Literature DB >> 33320432

De Novo Protein Design Using the Blueprint Builder in Rosetta.

Linna An1, Gyu Rie Lee1.   

Abstract

While native proteins cover diverse structural spaces and achieve various biological events, not many of them can directly serve human needs. One reason is that the native proteins usually contain idiosyncrasies evolved for their native functions but disfavoring engineering requirements. To overcome this issue, one strategy is to create de novo proteins which are designed to possess improved stability, high environmental tolerance, and enhanced engineering potential. Compared to other protein engineering strategies, in silico design of de novo proteins has significantly expanded the protein structural and sequence spaces, reduced wet lab workload, and incorporated engineered features in a guided and efficient manner. In the Baker laboratory we have been applying a design pipeline that uses the blueprint builder to design different folds of de novo proteins, and have successfully obtained libraries of de novo proteins with improved stability and engineering potential. In this article, we will use the design of de novo β-barrel proteins as an example to describe the principles and basic procedures of the blueprint builder-based design pipeline.
© 2020 Wiley Periodicals LLC. Basic Protocol 1: The construction of blueprints Alternate Protocol: Build blueprints based on existing protein .pdb files Basic Protocol 2: De novo protein design pipeline using the blueprint builder. © 2020 Wiley Periodicals LLC.

Entities:  

Keywords:  BluePrintBDR; Rosetta; blueprint; de novo protein; protein design

Year:  2020        PMID: 33320432     DOI: 10.1002/cpps.116

Source DB:  PubMed          Journal:  Curr Protoc Protein Sci        ISSN: 1934-3655


  2 in total

1.  De novo design of protein homodimers containing tunable symmetric protein pockets.

Authors:  Derrick R Hicks; Madison A Kennedy; Kirsten A Thompson; Michelle DeWitt; Brian Coventry; Alex Kang; Asim K Bera; T J Brunette; Banumathi Sankaran; Barry Stoddard; David Baker
Journal:  Proc Natl Acad Sci U S A       Date:  2022-07-21       Impact factor: 12.779

Review 2.  Computer-aided discovery, design, and investigation of COVID-19 therapeutics.

Authors:  Chun-Chun Chang; Hao-Jen Hsu; Tien-Yuan Wu; Je-Wen Liou
Journal:  Tzu Chi Med J       Date:  2022-03-28
  2 in total

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