Assessment of flexible backbone protein design methods for sequence library prediction in the therapeutic antibody Herceptin-HER2 interface
Computational protein design methods can complement experimental screening and selection techniques by predicting libraries of low-energy sequences compatible with a desired structure and function. Incorporating backbone flexibility in computational design allows conformational adjustments that shou...
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Published in | Protein science Vol. 20; no. 6; pp. 1082 - 1089 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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United States
Wiley Subscription Services, Inc
01.06.2011
Wiley Subscription Services, Inc., A Wiley Company |
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Abstract | Computational protein design methods can complement experimental screening and selection techniques by predicting libraries of low-energy sequences compatible with a desired structure and function. Incorporating backbone flexibility in computational design allows conformational adjustments that should broaden the range of predicted low-energy sequences. Here, we evaluate computational predictions of sequence libraries from different protocols for modeling backbone flexibility using the complex between the therapeutic antibody Herceptin and its target human epidermal growth factor receptor 2 (HER2) as a model system. Within the program RosettaDesign, three methods are compared: The first two use ensembles of structures generated by Monte Carlo protocols for near-native conformational sampling: kinematic closure (KIC) and backrub, and the third method uses snapshots from molecular dynamics (MD) simulations. KIC or backrub methods were better able to identify the amino acid residues experimentally observed by phage display in the Herceptin-HER2 interface than MD snapshots, which generated much larger conformational and sequence diversity. KIC and backrub, as well as fixed backbone simulations, captured the key mutation Asp98Trp in Herceptin, which leads to a further threefold affinity improvement of the already subnanomolar parental Herceptin-HER2 interface. Modeling subtle backbone conformational changes may assist in the design of sequence libraries for improving the affinity of antibody-antigen interfaces and could be suitable for other protein complexes for which structural information is available. |
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AbstractList | Computational protein design methods can complement experimental screening and selection techniques by predicting libraries of low-energy sequences compatible with a desired structure and function. Incorporating backbone flexibility in computational design allows conformational adjustments that should broaden the range of predicted low-energy sequences. Here, we evaluate computational predictions of sequence libraries from different protocols for modeling backbone flexibility using the complex between the therapeutic antibody Herceptin and its target human epidermal growth factor receptor 2 (HER2) as a model system. Within the program RosettaDesign, three methods are compared: The first two use ensembles of structures generated by Monte Carlo protocols for near-native conformational sampling: kinematic closure (KIC) and backrub, and the third method uses snapshots from molecular dynamics (MD) simulations. KIC or backrub methods were better able to identify the amino acid residues experimentally observed by phage display in the Herceptin-HER2 interface than MD snapshots, which generated much larger conformational and sequence diversity. KIC and