A Computationally Designed Water-Soluble Variant of a G-Protein-Coupled Receptor: The Human Mu Opioid Receptor

Pérez-Aguilar, José Manuel; Jin, Xi; Matsunaga, Felipe; Cui, Xu; Selling, Bernard; Saven, Jeffery G.; Liu, Renyu

doi:10.1371/journal.pone.0066009

Cited by 37 publications

(78 citation statements)

References 57 publications

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“…7 Although the homology model of the human MUR superimposed well with the crystal structure of murine MUR overall (root-mean-square deviation of the Cα atoms = 2.60 Å), 8 there are several structural differences, which would impact the selection of sites for designing to confer water solubility. Such differences are expected since there is only 27% sequence similarity between 288-residue transmembrane portions of the human β 2 adrenergic receptor and the human MUR 7 . The murine MUR crystal structure provides critical information to locate the mutated positions in wsMUR-TM since the sequence similarity between the human and the mouse MURs is 94% for the entire sequence and 99% for the segment solved in the crystal structure.…”

Section: Introductionmentioning

confidence: 92%

“…The protein was expressed in E. coli and retained structural and functional features consistent with those of the native MUR. 7 wsMUR-TM was engineered using a comparative (homology) model based on the crystal structures of human β 2 adrenergic receptor and bovine rhodopsin. 7 Although the homology model of the human MUR superimposed well with the crystal structure of murine MUR overall (root-mean-square deviation of the Cα atoms = 2.60 Å), 8 there are several structural differences, which would impact the selection of sites for designing to confer water solubility.…”

Section: Introductionmentioning

confidence: 99%

“…7 wsMUR-TM was engineered using a comparative (homology) model based on the crystal structures of human β 2 adrenergic receptor and bovine rhodopsin. 7 Although the homology model of the human MUR superimposed well with the crystal structure of murine MUR overall (root-mean-square deviation of the Cα atoms = 2.60 Å), 8 there are several structural differences, which would impact the selection of sites for designing to confer water solubility. Such differences are expected since there is only 27% sequence similarity between 288-residue transmembrane portions of the human β 2 adrenergic receptor and the human MUR 7 .…”

Section: Introductionmentioning

confidence: 99%

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Characterization of a Computationally Designed Water-soluble Human μ-Opioid Receptor Variant Using Available Structural Information

et al. 2014

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Background The recent X-ray crystal structure of the murine μ opioid receptor (MUR) allowed us to reengineer a previously designed water-soluble variant of the transmembrane portion of the human MUR (wsMUR-TM). Methods The new variant of water soluble MUR (wsMUR-TM_v2) was engineered based upon the murine MUR crystal structure. This novel variant was expressed in E. coli and purified. The properties of the receptor were characterized and compared with those of wsMUR-TM. Results Seven residues originally included for mutation in the design of the wsMUR-TM, were reverted to their native identities. wsMUR-TM_v2 contains 16% mutations of the total sequence. It was overexpressed and purified with high yield. Although dimers and higher oligomers were observed to form over time, the wsMUR-TM_v2 stayed predominantly monomeric at concentrations as high as 7.5 mg/ml in buffer within a 2-month period. Its secondary structure was predominantly helical and comparable with those of both the original wsMUR-TM variant and the native MUR. The binding affinity of wsMUR-TM_v2 for naltrexone (Kd ~ 70 nM) was in close agreement with that for wsMUR-TM. The helical content of wsMUR-TM_v2 decreased cooperatively with increasing temperature, and the introduction of sucrose was able to stabilize the protein. Conclusions A novel functional wsMUR-TM_v2 with only 16% mutations was successfully engineered, expressed in E. coli and purified based on information from the crystal structure of murine MUR. This not only provides a novel alternative tool for MUR studies in solution conditions, but also offers valuable information for protein engineering and structure function relationships.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characterization of a Computationally Designed Water-soluble Human μ-Opioid Receptor Variant Using Available Structural Information

et al. 2014

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“…E z β and related statistical functions (35,36) can recapitulate properties of natural outer membrane proteins (37,38) and predict the effects of mutations on protein stability and oligomerization (39). Similar potentials have driven computational approaches that have fully redesigned α-helical membrane protein surfaces to convert membrane proteins into water-soluble ones (40)(41)(42).…”

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confidence: 95%

Computational redesign of the lipid-facing surface of the outer membrane protein OmpA

Stapleton

Whitehead

Nanda

2015

Proc. Natl. Acad. Sci. U.S.A.

