Show simple item record

dc.contributor.authorChen, T. Scott
dc.contributor.authorRichman, Daniel
dc.contributor.authorFoight, Glenna W.
dc.contributor.authorKeating, Amy E.
dc.date.accessioned2018-06-26T17:55:13Z
dc.date.available2018-06-26T17:55:13Z
dc.date.issued2017-02
dc.identifier.isbn978-1-4939-6796-4
dc.identifier.isbn978-1-4939-6798-8
dc.identifier.issn1064-3745
dc.identifier.issn1940-6029
dc.identifier.urihttp://hdl.handle.net/1721.1/116640
dc.description.abstractPeptide 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.en_US
dc.description.sponsorshipNational Institute of General Medical Sciences (U.S.) (Award R01 GM110048)en_US
dc.publisherHumana Pressen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/978-1-4939-6798-8_13en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcePMCen_US
dc.titleEnriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Designen_US
dc.typeArticleen_US
dc.identifier.citationFoight, 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 LLCen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineeringen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biologyen_US
dc.contributor.mitauthorFoight, Glenna W.
dc.contributor.mitauthorKeating, Amy E.
dc.relation.journalModeling Peptide-Protein Interactionsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2018-06-26T17:45:40Z
dspace.orderedauthorsFoight, Glenna Wink; Chen, T. Scott; Richman, Daniel; Keating, Amy E.en_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-3749-7092
dc.identifier.orcidhttps://orcid.org/0000-0003-4074-8980
dspace.mitauthor.errortrue
mit.licenseOPEN_ACCESS_POLICYen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record