Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design
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...
| 主要な著者: | , , , |
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| その他の著者: | |
| フォーマット: | 論文 |
| 出版事項: |
Humana Press
2018
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| オンライン･アクセス: | http://hdl.handle.net/1721.1/116640 https://orcid.org/0000-0003-3749-7092 https://orcid.org/0000-0003-4074-8980 |
| 要約: | 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. |
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