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Advances in computational design methods have made possible extensive engineering of soluble proteins, but designed β-barrel membrane proteins await improvements in our understanding of the sequence determinants of folding and stability. A subset of the amino acid residues of membrane proteins interact with the cell membrane, and the design rules that govern this lipid-facing surface are poorly understood. We applied a residue-level depth potential for β-barrel membrane proteins to the complete redesign of the lipid-facing surface of Escherichia coli OmpA. Initial designs failed to fold correctly, but reversion of a small number of mutations indicated by backcross experiments yielded designs with substitutions to up to 60% of the surface that did support folding and membrane insertion. T he β-barrel membrane proteins comprise one of the two structural classes of integral membrane proteins. They are found within the outer membranes of bacteria, mitochondria, and chloroplasts, where they perform a range of structural, transport, and catalytic functions (1). In addition to their biological interest they are increasingly relevant to biotechnology, serving as scaffolds for bacterial surface display (2, 3) and atomically precise pores for nanopore-based DNA sequencing. Although the suitability of natural β-barrel membrane proteins for biotechnology has been improved by protein engineering (3-10), the ability to design membrane proteins de novo would deliver tools customized to meet the demands of each application.De novo design provides a stringent test of our understanding of the determinants of protein folding and stability. Protein design software [e.g., Rosetta (11,12)] has made tremendous strides in addressing the design problem for small water-soluble proteins (13-15), and design of simplified model α-helical membrane proteins including single transmembrane helices and small bundles (16)(17)(18)(19)(20) has also been accomplished. In contrast, a designed β-barrel membrane protein has yet to be reported, perhaps as a consequence of the unique design challenges presented by the folding pathway and architecture of these proteins. Unlike the α-helical membrane proteins, nascent β-barrel membrane proteins must transit the periplasm to the outer membrane, where folding and membrane insertion are thought to occur in concert (21,22). An extensive network of chaperones maintains the solubility of the unfolded barrel and guides membrane insertion. The C-terminal β-strand is known to interact with the BAM chaperone complex (23-25), which assists the folding of all β-barrel membrane proteins. However, despite recent progress (26-30), we do not fully understand how interactions between chaperones and transiting membrane proteins are directed by sequence-encoded information.Further complicating design is the inside-out architecture of β-barrel membrane proteins. In place of a hydrophobic core is either a central water-filled pore or a solid core composed of polar side chains. The lipid bilayer becomes increasingly hydrophobic...

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“…The resulting structure closely resembles that of the original membrane protein and retains the potassium ion selectivity of the native protein [23]. Water soluble analogs of other membrane proteins have been developed by this method including the mu opioid receptor [24]. The need for computational design is clear here: a large number of mutations are needed to achieve solubility and optimization is difficult to achieve through iterative rounds of direct evolution.…”

Section: Automated Protein Design: Radical Protein Engineering By mentioning

confidence: 99%

Recent advances in automated protein design and its future challenges

Setiawan

Brender

Zhang

2018

Expert Opinion on Drug Discovery

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Introduction Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.

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A Computationally Designed Water-Soluble Variant of a G-Protein-Coupled Receptor: The Human Mu Opioid Receptor

Cited by 37 publications

References 57 publications

Characterization of a Computationally Designed Water-soluble Human μ-Opioid Receptor Variant Using Available Structural Information

Characterization of a Computationally Designed Water-soluble Human μ-Opioid Receptor Variant Using Available Structural Information

Computational redesign of the lipid-facing surface of the outer membrane protein OmpA

Recent advances in automated protein design and its future challenges

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