Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 115, Issue 44, Pages E10342-E10351Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1812939115
Keywords
protein-protein interactions; peptide design; energy landscape; Bcl-2 inhibitor; machine learning
Categories
Funding
- National Institute of General Medical Sciences (NIGMS) [R01 GM096466, R01 GM110048]
- NSF
- NIH Office of Research Infrastructure Programs High-End Instrumentation Grant [S10 RR029205]
- DOE Office of Science [DE-AC02-06CH11357]
- NIGMS from the National Institutes of Health [P30 GM124165]
- NATIONAL CENTER FOR RESEARCH RESOURCES [S10RR029205] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [T32GM087237, R01GM110048, P30GM124165, R01GM096466] Funding Source: NIH RePORTER
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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, and the designed peptides have potential uses as diagnostic tools or cancer therapeutics.
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