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Flexible Backbone Methods for Predicting and Designing Peptide Specificity

Ollikainen, Noah (2017) Flexible Backbone Methods for Predicting and Designing Peptide Specificity. In: Modeling Peptide-Protein Interactions. Methods in Molecular Biology. No.1561. Springer , New York, NY, pp. 173-187. ISBN 978-1-4939-6796-4.

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Protein–protein interactions play critical roles in essentially every cellular process. These interactions are often mediated by protein interaction domains that enable proteins to recognize their interaction partners, often by binding to short peptide motifs. For example, PDZ domains, which are among the most common protein interaction domains in the human proteome, recognize specific linear peptide sequences that are often at the C-terminus of other proteins. Determining the set of peptide sequences that a protein interaction domain binds, or it’s “peptide specificity,” is crucial for understanding its cellular function, and predicting how mutations impact peptide specificity is important for elucidating the mechanisms underlying human diseases. Moreover, engineering novel cellular functions for synthetic biology applications, such as the biosynthesis of biofuels or drugs, requires the design of protein interaction specificity to avoid crosstalk with native metabolic and signaling pathways. The ability to accurately predict and design protein–peptide interaction specificity is therefore critical for understanding and engineering biological function. One approach that has recently been employed toward accomplishing this goal is computational protein design. This chapter provides an overview of recent methodological advances in computational protein design and highlights examples of how these advances can enable increased accuracy in predicting and designing peptide specificity.

Item Type:Book Section
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Additional Information:© 2017 Springer Science+Business Media LLC.
Subject Keywords:Protein interactions – Protein design – Sampling algorithms – Backbone flexibility – Conformational ensembles – Specificity prediction – Designing specificity
Series Name:Methods in Molecular Biology
Issue or Number:1561
Record Number:CaltechAUTHORS:20170306-091822101
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:74764
Deposited By: Tony Diaz
Deposited On:06 Mar 2017 17:27
Last Modified:11 Nov 2021 05:29

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