09/01/2016] [Accepted: 09/26/2016] [Indexed: 12/27/2022]
Abstract
The 28th-35th rounds of the Critical Assessment of PRotein Interactions (CAPRI) served as a practical benchmark for our RosettaDock protein-protein docking protocols, highlighting strengths and weaknesses of the approach. We achieved acceptable or better quality models in three out of 11 targets. For the two α-repeat protein-green fluorescent protein (αrep-GFP) complexes, we used a novel ellipsoidal partial-global docking method (Ellipsoidal Dock) to generate models with 2.2 Å/1.5 Å interface RMSD, capturing 49%/42% of the native contacts, for the 7-/5-repeat αrep complexes. For the DNase-immunity protein complex, we used a new predictor of hydrogen-bonding networks, HBNet with Bridging Waters, to place individual water models at the complex interface; models were generated with 1.8 Å interface RMSD and 12% native water contacts recovered. The targets for which RosettaDock failed to create an acceptable model were typically difficult in general, as six had no acceptable models submitted by any CAPRI predictor. The UCH-L5-RPN13 and UCH-L5-INO80G de-ubiquitinating enzyme-inhibitor complexes comprised inhibitors undergoing significant structural changes upon binding, with the partners being highly interwoven in the docked complexes. Our failure to predict the nucleosome-enzyme complex in Target 95 was largely due to tight constraints we placed on our model based on sparse biochemical data suggesting two specific cross-interface interactions, preventing the correct structure from being sampled. While RosettaDock's three successes show that it is a state-of-the-art docking method, the difficulties with highly flexible and multi-domain complexes highlight the need for better flexible docking and domain-assembly methods. Proteins 2017; 85:479-486. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Nicholas A. Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jeliazko R. Jeliazkov
- T.C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Shourya S. Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Scott E. Boyken
- Department of Biochemistry, University of Washington, Seattle, Washington
- Institute for Protein Design, University of Washington, Seattle, Washington
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, Washington
- Institute for Protein Design, University of Washington, Seattle, Washington
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland
- Johns Hopkins School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland
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78
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Liu X, Taylor RD, Griffin L, Coker SF, Adams R, Ceska T, Shi J, Lawson ADG, Baker T. Computational design of an epitope-specific Keap1 binding antibody using hotspot residues grafting and CDR loop swapping. Sci Rep 2017; 7:41306. [PMID: 28128368 PMCID: PMC5269676 DOI: 10.1038/srep41306] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Accepted: 12/19/2016] [Indexed: 12/20/2022] Open
Abstract
Therapeutic and diagnostic applications of monoclonal antibodies often require careful selection of binders that recognize specific epitopes on the target molecule to exert a desired modulation of biological function. Here we present a proof-of-concept application for the rational design of an epitope-specific antibody binding with the target protein Keap1, by grafting pre-defined structural interaction patterns from the native binding partner protein, Nrf2, onto geometrically matched positions of a set of antibody scaffolds. The designed antibodies bind to Keap1 and block the Keap1-Nrf2 interaction in an epitope-specific way. One resulting antibody is further optimised to achieve low-nanomolar binding affinity by in silico redesign of the CDRH3 sequences. An X-ray co-crystal structure of one resulting design reveals that the actual binding orientation and interface with Keap1 is very close to the design model, despite an unexpected CDRH3 tilt and VH/VL interface deviation, which indicates that the modelling precision may be improved by taking into account simultaneous CDR loops conformation and VH/VL orientation optimisation upon antibody sequence change. Our study confirms that, given a pre-existing crystal structure of the target protein-protein interaction, hotspots grafting with CDR loop swapping is an attractive route to the rational design of an antibody targeting a pre-selected epitope.
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Affiliation(s)
- Xiaofeng Liu
- UCB Celltech, 216 Bath Road, Slough, United Kingdom
| | | | | | | | - Ralph Adams
- UCB Celltech, 216 Bath Road, Slough, United Kingdom
| | - Tom Ceska
- UCB Celltech, 216 Bath Road, Slough, United Kingdom
| | - Jiye Shi
- UCB Pharma, Chemin du Foriest 1, B-1420 Braine-l'Alleud, Belgium
| | | | - Terry Baker
- UCB Celltech, 216 Bath Road, Slough, United Kingdom
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79
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Weitzner BD, Jeliazkov JR, Lyskov S, Marze N, Kuroda D, Frick R, Adolf-Bryfogle J, Biswas N, Dunbrack RL, Gray JJ. Modeling and docking of antibody structures with Rosetta. Nat Protoc 2017; 12:401-416. [PMID: 28125104 DOI: 10.1038/nprot.2016.180] [Citation(s) in RCA: 180] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We describe Rosetta-based computational protocols for predicting the 3D structure of an antibody from sequence (RosettaAntibody) and then docking the antibody to protein antigens (SnugDock). Antibody modeling leverages canonical loop conformations to graft large segments from experimentally determined structures, as well as offering (i) energetic calculations to minimize loops, (ii) docking methodology to refine the VL-VH relative orientation and (iii) de novo prediction of the elusive complementarity determining region (CDR) H3 loop. To alleviate model uncertainty, antibody-antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fully automated via the ROSIE web server (http://rosie.rosettacommons.org/) or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody-antigen docking. Tasks can be completed in under a day by using public supercomputers.
