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Title: Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1

Abstract

The rise of antibiotic resistance calls for new therapeutics targeting resistance factors such as the New Delhi metallo-β-lactamase 1 (NDM-1), a bacterial enzyme that degrades β-lactam antibiotics. We present structure-guided computational methods for designing peptide macrocycles built from mixtures ofl- andd-amino acids that are able to bind to and inhibit targets of therapeutic interest. Our methods explicitly consider the propensity of a peptide to favor a binding-competent conformation, which we found to predict rank order of experimentally observed IC50 values across seven designed NDM-1- inhibiting peptides. We were able to determine X-ray crystal structures of three of the designed inhibitors in complex with NDM-1, and in all three the conformation of the peptide is very close to the computationally designed model. In two of the three structures, the binding mode with NDM-1 is also very similar to the design model, while in the third, we observed an alternative binding mode likely arising from internal symmetry in the shape of the design combined with flexibility of the target. Although challenges remain in robustly predicting target backbone changes, binding mode, and the effects of mutations on binding affinity, our methods for designing ordered, binding-competent macrocycles should have broad applicability to a widemore » range of therapeutic targets.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [2];  [3];  [3];  [2];  [3];  [2]; ORCiD logo [3];  [4];  [5];  [6];  [7];  [8];  [9];  [2]; ORCiD logo [3]
  1. Flatiron Institute, New York, NY (United States). Center for Computational Biology; Univ. of Washington, Seattle, WA (United States). Dept. of Biochemistry, Molecular Engineering and Sciences. Inst. for Protein Design
  2. Univ. of British Columbia, Vancouver, BC (Canada). Centre for Blood Research. Dept. of Biochemistry and Molecular Biology
  3. Univ. of Washington, Seattle, WA (United States). Dept. of Biochemistry, Molecular Engineering and Sciences. Inst. for Protein Design
  4. Stanford Univ., CA (United States). School of Medicine. Dept. of Biochemistry
  5. Flatiron Institute, New York, NY (United States). Center for Computational Biology
  6. Univ. of North Carolina, Chapel Hill, NC (United States). Dept. of Biochemistry and Biophysics
  7. Franklin & Marshall College, Lancaster, PA (United States). Dept. of Chemistry; Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Chemical & Biomolecular Engineering
  8. Vanderbilt Univ., Nashville, TN (United States). Dept. of Chemistry. Center for Structural Biology
  9. Flatiron Institute, New York, NY (United States). Center for Computational Biology; New York Univ. (NYU), NY (United States). Dept. of Biology. Center for Genomics and Systems Biology; New York Univ. (NYU), NY (United States). Dept. of Computer Science. Courant Inst. of Mathematical Sciences
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
OSTI Identifier:
1816323
Grant/Contract Number:  
AC02-05CH11231; AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
Proceedings of the National Academy of Sciences of the United States of America
Additional Journal Information:
Journal Volume: 118; Journal Issue: 12; Journal ID: ISSN 0027-8424
Publisher:
National Academy of Sciences
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 42 ENGINEERING; antibiotic resistance; drug design; protein folding; peptide macrocycles; computational design

Citation Formats

Mulligan, Vikram Khipple, Workman, Sean, Sun, Tianjun, Rettie, Stephen, Li, Xinting, Worrall, Liam J., Craven, Timothy W., King, Dustin T., Hosseinzadeh, Parisa, Watkins, Andrew M., Renfrew, P. Douglas, Guffy, Sharon, Labonte, Jason W., Moretti, Rocco, Bonneau, Richard, Strynadka, Natalie C. J., and Baker, David. Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1. United States: N. p., 2021. Web. doi:10.1073/pnas.2012800118.
Mulligan, Vikram Khipple, Workman, Sean, Sun, Tianjun, Rettie, Stephen, Li, Xinting, Worrall, Liam J., Craven, Timothy W., King, Dustin T., Hosseinzadeh, Parisa, Watkins, Andrew M., Renfrew, P. Douglas, Guffy, Sharon, Labonte, Jason W., Moretti, Rocco, Bonneau, Richard, Strynadka, Natalie C. J., & Baker, David. Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1. United States. https://doi.org/10.1073/pnas.2012800118
Mulligan, Vikram Khipple, Workman, Sean, Sun, Tianjun, Rettie, Stephen, Li, Xinting, Worrall, Liam J., Craven, Timothy W., King, Dustin T., Hosseinzadeh, Parisa, Watkins, Andrew M., Renfrew, P. Douglas, Guffy, Sharon, Labonte, Jason W., Moretti, Rocco, Bonneau, Richard, Strynadka, Natalie C. J., and Baker, David. Mon . "Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1". United States. https://doi.org/10.1073/pnas.2012800118. https://www.osti.gov/servlets/purl/1816323.
@article{osti_1816323,
title = {Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1},
author = {Mulligan, Vikram Khipple and Workman, Sean and Sun, Tianjun and Rettie, Stephen and Li, Xinting and Worrall, Liam J. and Craven, Timothy W. and King, Dustin T. and Hosseinzadeh, Parisa and Watkins, Andrew M. and Renfrew, P. Douglas and Guffy, Sharon and Labonte, Jason W. and Moretti, Rocco and Bonneau, Richard and Strynadka, Natalie C. J. and Baker, David},
abstractNote = {The rise of antibiotic resistance calls for new therapeutics targeting resistance factors such as the New Delhi metallo-β-lactamase 1 (NDM-1), a bacterial enzyme that degrades β-lactam antibiotics. We present structure-guided computational methods for designing peptide macrocycles built from mixtures ofl- andd-amino acids that are able to bind to and inhibit targets of therapeutic interest. Our methods explicitly consider the propensity of a peptide to favor a binding-competent conformation, which we found to predict rank order of experimentally observed IC50 values across seven designed NDM-1- inhibiting peptides. We were able to determine X-ray crystal structures of three of the designed inhibitors in complex with NDM-1, and in all three the conformation of the peptide is very close to the computationally designed model. In two of the three structures, the binding mode with NDM-1 is also very similar to the design model, while in the third, we observed an alternative binding mode likely arising from internal symmetry in the shape of the design combined with flexibility of the target. Although challenges remain in robustly predicting target backbone changes, binding mode, and the effects of mutations on binding affinity, our methods for designing ordered, binding-competent macrocycles should have broad applicability to a wide range of therapeutic targets.},
doi = {10.1073/pnas.2012800118},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 12,
volume = 118,
place = {United States},
year = {Mon Mar 15 00:00:00 EDT 2021},
month = {Mon Mar 15 00:00:00 EDT 2021}
}

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