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Title: Peptide design by optimization on a data-parameterized protein interaction landscape

Abstract

Many applications in protein engineering require optimizing multiple protein properties simultaneously, such as binding one target but not others or binding a target while maintaining stability. Such multistate design problems require navigating a high-dimensional space to find proteins with desired characteristics. A model that relates protein sequence to functional attributes can guide design to solutions that would be hard to discover via screening. In this work, we measured thousands of protein–peptide binding affinities with the high-throughput interaction assay amped SORTCERY and used the data to parameterize a model of the alpha-helical peptide-binding landscape for three members of the Bcl-2 family of proteins: Bcl-x L, Mcl-1, and Bfl-1. We applied optimization protocols to explore extremes in this landscape to discover peptides with desired interaction profiles. Computational design generated 36 peptides, all of which bound with high affinity and specificity to just one of Bcl-x L, Mcl-1, or Bfl-1, as intended. We designed additional peptides that bound selectively to two out of three of these proteins. The designed peptides were dissimilar to known Bcl-2–binding peptides, and high-resolution crystal structures confirmed that they engaged their targets as expected. Excellent results on this challenging problem demonstrate the power of a landscape modeling approach, andmore » the designed peptides have potential uses as diagnostic tools or cancer therapeutics.« less

Authors:
 [1];  [1];  [1];  [1];  [1]; ORCiD logo [2]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  2. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Koch Inst. for Integrative Cancer Research, Cambridge, MA (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States). Advanced Photon Source (APS)
Sponsoring Org.:
National Science Foundation (NSF)
OSTI Identifier:
1513012
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Proceedings of the National Academy of Sciences of the United States of America
Additional Journal Information:
Journal Volume: 115; Journal Issue: 44; Journal ID: ISSN 0027-8424
Publisher:
National Academy of Sciences
Country of Publication:
United States
Language:
ENGLISH
Subject:
59 BASIC BIOLOGICAL SCIENCES; protein–protein interactions; peptide design; energy landscape; Bcl-2 inhibitor; machine learning

Citation Formats

Jenson, Justin M., Xue, Vincent, Stretz, Lindsey, Mandal, Tirtha, Reich, Lothar “Luther”, and Keating, Amy E. Peptide design by optimization on a data-parameterized protein interaction landscape. United States: N. p., 2018. Web. doi:10.1073/pnas.1812939115.
Jenson, Justin M., Xue, Vincent, Stretz, Lindsey, Mandal, Tirtha, Reich, Lothar “Luther”, & Keating, Amy E. Peptide design by optimization on a data-parameterized protein interaction landscape. United States. doi:10.1073/pnas.1812939115.
Jenson, Justin M., Xue, Vincent, Stretz, Lindsey, Mandal, Tirtha, Reich, Lothar “Luther”, and Keating, Amy E. Mon . "Peptide design by optimization on a data-parameterized protein interaction landscape". United States. doi:10.1073/pnas.1812939115. https://www.osti.gov/servlets/purl/1513012.
@article{osti_1513012,
title = {Peptide design by optimization on a data-parameterized protein interaction landscape},
author = {Jenson, Justin M. and Xue, Vincent and Stretz, Lindsey and Mandal, Tirtha and Reich, Lothar “Luther” and Keating, Amy E.},
abstractNote = {Many applications in protein engineering require optimizing multiple protein properties simultaneously, such as binding one target but not others or binding a target while maintaining stability. Such multistate design problems require navigating a high-dimensional space to find proteins with desired characteristics. A model that relates protein sequence to functional attributes can guide design to solutions that would be hard to discover via screening. In this work, we measured thousands of protein–peptide binding affinities with the high-throughput interaction assay amped SORTCERY and used the data to parameterize a model of the alpha-helical peptide-binding landscape for three members of the Bcl-2 family of proteins: Bcl-xL, Mcl-1, and Bfl-1. We applied optimization protocols to explore extremes in this landscape to discover peptides with desired interaction profiles. Computational design generated 36 peptides, all of which bound with high affinity and specificity to just one of Bcl-xL, Mcl-1, or Bfl-1, as intended. We designed additional peptides that bound selectively to two out of three of these proteins. The designed peptides were dissimilar to known Bcl-2–binding peptides, and high-resolution crystal structures confirmed that they engaged their targets as expected. Excellent results on this challenging problem demonstrate the power of a landscape modeling approach, and the designed peptides have potential uses as diagnostic tools or cancer therapeutics.},
doi = {10.1073/pnas.1812939115},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
issn = {0027-8424},
number = 44,
volume = 115,
place = {United States},
year = {2018},
month = {10}
}

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Cited by: 6 works
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