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Affiliation(s)
- Brian D Weitzner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeliazko R Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Nicholas Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Daisuke Kuroda
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Analytical and Physical Chemistry, Showa University School of Pharmacy, Tokyo, Japan
| | - Rahel Frick
- Centre for Immune Regulation, Department of Biosciences, University of Oslo, Oslo, Norway.,Centre for Immune Regulation, Department of Immunology, Oslo University Hospital Rikshospitalet, Oslo, Norway
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California, USA.,Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA
| | - Naireeta Biswas
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Roland L Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, USA.,Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland, USA.,Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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80
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Bohnuud T, Luo L, Wodak SJ, Vajda S, Bonvin AM, Weng Z, Schueler-Furman O, Kozakov D. A benchmark testing ground for integrating homology modeling and protein docking. Proteins 2017; 85:10-16. [PMID: 27172383 PMCID: PMC5817996 DOI: 10.1002/prot.25063] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Accepted: 05/08/2016] [Indexed: 12/20/2022]
Abstract
Protein docking procedures carry out the task of predicting the structure of a protein-protein complex starting from the known structures of the individual protein components. More often than not, however, the structure of one or both components is not known, but can be derived by homology modeling on the basis of known structures of related proteins deposited in the Protein Data Bank (PDB). Thus, the problem is to develop methods that optimally integrate homology modeling and docking with the goal of predicting the structure of a complex directly from the amino acid sequences of its component proteins. One possibility is to use the best available homology modeling and docking methods. However, the models built for the individual subunits often differ to a significant degree from the bound conformation in the complex, often much more so than the differences observed between free and bound structures of the same protein, and therefore additional conformational adjustments, both at the backbone and side chain levels need to be modeled to achieve an accurate docking prediction. In particular, even homology models of overall good accuracy frequently include localized errors that unfavorably impact docking results. The predicted reliability of the different regions in the model can also serve as a useful input for the docking calculations. Here we present a benchmark dataset that should help to explore and solve combined modeling and docking problems. This dataset comprises a subset of the experimentally solved 'target' complexes from the widely used Docking Benchmark from the Weng Lab (excluding antibody-antigen complexes). This subset is extended to include the structures from the PDB related to those of the individual components of each complex, and hence represent potential templates for investigating and benchmarking integrated homology modeling and docking approaches. Template sets can be dynamically customized by specifying ranges in sequence similarity and in PDB release dates, or using other filtering options, such as excluding sets of specific structures from the template list. Multiple sequence alignments, as well as structural alignments of the templates to their corresponding subunits in the target are also provided. The resource is accessible online or can be downloaded at http://cluspro.org/benchmark, and is updated on a weekly basis in synchrony with new PDB releases. Proteins 2016; 85:10-16. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Tanggis Bohnuud
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Lingqi Luo
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Shoshana J. Wodak
- VIB Structural Biology Research Center, VUB Pleinlaan 2, 1050 Brussels
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Department of Chemistry, Boston University, Boston, MA 02215, USA
| | - Alexandre M.J.J. Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, 3584CH, the Netherlands
| | - Zhiping Weng
- Biochemistry and Molecular Pharmacology University of Massachusetts Medical School Worcester MA United States
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Hadassah Medical School, Hebrew University, Jerusalem, Israel
| | - Dima Kozakov
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Department of Applied Mathematics and Statistics, Stony Brook University NY, USA
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81
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Abstract
Antibodies are a group of proteins responsible for mediating immune reactions in vertebrates. They are able to bind a variety of structural motifs on noxious molecules tagging them for elimination from the organism. As a result of their versatile binding properties, antibodies are currently one of the most important classes of biopharmaceuticals. In this chapter, we discuss how knowledge-based computational methods can aid experimentalists in the development of potent antibodies. When using common experimental methods for antibody development, we often know the sequence of an antibody that binds to our molecule, antigen, of interest. We may also have a structure or model of the antigen. In these cases, computational methods can help by both modeling the antibody and identifying the antibody-antigen contact residues. This information can then play a key role in the rational design of more potent antibodies.
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Affiliation(s)
| | - James Dunbar
- Department of Statistics, University of Oxford, Oxford, UK
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82
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Weitzner BD, Gray JJ. Accurate Structure Prediction of CDR H3 Loops Enabled by a Novel Structure-Based C-Terminal Constraint. THE JOURNAL OF IMMUNOLOGY 2016; 198:505-515. [PMID: 27872211 DOI: 10.4049/jimmunol.1601137] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Accepted: 09/12/2016] [Indexed: 11/19/2022]
Abstract
Ab structure prediction has made great strides, but accurately modeling CDR H3 loops remains elusive. Unlike the other five CDR loops, CDR H3 does not adopt canonical conformations and must be modeled de novo. During Antibody Modeling Assessment II, we found that biasing simulations toward kinked conformations enables generating low-root mean square deviation models (Weitzner et al. 2014. Proteins 82: 1611-1623), and since then, we have presented new geometric parameters defining the kink conformation (Weitzner et al. 2015. Structure 23: 302-311). In this study, we use these parameters to develop a new biasing constraint. When applied to a benchmark set of high-quality CDR H3 loops, the average minimum root mean square deviation sampled is 0.93 Å, compared with 1.34 Å without the constraint. We then test the performance of the constrained de novo method for homology modeling and rigid-body docking and present the results for 1) the Antibody Modeling Assessment II targets, 2) the 2009 RosettaAntibody benchmark set, and 3) the high-quality set.
