Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design
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Author(s)
Chen, T. Scott
Richman, Daniel
Foight, Glenna W.
Keating, Amy E.
Date Issued
February 2017
Journal
Modeling Peptide-Protein Interactions
Publisher
Humana Press
Citation
Foight, Glenna Wink et al. “Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design.” Modeling Peptide-Protein Interactions (2017): 213–232 © 2017 Springer Science+Business Media LLC
Version
Author's final manuscript
Abstract
Peptide reagents with high affinity or specificity for their target protein interaction partner are of utility for many important applications. Optimization of peptide binding by screening large libraries is a proven and powerful approach. Libraries designed to be enriched in peptide sequences that are predicted to have desired affinity or specificity characteristics are more likely to yield success than random mutagenesis. We present a library optimization method in which the choice of amino acids to encode at each peptide position can be guided by available experimental data or structure-based predictions. We discuss how to use analysis of predicted library performance to inform rounds of library design. Finally, we include protocols for more complex library design procedures that consider the chemical diversity of the amino acids at each peptide position and optimize a library score based on a user-specified input model.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Biology
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-1-4939-6798-8_13