backrub, as well as fixed backbone simulations, captured the key mutation Asp98Trp in Herceptin, which leads to a further threefold affinity improvement of the already subnanomolar parental Herceptin-HER2 interface. Modeling subtle backbone conformational changes may assist in the design of sequence libraries for improving the affinity of antibody-antigen interfaces and could be suitable for other protein complexes for which structural information is available. [PUBLICATION ABSTRACT] Computational protein design methods can complement experimental screening and selection techniques by predicting libraries of low-energy sequences compatible with a desired structure and function. Incorporating backbone flexibility in computational design allows conformational adjustments that should broaden the range of predicted low-energy sequences. Here, we evaluate computational predictions of sequence libraries from different protocols for modeling backbone flexibility using the complex between the therapeutic antibody Herceptin and its target human epidermal growth factor receptor 2 (HER2) as a model system. Within the program RosettaDesign, three methods are compared: The first two use ensembles of structures generated by Monte Carlo protocols for near-native conformational sampling: kinematic closure (KIC) and backrub, and the third method uses snapshots from molecular dynamics (MD) simulations. KIC or backrub methods were better able to identify the amino acid residues experimentally observed by phage display in the Herceptin-HER2 interface than MD snapshots, which generated much larger conformational and sequence diversity. KIC and backrub, as well as fixed backbone simulations, captured the key mutation Asp98Trp in Herceptin, which leads to a further threefold affinity improvement of the already subnanomolar parental Herceptin-HER2 interface. Modeling subtle backbone conformational changes may assist in the design of sequence libraries for improving the affinity of antibody-antigen interfaces and could be suitable for other protein complexes for which structural information is available. Abstract Computational protein design methods can complement experimental screening and selection techniques by predicting libraries of low‐energy sequences compatible with a desired structure and function. Incorporating backbone flexibility in computational design allows conformational adjustments that should broaden the range of predicted low‐energy sequences. Here, we evaluate computational predictions of sequence libraries from different protocols for modeling backbone flexibility using the complex between the therapeutic antibody Herceptin and its target human epidermal growth factor receptor 2 (HER2) as a model system. Within the program RosettaDesign, three methods are compared: The first two use ensembles of structures generated by Monte Carlo protocols for near‐native conformational sampling: kinematic closure (KIC) and backrub, and the third method uses snapshots from molecular dynamics (MD) simulations. KIC or backrub methods were better able to identify the amino acid residues experimentally observed by phage display in the Herceptin–HER2 interface than MD snapshots, which generated much larger conformational and sequence diversity. KIC and backrub, as well as fixed backbone simulations, captured the key mutation Asp98Trp in Herceptin, which leads to a further threefold affinity improvement of the already subnanomolar parental Herceptin‐HER2 interface. Modeling subtle backbone conformational changes may assist in the design of sequence libraries for improving the affinity of antibody–antigen interfaces and could be suitable for other protein complexes for which structural information is available. |