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Affiliation(s)
- Brian D Weitzner
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218
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83
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Porter JR, Weitzner BD, Lange OF. A Framework to Simplify Combined Sampling Strategies in Rosetta. PLoS One 2015; 10:e0138220. [PMID: 26381271 PMCID: PMC4575156 DOI: 10.1371/journal.pone.0138220] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Accepted: 08/27/2015] [Indexed: 11/19/2022] Open
Abstract
A core task in computational structural biology is the search of conformational space for low energy configurations of a biological macromolecule. Because conformational space has a very high dimensionality, the most successful search methods integrate some form of prior knowledge into a general sampling algorithm to reduce the effective dimensionality. However, integrating multiple types of constraints can be challenging. To streamline the incorporation of diverse constraints, we developed the Broker: an extension of the Rosetta macromolecular modeling suite that can express a wide range of protocols using constraints by combining small, independent modules, each of which implements a different set of constraints. We demonstrate expressiveness of the Broker through several code vignettes. The framework enables rapid protocol development in both biomolecular design and structural modeling tasks and thus is an important step towards exposing the rich functionality of Rosetta's core libraries to a growing community of users addressing a diverse set of tasks in computational biology.
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Affiliation(s)
- Justin R. Porter
- School of Medicine, Washington University in St. Louis, Missouri, Washington, United States of America
| | - Brian D. Weitzner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Oliver F. Lange
- Lehrstuhl für Biomolekulare NMR-Spektroskopie, Fakultät Chemie, Techniche Universität München, Munich, Germany
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84
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Park H, Lee H, Seok C. High-resolution protein-protein docking by global optimization: recent advances and future challenges. Curr Opin Struct Biol 2015; 35:24-31. [PMID: 26295792 DOI: 10.1016/j.sbi.2015.08.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2015] [Revised: 07/13/2015] [Accepted: 08/03/2015] [Indexed: 01/12/2023]
Abstract
A computational protein-protein docking method that predicts atomic details of protein-protein interactions from protein monomer structures is an invaluable tool for understanding the molecular mechanisms of protein interactions and for designing molecules that control such interactions. Compared to low-resolution docking, high-resolution docking explores the conformational space in atomic resolution to provide predictions with atomic details. This allows for applications to more challenging docking problems that involve conformational changes induced by binding. Recently, high-resolution methods have become more promising as additional information such as global shapes or residue contacts are now available from experiments or sequence/structure data. In this review article, we highlight developments in high-resolution docking made during the last decade, specifically regarding global optimization methods employed by the docking methods. We also discuss two major challenges in high-resolution docking: prediction of backbone flexibility and water-mediated interactions.
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Affiliation(s)
- Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Hasup Lee
- Department of Chemistry, Seoul National University, Seoul 151-747, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 151-747, Republic of Korea.
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85
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Sela-Culang I, Ofran Y, Peters B. Antibody specific epitope prediction-emergence of a new paradigm. Curr Opin Virol 2015; 11:98-102. [PMID: 25837466 DOI: 10.1016/j.coviro.2015.03.012] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2014] [Revised: 03/11/2015] [Accepted: 03/16/2015] [Indexed: 11/19/2022]
Abstract
The development of accurate tools for predicting B-cell epitopes is important but difficult. Traditional methods have examined which regions in an antigen are likely binding sites of an antibody. However, it is becoming increasingly clear that most antigen surface residues will be able to bind one or more of the myriad of possible antibodies. In recent years, new approaches have emerged for predicting an epitope for a specific antibody, utilizing information encoded in antibody sequence or structure. Applying such antibody-specific predictions to groups of antibodies in combination with easily obtainable experimental data improves the performance of epitope predictions. We expect that further advances of such tools will be possible with the integration of immunoglobulin repertoire sequencing data.
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Affiliation(s)
- Inbal Sela-Culang
- The Goodman Faculty of Life Sciences, Nanotechnology Building, Bar-Ilan University, Ramat Gan 52900, Israel
| | - Yanay Ofran
- The Goodman Faculty of Life Sciences, Nanotechnology Building, Bar-Ilan University, Ramat Gan 52900, Israel
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, Division of Vaccine Discovery, 9420 Athena Circle, La Jolla, CA 92037, USA.
