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

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

PROVIDER: PRJNA484708 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR7647412_1.fastq.gz	Fastqsanger.gz
	SRR7647412_2.fastq.gz	Fastqsanger.gz
	SRR7647413_1.fastq.gz	Fastqsanger.gz
	SRR7647413_2.fastq.gz	Fastqsanger.gz
	SRR7647414_1.fastq.gz	Fastqsanger.gz

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

Project description:We applied the high-throughput interaction assay SORTCERY to measure thousands of protein-peptide binding affinities and used the data to parameterize models of the peptide-binding landscape for three members of the Bcl-2 family of proteins. We applied the models to design peptides that bound with high affinity and specificity to just one of Bcl-xL, Mcl-1, or Bfl-1. We designed additional peptides that bound selectively to two out of three of these proteins. The raw data provided are the multiplexed fastq files that serve as inputs to our analysis pipeline. Additional detail is available in our corresponding publication and at the following github repository. https://github.com/KeatingLab/sortcery_design

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

Project description: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.

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