Literature DB >> 34302669

Membrane Protein Engineering with Rosetta.

Rebecca F Alford1, Jeffrey J Gray2,3.   

Abstract

Protein engineering can yield new molecular tools for nanotechnology and therapeutic applications through modulating physiochemical and biological properties. Engineering membrane proteins is especially attractive because they perform key cellular processes including transport, nutrient uptake, removal of toxins, respiration, motility, and signaling. In this chapter, we describe two protocols for membrane protein engineering with the Rosetta software: (1) ΔΔG calculations for single point mutations and (2) sequence optimization in different membrane lipid compositions. These modular protocols are easily adaptable for more complex problems and serve as a foundation for efficient membrane protein engineering calculations.
© 2021. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Implicit membrane; Lipid composition; Monte Carlo; Protein design; Rosetta

Mesh:

Substances:

Year:  2021        PMID: 34302669      PMCID: PMC9070538          DOI: 10.1007/978-1-0716-1468-6_3

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  47 in total

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Authors:  T Lazaridis; M Karplus
Journal:  Proteins       Date:  1999-05-01

2.  Distribution of amino acids in a lipid bilayer from computer simulations.

Authors:  Justin L MacCallum; W F Drew Bennett; D Peter Tieleman
Journal:  Biophys J       Date:  2008-01-22       Impact factor: 4.033

3.  Computational design of orthogonal membrane receptor-effector switches for rewiring signaling pathways.

Authors:  M Young; T Dahoun; B Sokrat; C Arber; K M Chen; M Bouvier; P Barth
Journal:  Proc Natl Acad Sci U S A       Date:  2018-06-18       Impact factor: 11.205

4.  Naturally evolved G protein-coupled receptors adopt metastable conformations.

Authors:  Kuang-Yui Michael Chen; Fuguo Zhou; Bartlomiej G Fryszczyn; Patrick Barth
Journal:  Proc Natl Acad Sci U S A       Date:  2012-07-30       Impact factor: 11.205

Review 5.  The coming of age of de novo protein design.

Authors:  Po-Ssu Huang; Scott E Boyken; David Baker
Journal:  Nature       Date:  2016-09-15       Impact factor: 49.962

6.  Computational design of membrane proteins using RosettaMembrane.

Authors:  Amanda M Duran; Jens Meiler
Journal:  Protein Sci       Date:  2017-11-15       Impact factor: 6.725

7.  Influence of Protein Scaffold on Side-Chain Transfer Free Energies.

Authors:  Dagen C Marx; Karen G Fleming
Journal:  Biophys J       Date:  2017-08-08       Impact factor: 4.033

Review 8.  Overcoming barriers to membrane protein structure determination.

Authors:  Roslyn M Bill; Peter J F Henderson; So Iwata; Edmund R S Kunji; Hartmut Michel; Richard Neutze; Simon Newstead; Bert Poolman; Christopher G Tate; Horst Vogel
Journal:  Nat Biotechnol       Date:  2011-04       Impact factor: 54.908

9.  Computational design of self-assembling protein nanomaterials with atomic level accuracy.

Authors:  Neil P King; William Sheffler; Michael R Sawaya; Breanna S Vollmar; John P Sumida; Ingemar André; Tamir Gonen; Todd O Yeates; David Baker
Journal:  Science       Date:  2012-06-01       Impact factor: 47.728

10.  PoreDesigner for tuning solute selectivity in a robust and highly permeable outer membrane pore.

Authors:  Ratul Chowdhury; Tingwei Ren; Manish Shankla; Karl Decker; Matthew Grisewood; Jeevan Prabhakar; Carol Baker; John H Golbeck; Aleksei Aksimentiev; Manish Kumar; Costas D Maranas
Journal:  Nat Commun       Date:  2018-09-10       Impact factor: 14.919

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  1 in total

1.  Connexins and Pannexins-Similarities and Differences According to the FOD-M Model.

Authors:  Irena Roterman; Katarzyna Stapor; Piotr Fabian; Leszek Konieczny
Journal:  Biomedicines       Date:  2022-06-25
  1 in total

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