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86
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Peptides designed to spatially depict the Epstein-Barr virus major virion glycoprotein gp350 neutralization epitope elicit antibodies that block virus-neutralizing antibody 72A1 interaction with the native gp350 molecule. J Virol 2015; 89:4932-41. [PMID: 25694592 DOI: 10.1128/jvi.03269-14] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Accepted: 02/10/2015] [Indexed: 11/20/2022] Open
Abstract
UNLABELLED Epstein-Barr virus (EBV) is the etiologic agent of infectious mononucleosis and the root cause of B-cell lymphoproliferative disease in individuals with a weakened immune system, as well as a principal cofactor in nasopharyngeal carcinoma, various lymphomas, and other cancers. The EBV major virion surface glycoprotein gp350 is viewed as the best vaccine candidate to prevent infectious mononucleosis in healthy EBV-naive persons and EBV-related cancers in at-risk individuals. Previous epitope mapping of gp350 revealed only one dominant neutralizing epitope, which has been shown to be the target of the monoclonal antibody 72A1. Computer modeling of the 72A1 antibody interaction with the gp350 amino terminus was used to identify gp350 amino acids that could form strong ionic, electrostatic, or hydrogen bonds with the 72A1 antibody. Peptide DDRTTLQLAQNPVYIPETYPYIKWDN (designated peptide 2) and peptide GSAKPGNGSYFASVKTEMLGNEID (designated peptide 3) were designed to spatially represent the gp350 amino acids predicted to interact with the 72A1 antibody paratope. Peptide 2 bound to the 72A1 antibody and blocked 72A1 antibody recognition of the native gp350 molecule. Peptide 2 and peptide 3 were recognized by human IgG and shown to elicit murine antibodies that could target gp350 and block its recognition by the 72A1 antibody. This work provides a structural mapping of the interaction between the EBV-neutralizing antibody 72A1 and the major virion surface protein gp350. gp350 mimetic peptides that spatially depict the EBV-neutralizing epitope would be useful as a vaccine to focus the immune system exclusively to this important virus epitope. IMPORTANCE The production of virus-neutralizing antibodies targeting the Epstein-Barr virus (EBV) major surface glycoprotein gp350 is important for the prevention of infectious mononucleosis and EBV-related cancers. The data presented here provide the first in silico map of the gp350 interaction with a virus-blocking monoclonal antibody. Immunization with gp350 peptides identified by in silico mapping generated antibodies that cross-react with the EBV gp350 molecule and block recognition of the gp350 molecule by a virus-neutralizing antibody. Through its ability to focus the immune system exclusively on the gp350 sequence important for viral entry, these peptides may form the basis of an EBV vaccine candidate. This strategy would sidestep the production of other irrelevant gp350 antibodies that divert the immune system from generating a protective antiviral response or that impede access to the virus-blocking epitope by protective antibodies.
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87
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Structural and electrostatic analysis of HLA B-cell epitopes: inference on immunogenicity and prediction of humoral alloresponses. Curr Opin Organ Transplant 2015; 19:420-7. [PMID: 24977436 DOI: 10.1097/mot.0000000000000108] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
PURPOSE OF REVIEW The immunogenic capacity of donor human leukocyte antigen (HLA) to induce humoral immune responses is not an intrinsic property of the mismatched alloantigen but depends on the HLA phenotype of the recipient. In recent years, advances in molecular sequence technology and information from X-ray crystallography have enabled structural comparison of donor and recipient HLA type providing an opportunity for a more rational approach for determining HLA compatibility. In this article, we review studies investigating the molecular basis of antibody-antigen interactions and present computational approaches to determine the complex physiochemical and structural properties of B-cell epitopes. RECENT FINDINGS The relative immunogenicity of individual HLA mismatches may be predicted from analysis of polymorphic amino acids at continuous and discontinuous HLA sequence positions. The use of alloantigen sequence information alone, however, provides limited insight into key determinants of B-cell epitope immunogenicity, such as the orientation, accessibility and physiochemical properties of amino acid side chains. Advances in computational molecular modelling techniques now enable assessment of HLA-alloantibody interactions at the atomic level. Recent evidence supports a strong link between HLA B-cell epitope surface electrostatic potential and their immunogenicity. SUMMARY Assessment of the surface electrostatic properties of HLA alloantigens and computational analyses of HLA-alloantibody interactions represent a promising area for future research into the molecular basis of HLA immunogenicity and antigenicity.
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88
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Abstract
Antibodies recognize their cognate antigens in a precise and effective way. In order to do so, they target regions of the antigenic molecules that have specific features such as large exposed areas, presence of charged or polar atoms, specific secondary structure elements, and lack of similarity to self-proteins. Given the sequence or the structure of a protein of interest, several methods exploit such features to predict the residues that are more likely to be recognized by an immunoglobulin. Here, we present two methods (BepiPred and DiscoTope) to predict linear and discontinuous antibody epitopes from the sequence and/or the three-dimensional structure of a target protein.
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Affiliation(s)
- Morten Nielsen
- Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Paolo Marcatili
- Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark.
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89
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Abstract
ABSTRACT
With the advent of high-throughput sequencing, and the increased availability of experimental structures of antibodies and antibody-antigen complexes, comes the improvement of computational approaches to predict the structure and design the function of antibodies and antibody-antigen complexes. While antibodies pose formidable challenges for protein structure prediction and design due to their large size and highly flexible loops in the complementarity-determining regions, they also offer exciting opportunities: the central importance of antibodies for human health results in a wealth of structural and sequence information that—as a knowledge base—can drive the modeling algorithms by limiting the conformational and sequence search space to likely regions of success. Further, efficient experimental platforms exist to test predicted antibody structure or designed antibody function, thereby leading to an iterative feedback loop between computation and experiment. We briefly review the history of computer-aided prediction of structure and design of function in the antibody field before we focus on recent methodological developments and the most exciting application examples.