Author | Kortemme, Tanja Mandell, Daniel J Babor, Mariana |
AuthorAffiliation | 2 Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California 94158-2330 1 California Institute for Quantitative Biomedical Research, University of California San Francisco, San Francisco, California 94158-2330 |
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Cites_doi | 10.1126/science.278.5335.82 10.1093/protein/gzp042 10.1002/prot.10613 10.1016/S0022-2836(02)00677-0 10.1016/j.str.2005.10.007 10.1038/nbt0905-1073 10.1016/j.jmb.2008.05.006 10.1016/j.sbi.2004.03.001 10.1006/jmbi.2001.4949 10.1371/journal.pcbi.0020085 10.1073/pnas.212627499 10.1016/j.str.2008.09.012 10.1073/pnas.0707977104 10.1002/prot.20185 10.1016/j.jmb.2008.05.023 10.1016/0022-2836(87)90358-5 10.1016/j.jmb.2010.07.032 10.1016/j.jmb.2007.04.069 10.1016/0021-9991(77)90098-5 10.1126/science.282.5393.1462 10.1126/science.1089427 10.1002/pro.5560060810 10.1016/j.str.2007.09.024 10.1529/biophysj.107.110627 10.1006/jmbi.1999.2866 10.1007/978-94-015-7658-1_21 10.1073/pnas.0609647103 10.1038/nmeth0809-551 10.1093/bioinformatics/btm197 10.1016/j.copbio.2009.07.006 10.1007/s008940100045 10.1110/ps.0203902 10.1016/j.sbi.2010.02.004 10.1371/journal.pcbi.1000393 10.1063/1.448118 |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Additional Supporting Information may be found in the online version of this article. Grant sponsor: NSF; Grant numbers: MCB-0744541 and EF-0849400 (to T.K.); Grant sponsors: University of California Lab Research Program (to T.K.) and PhRMA Foundation Predoctoral Fellowship (to D.J.M.). Author contributions: M.B.: conceived and designed the experiments, performed the experiments and analyzed the data, discussed the data and wrote the manuscript; T.K.: conceived and designed the experiments, discussed the data and wrote the manuscript; D.J.M.: developed the KIC refinement design protocol, discussed the data and wrote the manuscript. Mariana Babor's current address is Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, California 92037. |
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References | 17971437 - Proc Natl Acad Sci U S A. 2007 Nov 6;104(45):17668-73 16472746 - Structure. 2006 Feb;14(2):265-74 19081054 - Structure. 2008 Dec 10;16(12):1777-88 17827237 - Biophys J. 2008 Jan 15;94(2):584-99 18547585 - J Mol Biol. 2008 Jul 18;380(4):742-56 11545603 - J Mol Biol. 2001 Sep 7;312(1):289-307 9822371 - Science. 1998 Nov 20;282(5393):1462-7 18073107 - Structure. 2007 Dec;15(12):1567-76 17646295 - Bioinformatics. 2007 Jul 1;23(13):i185-94 12441379 - Protein Sci. 2002 Dec;11(12):2804-13 2441069 - J Mol Biol. 1987 Feb 20;193(4):775-91 19709874 - Curr Opin Biotechnol. 2009 Aug;20(4):420-8 14631033 - Science. 2003 Nov 21;302(5649):1364-8 9311930 - Science. 1997 Oct 3;278(5335):82-7 20654621 - J Mol Biol. 2010 Sep 17;402(2):460-74 15048827 - Proteins. 2004 May 1;55(2):351-67 19644455 - Nat Methods. 2009 Aug;6(8):551-2 16151394 - Nat Biotechnol. 2005 Sep;23(9):1073-8 15093835 - Curr Opin Struct Biol. 2004 Apr;14(2):202-7 19643976 - Protein Eng Des Sel. 2009 Sep;22(9):575-86 16839198 - PLoS Comput Biol. 2006 Jul 7;2(7):e85 18547586 - J Mol Biol. 2008 Jul 18;380(4):757-74 12206766 - J Mol Biol. 2002 Aug 30;321(5):851-62 10388574 - J Mol Biol. 1999 Jul 2;290(1):305-18 17179210 - Proc Natl Acad Sci U S A. 2007 Jan 2;104(1):48-53 9260282 - Protein Sci. 1997 Aug;6(8):1701-7 17597151 - J Mol Biol. 2007 Aug 24;371(4):1099-117 15340927 - Proteins. 2004 Nov 1;57(2):400-13 20303740 - Curr Opin Struct Biol. 2010 Jun;20(3):377-84 19478996 - PLoS Comput Biol. 2009 May;5(5):e1000393 12446841 - Proc Natl Acad Sci U S A. 2002 Dec 10;99(25):15926-31 e_1_2_7_5_2 e_1_2_7_4_2 e_1_2_7_3_2 e_1_2_7_2_2 e_1_2_7_9_2 e_1_2_7_8_2 e_1_2_7_7_2 e_1_2_7_6_2 e_1_2_7_19_2 e_1_2_7_18_2 e_1_2_7_17_2 e_1_2_7_16_2 e_1_2_7_15_2 e_1_2_7_14_2 e_1_2_7_13_2 e_1_2_7_12_2 e_1_2_7_11_2 e_1_2_7_10_2 e_1_2_7_26_2 e_1_2_7_27_2 e_1_2_7_28_2 e_1_2_7_29_2 Press W (e_1_2_7_32_2) 2007 van Gunsteren WF (e_1_2_7_34_2) 1996 e_1_2_7_25_2 e_1_2_7_24_2 e_1_2_7_30_2 e_1_2_7_23_2 e_1_2_7_31_2 e_1_2_7_22_2 e_1_2_7_21_2 e_1_2_7_33_2 e_1_2_7_20_2 e_1_2_7_35_2 e_1_2_7_36_2 e_1_2_7_37_2 e_1_2_7_38_2 |
