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Fine Sampling of Sequence Space for Membrane Protein Structural Biology.

ABSTRACT: We describe an enhancement of traditional genomics-based approaches to improve the success of structure determination of membrane proteins. Following a broad screen of sequence space to identify initial expression-positive targets, we employ a second step to select orthologs with closely related sequences to these hits. We demonstrate that a greater percentage of these latter targets express well and are stable in detergent, increasing the likelihood of identifying candidates that will ultimately yield structural information.

SUBMITTER: Loukeris M

PROVIDER: S-EPMC8286341 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Fine Sampling of Sequence Space for Membrane Protein Structural Biology.

Loukeris Michael M Sanghai Zahra Assur ZA Vendome Jeremie J Hendrickson Wayne A WA Kloss Brian B Mancia Filippo F

Journal of molecular biology 20210520 15

We describe an enhancement of traditional genomics-based approaches to improve the success of structure determination of membrane proteins. Following a broad screen of sequence space to identify initial expression-positive targets, we employ a second step to select orthologs with closely related sequences to these hits. We demonstrate that a greater percentage of these latter targets express well and are stable in detergent, increasing the likelihood of identifying candidates that will ultimatel ...[more]

PMID: 34022208

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Project description:The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

Dataset Information

Fine Sampling of Sequence Space for Membrane Protein Structural Biology.

Publications

Fine Sampling of Sequence Space for Membrane Protein Structural Biology.

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