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90
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Pierce BG, Weng Z. A flexible docking approach for prediction of T cell receptor-peptide-MHC complexes. Protein Sci 2014; 22:35-46. [PMID: 23109003 DOI: 10.1002/pro.2181] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2012] [Accepted: 10/15/2012] [Indexed: 11/10/2022]
Abstract
T cell receptors (TCRs) are immune proteins that specifically bind to antigenic molecules, which are often foreign peptides presented by major histocompatibility complex proteins (pMHCs), playing a key role in the cellular immune response. To advance our understanding and modeling of this dynamic immunological event, we assembled a protein-protein docking benchmark consisting of 20 structures of crystallized TCR/pMHC complexes for which unbound structures exist for both TCR and pMHC. We used our benchmark to compare predictive performance using several flexible and rigid backbone TCR/pMHC docking protocols. Our flexible TCR docking algorithm, TCRFlexDock, improved predictive success over the fixed backbone protocol, leading to near-native predictions for 80% of the TCR/pMHC cases among the top 10 models, and 100% of the cases in the top 30 models. We then applied TCRFlexDock to predict the two distinct docking modes recently described for a single TCR bound to two different antigens, and tested several protein modeling scoring functions for prediction of TCR/pMHC binding affinities. This algorithm and benchmark should enable future efforts to predict, and design of uncharacterized TCR/pMHC complexes.
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Affiliation(s)
- Brian G Pierce
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA
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91
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Weitzner BD, Kuroda D, Marze N, Xu J, Gray JJ. Blind prediction performance of RosettaAntibody 3.0: grafting, relaxation, kinematic loop modeling, and full CDR optimization. Proteins 2014; 82:1611-23. [PMID: 24519881 PMCID: PMC4107143 DOI: 10.1002/prot.24534] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2013] [Revised: 01/16/2014] [Accepted: 01/28/2014] [Indexed: 01/21/2023]
Abstract
Antibody Modeling Assessment II (AMA-II) provided an opportunity to benchmark RosettaAntibody on a set of 11 unpublished antibody structures. RosettaAntibody produced accurate, physically realistic models, with all framework regions and 42 of the 55 non-H3 CDR loops predicted to under an Ångström. The performance is notable when modeling H3 on a homology framework, where RosettaAntibody produced the best model among all participants for four of the 11 targets, two of which were predicted with sub-Ångström accuracy. To improve RosettaAntibody, we pursued the causes of model errors. The most common limitation was template unavailability, underscoring the need for more antibody structures and/or better de novo loop methods. In some cases, better templates could have been found by considering residues outside of the CDRs. De novo CDR H3 modeling remains challenging at long loop lengths, but constraining the C-terminal end of H3 to a kinked conformation allows near-native conformations to be sampled more frequently. We also found that incorrect VL -VH orientations caused models with low H3 RMSDs to score poorly, suggesting that correct VL -VH orientations will improve discrimination between near-native and incorrect conformations. These observations will guide the future development of RosettaAntibody.
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Affiliation(s)
- Brian D. Weitzner
- Department of Chemical & Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD
| | - Daisuke Kuroda
- Department of Chemical & Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD
| | - Nicholas Marze
- Department of Chemical & Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD
| | - Jianqing Xu
- Department of Chemical & Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD
| | - Jeffrey J. Gray
- Department of Chemical & Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD
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92
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Wang S, Liu M, Zeng D, Qiu W, Ma P, Yu Y, Chang H, Sun Z. Increasing stability of antibody via antibody engineering: stability engineering on an anti-hVEGF. Proteins 2014; 82:2620-30. [PMID: 24916692 DOI: 10.1002/prot.24626] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Revised: 05/28/2014] [Accepted: 06/05/2014] [Indexed: 11/09/2022]
Abstract
Antibody stability is very important for expression, activity, specificity, and storage. This knowledge of antibody structure has made it possible for a computer-aided molecule design to be used to optimize and increase antibody stability. Many computational methods have been built based on knowledge or structure, however, a good integrated engineering system has yet to be developed that combines these methods. In the current study, we designed an integrated computer-aided engineering protocol, which included several successful methods. Mutants were designed considering factors that affected stability and multiwall filter screening was used to improve the design accuracy. Using this protocol, the thermo-stability of an anti-hVEGF antibody was significantly improved. Nearly 40% of the single-point mutants proved to be more stable than the parent antibody and most of the mutations could be stacked effectively. The T₅₀ also improved about 7°C by combinational mutation of seven sites in the light chain and three sites in the heavy chain. Data indicate that the protocol is an effective method for optimization of antibody structure, especially for improving thermo-stability. This protocol could also be used to enhance the stability of other antibodies.
