國立虎尾科技大學 |

Computational Protein Design for Peptide-Directed Bioconjugation.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Computational Protein Design for Peptide-Directed Bioconjugation./
作者:	Plaks, Joseph G.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	185 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-11, Section: B.
Contained By:	Dissertations Abstracts International80-11B.
標題:	Bioengineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13426343
ISBN:	9781392164457

Computational Protein Design for Peptide-Directed Bioconjugation.
Plaks, Joseph G.

Computational Protein Design for Peptide-Directed Bioconjugation. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 185 p.

Source: Dissertations Abstracts International, Volume: 80-11, Section: B.

Thesis (Ph.D.)--University of Colorado at Boulder, 2019.

This item must not be added to any third party search indexes.

Proteins enable living organisms to perform many of their critical functions, having been applied over evolutionary time to solve problems of overwhelming diversity and complexity. Protein engineering seeks to deploy these versatile molecules in addressing problems of human concern and would benefit from innovations that improve protein utilization in unnatural environments as well as from increased predictive capability in protein design.Bioconjugation facilitates the use of proteins in unnatural environments by permitting the attachment of molecules, such as polymers, that can modulate protein stability, solubility, and activity and by mediating protein immobilization. We initially explored this propensity of bioconjugation by designing an enzymatic polyurethane-based material in which proteins were covalently immobilized via non-specific, isocyanate reaction chemistries. The resulting material resisted bacterial biofilm formation through the activity of the embedded enzymes, which hydrolyzed signaling molecules involved in quorum sensing.In order to gain better regional and temporal reaction control, we transitioned to working with an enzyme-mediated conjugation system in which lipoic acid ligase functionalizes a specific 13-residue peptide sequence (the LAP sequence) with an azide-bearing lipoic acid derivative. Using GFP as a model protein, we demonstrated that the LAP sequence could be inserted at internal positions within a protein’s structure and successfully ligated and subsequently modified via azide-alkyne click chemistry within that context. Given the ability of the LAP sequence to site-specifically direct conjugation at internal sites within protein structures, we developed a predictive computational approach to facilitate the design of internal LAP insertion sites within diverse protein targets.The kinematic loop modeling application within the Rosetta framework was adapted for rapid scanning and characterization of insertion sites, using Rosetta’s coarse-grained centroid score function for site differentiation. Soluble protein expression of LAP-containing proteins was found to correlate with Rosetta scores, and unintuitive sites were identified relative to B-factor and secondary structure considerations. Our results highlighted the role played by residues in the near-loop environment in determining whether a particular site within a protein accommodates an inserted loop, such as the LAP sequence, and suggest that our computational approach, which is highly user accessible, can usefully predict such sites.

ISBN: 9781392164457Subjects--Topical Terms:

598252
Bioengineering.
Subjects--Index Terms:

Bioconjugation

Computational Protein Design for Peptide-Directed Bioconjugation.
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520 $a Proteins enable living organisms to perform many of their critical functions, having been applied over evolutionary time to solve problems of overwhelming diversity and complexity. Protein engineering seeks to deploy these versatile molecules in addressing problems of human concern and would benefit from innovations that improve protein utilization in unnatural environments as well as from increased predictive capability in protein design.Bioconjugation facilitates the use of proteins in unnatural environments by permitting the attachment of molecules, such as polymers, that can modulate protein stability, solubility, and activity and by mediating protein immobilization. We initially explored this propensity of bioconjugation by designing an enzymatic polyurethane-based material in which proteins were covalently immobilized via non-specific, isocyanate reaction chemistries. The resulting material resisted bacterial biofilm formation through the activity of the embedded enzymes, which hydrolyzed signaling molecules involved in quorum sensing.In order to gain better regional and temporal reaction control, we transitioned to working with an enzyme-mediated conjugation system in which lipoic acid ligase functionalizes a specific 13-residue peptide sequence (the LAP sequence) with an azide-bearing lipoic acid derivative. Using GFP as a model protein, we demonstrated that the LAP sequence could be inserted at internal positions within a protein’s structure and successfully ligated and subsequently modified via azide-alkyne click chemistry within that context. Given the ability of the LAP sequence to site-specifically direct conjugation at internal sites within protein structures, we developed a predictive computational approach to facilitate the design of internal LAP insertion sites within diverse protein targets.The kinematic loop modeling application within the Rosetta framework was adapted for rapid scanning and characterization of insertion sites, using Rosetta’s coarse-grained centroid score function for site differentiation. Soluble protein expression of LAP-containing proteins was found to correlate with Rosetta scores, and unintuitive sites were identified relative to B-factor and secondary structure considerations. Our results highlighted the role played by residues in the near-loop environment in determining whether a particular site within a protein accommodates an inserted loop, such as the LAP sequence, and suggest that our computational approach, which is highly user accessible, can usefully predict such sites.
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