References_xml | – ident: e_1_2_7_3_2 doi: 10.1126/science.278.5335.82 – ident: e_1_2_7_24_2 doi: 10.1093/protein/gzp042 – ident: e_1_2_7_35_2 doi: 10.1002/prot.10613 – ident: e_1_2_7_28_2 doi: 10.1016/S0022-2836(02)00677-0 – ident: e_1_2_7_26_2 doi: 10.1016/j.str.2005.10.007 – ident: e_1_2_7_25_2 doi: 10.1038/nbt0905-1073 – ident: e_1_2_7_31_2 doi: 10.1016/j.jmb.2008.05.006 – ident: e_1_2_7_5_2 doi: 10.1016/j.sbi.2004.03.001 – ident: e_1_2_7_4_2 doi: 10.1006/jmbi.2001.4949 – ident: e_1_2_7_11_2 doi: 10.1371/journal.pcbi.0020085 – ident: e_1_2_7_29_2 doi: 10.1073/pnas.212627499 – ident: e_1_2_7_17_2 doi: 10.1016/j.str.2008.09.012 – ident: e_1_2_7_22_2 doi: 10.1073/pnas.0707977104 – ident: e_1_2_7_14_2 doi: 10.1002/prot.20185 – ident: e_1_2_7_16_2 doi: 10.1016/j.jmb.2008.05.023 – ident: e_1_2_7_2_2 doi: 10.1016/0022-2836(87)90358-5 – ident: e_1_2_7_23_2 doi: 10.1016/j.jmb.2010.07.032 – ident: e_1_2_7_13_2 doi: 10.1016/j.jmb.2007.04.069 – ident: e_1_2_7_37_2 doi: 10.1016/0021-9991(77)90098-5 – ident: e_1_2_7_19_2 doi: 10.1126/science.282.5393.1462 – ident: e_1_2_7_20_2 doi: 10.1126/science.1089427 – ident: e_1_2_7_8_2 doi: 10.1002/pro.5560060810 – ident: e_1_2_7_15_2 doi: 10.1016/j.str.2007.09.024 – ident: e_1_2_7_21_2 doi: 10.1529/biophysj.107.110627 – ident: e_1_2_7_9_2 doi: 10.1006/jmbi.1999.2866 – ident: e_1_2_7_36_2 doi: 10.1007/978-94-015-7658-1_21 – ident: e_1_2_7_30_2 doi: 10.1073/pnas.0609647103 – ident: e_1_2_7_27_2 doi: 10.1038/nmeth0809-551 – ident: e_1_2_7_12_2 doi: 10.1093/bioinformatics/btm197 – ident: e_1_2_7_7_2 doi: 10.1016/j.copbio.2009.07.006 – ident: e_1_2_7_33_2 doi: 10.1007/s008940100045 – ident: e_1_2_7_10_2 doi: 10.1110/ps.0203902 – volume-title: Biomolecular simulation: the GROMOS96 manual and user guide year: 1996 ident: e_1_2_7_34_2 contributor: fullname: van Gunsteren WF – ident: e_1_2_7_6_2 doi: 10.1016/j.sbi.2010.02.004 – ident: e_1_2_7_18_2 doi: 10.1371/journal.pcbi.1000393 – volume-title: Numerical Recipes: The Art of Scientific Computing year: 2007 ident: e_1_2_7_32_2 contributor: fullname: Press W – ident: e_1_2_7_38_2 doi: 10.1063/1.448118 |
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Snippet | Computational protein design methods can complement experimental screening and selection techniques by predicting libraries of low-energy sequences compatible... Abstract Computational protein design methods can complement experimental screening and selection techniques by predicting libraries of low‐energy sequences... |
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SubjectTerms | Amino Acid Sequence Antibodies, Monoclonal - chemistry Antibodies, Monoclonal - immunology Antibodies, Monoclonal, Humanized Antigen-Antibody Complex - chemistry Antigen-Antibody Complex - immunology Computer Simulation Drug Design For the Record Humans Libraries Methods Models, Molecular Molecular Sequence Data Protein Conformation Proteins Receptor, ErbB-2 - chemistry Receptor, ErbB-2 - immunology Trastuzumab |
Title | Assessment of flexible backbone protein design methods for sequence library prediction in the therapeutic antibody Herceptin-HER2 interface |
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