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Affiliation(s)
- Shuang Wang
- Laboratory of Protein Engineering, Beijing Institute of Biotechnology, Beijing, China
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93
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Fasnacht M, Butenhof K, Goupil-Lamy A, Hernandez-Guzman F, Huang H, Yan L. Automated antibody structure prediction using Accelrys tools: results and best practices. Proteins 2014; 82:1583-98. [PMID: 24833271 PMCID: PMC4312887 DOI: 10.1002/prot.24604] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2013] [Revised: 04/24/2014] [Accepted: 04/29/2014] [Indexed: 12/25/2022]
Abstract
We describe the methodology and results from our participation in the second Antibody Modeling Assessment experiment. During the experiment we predicted the structure of eleven unpublished antibody Fv fragments. Our prediction methods centered on template-based modeling; potential templates were selected from an antibody database based on their sequence similarity to the target in the framework regions. Depending on the quality of the templates, we constructed models of the antibody framework regions either using a single, chimeric or multiple template approach. The hypervariable loop regions in the initial models were rebuilt by grafting the corresponding regions from suitable templates onto the model. For the H3 loop region, we further refined models using ab initio methods. The final models were subjected to constrained energy minimization to resolve severe local structural problems. The analysis of the models submitted show that Accelrys tools allow for the construction of quite accurate models for the framework and the canonical CDR regions, with RMSDs to the X-ray structure on average below 1 Å for most of these regions. The results show that accurate prediction of the H3 hypervariable loops remains a challenge. Furthermore, model quality assessment of the submitted models show that the models are of quite high quality, with local geometry assessment scores similar to that of the target X-ray structures. Proteins 2014; 82:1583–1598. © 2014 The Authors. Proteins published by Wiley Periodicals, Inc.
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94
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Krawczyk K, Liu X, Baker T, Shi J, Deane CM. Improving B-cell epitope prediction and its application to global antibody-antigen docking. Bioinformatics 2014; 30:2288-94. [PMID: 24753488 PMCID: PMC4207425 DOI: 10.1093/bioinformatics/btu190] [Citation(s) in RCA: 108] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Motivation: Antibodies are currently the most important class of biopharmaceuticals. Development of such antibody-based drugs depends on costly and time-consuming screening campaigns. Computational techniques such as antibody–antigen docking hold the potential to facilitate the screening process by rapidly providing a list of initial poses that approximate the native complex. Results: We have developed a new method to identify the epitope region on the antigen, given the structures of the antibody and the antigen—EpiPred. The method combines conformational matching of the antibody–antigen structures and a specific antibody–antigen score. We have tested the method on both a large non-redundant set of antibody–antigen complexes and on homology models of the antibodies and/or the unbound antigen structure. On a non-redundant test set, our epitope prediction method achieves 44% recall at 14% precision against 23% recall at 14% precision for a background random distribution. We use our epitope predictions to rescore the global docking results of two rigid-body docking algorithms: ZDOCK and ClusPro. In both cases including our epitope, prediction increases the number of near-native poses found among the top decoys. Availability and implementation: Our software is available from http://www.stats.ox.ac.uk/research/proteins/resources. Contact:deane@stats.ox.ac.uk Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Konrad Krawczyk
- Department of Statistics, Oxford University, OX1 3TG, Oxford, UCB Pharma, SL1 3WE Slough, UK and Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - Xiaofeng Liu
- Department of Statistics, Oxford University, OX1 3TG, Oxford, UCB Pharma, SL1 3WE Slough, UK and Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - Terry Baker
- Department of Statistics, Oxford University, OX1 3TG, Oxford, UCB Pharma, SL1 3WE Slough, UK and Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - Jiye Shi
- Department of Statistics, Oxford University, OX1 3TG, Oxford, UCB Pharma, SL1 3WE Slough, UK and Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, ChinaDepartment of Statistics, Oxford University, OX1 3TG, Oxford, UCB Pharma, SL1 3WE Slough, UK and Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - Charlotte M Deane
- Department of Statistics, Oxford University, OX1 3TG, Oxford, UCB Pharma, SL1 3WE Slough, UK and Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
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95
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Almagro JC, Gilliland GL, Breden F, Scott JK, Sok D, Pauthner M, Reichert JM, Helguera G, Andrabi R, Mabry R, Bléry M, Voss JE, Laurén J, Abuqayyas L, Barghorn S, Ben-Jacob E, Crowe JE, Huston JS, Johnston SA, Krauland E, Lund-Johansen F, Marasco WA, Parren PWHI, Xu KY. Antibody engineering and therapeutics, The Annual Meeting of the Antibody Society: December 8-12, 2013, Huntington Beach, CA. MAbs 2014; 6:577-618. [PMID: 24589717 PMCID: PMC4011904 DOI: 10.4161/mabs.28421] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
The 24th Antibody Engineering & Therapeutics meeting brought together a broad range of participants who were updated on the latest advances in antibody research and development. Organized by IBC Life Sciences, the gathering is the annual meeting of The Antibody Society, which serves as the scientific sponsor. Preconference workshops on 3D modeling and delineation of clonal lineages were featured, and the conference included sessions on a wide variety of topics relevant to researchers, including systems biology; antibody deep sequencing and repertoires; the effects of antibody gene variation and usage on antibody response; directed evolution; knowledge-based design; antibodies in a complex environment; polyreactive antibodies and polyspecificity; the interface between antibody therapy and cellular immunity in cancer; antibodies in cardiometabolic medicine; antibody pharmacokinetics, distribution and off-target toxicity; optimizing antibody formats for immunotherapy; polyclonals, oligoclonals and bispecifics; antibody discovery platforms; and antibody-drug conjugates.
