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A Novel Domain Assembly Routine for Creating Full-Length Models of Membrane Proteins from Known Domain Structures
Author(s) -
Julia Koehler Leman,
Richard Bonneau
Publication year - 2017
Publication title -
biochemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.43
H-Index - 253
eISSN - 1520-4995
pISSN - 0006-2960
DOI - 10.1021/acs.biochem.7b00995
Subject(s) - domain (mathematical analysis) , linker , transmembrane domain , transmembrane protein , membrane , membrane protein , function (biology) , biophysics , protein domain , egf like domain , chemistry , crystallography , computer science , biology , biochemistry , microbiology and biotechnology , mathematics , mathematical analysis , receptor , gene , operating system
Membrane proteins composed of soluble and membrane domains are often studied one domain at a time. However, to understand the biological function of entire protein systems and their interactions with each other and drugs, knowledge of full-length structures or models is required. Although few computational methods exist that could potentially be used to model full-length constructs of membrane proteins, none of these methods are perfectly suited for the problem at hand. Existing methods require an interface or knowledge of the relative orientations of the domains or are not designed for domain assembly, and none of them are developed for membrane proteins. Here we describe the first domain assembly protocol specifically designed for membrane proteins that assembles intra- and extracellular soluble domains and the transmembrane domain into models of the full-length membrane protein. Our protocol does not require an interface between the domains and samples possible domain orientations based on backbone dihedrals in the flexible linker regions, created via fragment insertion, while keeping the transmembrane domain fixed in the membrane. For five examples tested, our method mp_domain_assembly, implemented in RosettaMP, samples domain orientations close to the known structure and is best used in conjunction with experimental data to reduce the conformational search space.

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