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Affiliation(s)
| | | | | | | | - Devin Sok
- The Scripps Research Institute; La Jolla, CA USA
| | | | | | - Gustavo Helguera
- CONICET; Laboratorio Biotecnología Farmacéutica; Instituto de Biología y Medicina Experimental, IBYME; Ciudad Autónoma de Buenos Aires, Argentina
| | | | | | | | - James E Voss
- The Scripps Research Institute; La Jolla, CA USA
| | - Juha Laurén
- Regeneron Pharmaceuticals, Inc.; Tarrytown, NY USA
| | | | | | | | - James E Crowe
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | | | | | | | - Kai Y Xu
- University of Maryland; Baltimore, MD USA
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96
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Kilambi KP, Pacella MS, Xu J, Labonte JW, Porter JR, Muthu P, Drew K, Kuroda D, Schueler-Furman O, Bonneau R, Gray JJ. Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20-27. Proteins 2013; 81:2201-9. [PMID: 24123494 PMCID: PMC4037910 DOI: 10.1002/prot.24425] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Revised: 09/12/2013] [Accepted: 09/13/2013] [Indexed: 11/09/2022]
Abstract
Rounds 20-27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2-Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4-Rep2 and g-type lysozyme-PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 Å and 2.0 Å, respectively. A novel flexible sugar-protein docking protocol was also developed and used for structure prediction of the BT4661-heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems.
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Affiliation(s)
- Krishna Praneeth Kilambi
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Michael S. Pacella
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jianqing Xu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jason W. Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Justin R. Porter
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Pravin Muthu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Kevin Drew
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York
| | - Daisuke Kuroda
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
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97
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Krawczyk K, Baker T, Shi J, Deane CM. Antibody i-Patch prediction of the antibody binding site improves rigid local antibody-antigen docking. Protein Eng Des Sel 2013; 26:621-9. [PMID: 24006373 DOI: 10.1093/protein/gzt043] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Antibodies are a class of proteins indispensable for the vertebrate immune system. The general architecture of all antibodies is very similar, but they contain a hypervariable region which allows millions of antibody variants to exist, each of which can bind to different molecules. This binding malleability means that antibodies are an increasingly important category of biopharmaceuticals and biomarkers. We present Antibody i-Patch, a method that annotates the most likely antibody residues to be in contact with the antigen. We show that our predictions correlate with energetic importance and thus we argue that they may be useful in guiding mutations in the artificial affinity maturation process. Using our predictions as constraints for a rigid-body docking algorithm, we are able to obtain high-quality results in minutes. Our annotation method and re-scoring system for docking achieve their predictive power by using antibody-specific statistics. Antibody i-Patch is available from http://www.stats.ox.ac.uk/research/proteins/resources.
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Affiliation(s)
- Konrad Krawczyk
- Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK
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98
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Brenke R, Hall DR, Chuang GY, Comeau SR, Bohnuud T, Beglov D, Schueler-Furman O, Vajda S, Kozakov D. Application of asymmetric statistical potentials to antibody-protein docking. ACTA ACUST UNITED AC 2013; 28:2608-14. [PMID: 23053206 DOI: 10.1093/bioinformatics/bts493] [Citation(s) in RCA: 143] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION An effective docking algorithm for antibody-protein antigen complex prediction is an important first step toward design of biologics and vaccines. We have recently developed a new class of knowledge-based interaction potentials called Decoys as the Reference State (DARS) and incorporated DARS into the docking program PIPER based on the fast Fourier transform correlation approach. Although PIPER was the best performer in the latest rounds of the CAPRI protein docking experiment, it is much less accurate for docking antibody-protein antigen pairs than other types of complexes, in spite of incorporating sequence-based information on the location of the paratope. Analysis of antibody-protein antigen complexes has revealed an inherent asymmetry within these interfaces. Specifically, phenylalanine, tryptophan and tyrosine residues highly populate the paratope of the antibody but not the epitope of the antigen. RESULTS Since this asymmetry cannot be adequately modeled using a symmetric pairwise potential, we have removed the usual assumption of symmetry. Interaction statistics were extracted from antibody-protein complexes under the assumption that a particular atom on the antibody is different from the same atom on the antigen protein. The use of the new potential significantly improves the performance of docking for antibody-protein antigen complexes, even without any sequence information on the location of the paratope. We note that the asymmetric potential captures the effects of the multi-body interactions inherent to the complex environment in the antibody-protein antigen interface. AVAILABILITY The method is implemented in the ClusPro protein docking server, available at http://cluspro.bu.edu.
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Affiliation(s)
- Ryan Brenke
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
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99
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Lyskov S, Chou FC, Conchúir SÓ, Der BS, Drew K, Kuroda D, Xu J, Weitzner BD, Renfrew PD, Sripakdeevong P, Borgo B, Havranek JJ, Kuhlman B, Kortemme T, Bonneau R, Gray JJ, Das R. Serverification of molecular modeling applications: the Rosetta Online Server that Includes Everyone (ROSIE). PLoS One 2013; 8:e63906. [PMID: 23717507 PMCID: PMC3661552 DOI: 10.1371/journal.pone.0063906] [Citation(s) in RCA: 275] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2013] [Accepted: 04/04/2013] [Indexed: 11/21/2022] Open
Abstract
The Rosetta molecular modeling software package provides experimentally tested and rapidly evolving tools for the 3D structure prediction and high-resolution design of proteins, nucleic acids, and a growing number of non-natural polymers. Despite its free availability to academic users and improving documentation, use of Rosetta has largely remained confined to developers and their immediate collaborators due to the code's difficulty of use, the requirement for large computational resources, and the unavailability of servers for most of the Rosetta applications. Here, we present a unified web framework for Rosetta applications called ROSIE (Rosetta Online Server that Includes Everyone). ROSIE provides (a) a common user interface for Rosetta protocols, (b) a stable application programming interface for developers to add additional protocols, (c) a flexible back-end to allow leveraging of computer cluster resources shared by RosettaCommons member institutions, and (d) centralized administration by the RosettaCommons to ensure continuous maintenance. This paper describes the ROSIE server infrastructure, a step-by-step 'serverification' protocol for use by Rosetta developers, and the deployment of the first nine ROSIE applications by six separate developer teams: Docking, RNA de novo, ERRASER, Antibody, Sequence Tolerance, Supercharge, Beta peptide design, NCBB design, and VIP redesign. As illustrated by the number and diversity of these applications, ROSIE offers a general and speedy paradigm for serverification of Rosetta applications that incurs negligible cost to developers and lowers barriers to Rosetta use for the broader biological community. ROSIE is available at http://rosie.rosettacommons.org.
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Affiliation(s)
- Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Fang-Chieh Chou
- Department of Biochemistry, Stanford University School of Medicine, Stanford, California, United States of America
| | - Shane Ó. Conchúir
- California Institute for Quantitative Biomedical Research, University of California San Francisco, San Francisco, California, United States of America
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
| | - Bryan S. Der
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Kevin Drew
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
| | - Daisuke Kuroda
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Jianqing Xu
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Brian D. Weitzner
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - P. Douglas Renfrew
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
| | - Parin Sripakdeevong
- Biophysics Program, Stanford University, Stanford, California, United States of America
| | - Benjamin Borgo
- Department of Genetics, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - James J. Havranek
- Department of Genetics, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Tanja Kortemme
- California Institute for Quantitative Biomedical Research, University of California San Francisco, San Francisco, California, United States of America
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- Graduate Group in Biophysics, University of California San Francisco, San Francisco, California, United States of America
| | - Richard Bonneau
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
- Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, New York, United States of America
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, California, United States of America
- Department of Physics, Stanford University, Stanford, California, United States of America
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100
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Xu J, Tack D, Hughes RA, Ellington AD, Gray JJ. Structure-based non-canonical amino acid design to covalently crosslink an antibody-antigen complex. J Struct Biol 2013; 185:215-22. [PMID: 23680795 DOI: 10.1016/j.jsb.2013.05.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2013] [Revised: 04/04/2013] [Accepted: 05/02/2013] [Indexed: 10/26/2022]
Abstract
Engineering antibodies to utilize non-canonical amino acids (NCAA) should greatly expand the utility of an already important biological reagent. In particular, introducing crosslinking reagents into antibody complementarity determining regions (CDRs) should provide a means to covalently crosslink residues at the antibody-antigen interface. Unfortunately, finding the optimum position for crosslinking two proteins is often a matter of iterative guessing, even when the interface is known in atomic detail. Computer-aided antibody design can potentially greatly restrict the number of variants that must be explored in order to identify successful crosslinking sites. We have therefore used Rosetta to guide the introduction of an oxidizable crosslinking NCAA, l-3,4-dihydroxyphenylalanine (l-DOPA), into the CDRs of the anti-protective antigen scFv antibody M18, and have measured crosslinking to its cognate antigen, domain 4 of the anthrax protective antigen. Computed crosslinking distance, solvent accessibility, and interface energetics were three factors considered that could impact the efficiency of l-DOPA-mediated crosslinking. In the end, 10 variants were synthesized, and crosslinking efficiencies were generally 10% or higher, with the best variant crosslinking to 52% of the available antigen. The results suggest that computational analysis can be used in a pipeline for engineering crosslinking antibodies. The rules learned from l-DOPA crosslinking of antibodies may also be generalizable to the formation of other crosslinked interfaces and complexes.
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Affiliation(s)
- Jianqing Xu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Drew Tack
- Department of Chemistry and Biochemistry, Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Randall A Hughes
- Department of Chemistry and Biochemistry, Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX 78712, USA; Applied Research Laboratories, The University of Texas at Austin, Austin, TX 78758, USA
| | - Andrew D Ellington
- Department of Chemistry and Biochemistry, Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX 78712, USA; Applied Research Laboratories, The University of Texas at Austin, Austin, TX 78758, USA.
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
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