Cryo-EM structure of constitutively active human Frizzled 7 in complex with heterotrimeric Gs

Xu, Lu; Chen, Bin; Schihada, Hannes; Wright, Shane C.; Turku, Ainoleena; Wu, Yiran; Han, Gye Won; Kowalski-Jahn, Maria; Kozielewicz, Paweł; Bowin, Carl-Fredrik; Zhang, Xianjun; Li, Chao; Bouvier, Michel; Schulte, Gunnar; Xu, Fei

doi:10.1038/s41422-021-00525-6

Cited by 33 publications

(29 citation statements)

References 15 publications

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“…We selected five transporters that not only met these criteria but also reflected a range of transport mechanisms characterized in the literature ( Drew and Boudker, 2016 ), including rocking-bundle (LAT1, Yan et al, 2019 ; Yan et al, 2021 ; ZnT8, Xue et al, 2020 ), rocker-switch (MCT1, Wang et al, 2021 ; STP10, Bavnhøj et al, 2021 ), and elevator (ASCT2, Garibsingh et al, 2021 ; Garaeva et al, 2019 ). We also included three GPCRs, which were distributed across classes A (CGRPR, Liang et al, 2020 ; Josephs et al, 2021 ), B1 (PTH1R, Ehrenmann et al, 2018 ; Zhao et al, 2019 ), and F (FZD7, Xu et al, 2021 ; to serve as points of comparison, we used the active conformation of FZD7 and the inactive conformation of the nearly identical FZD4, Yang et al, 2018 ).…”

Section: Resultsmentioning

confidence: 99%

Sampling alternative conformational states of transporters and receptors with AlphaFold2

Alamo

Sala

Mchaourab

et al. 2022

eLife

298

317

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Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward-facing (IF) and outward-facing (OF) states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy. Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity. Here, we present an approach to drive AF2 to sample alternative conformations of topologically diverse transporters and G-protein coupled receptors (GPCRs) that are absent from the AF2 training set. Whereas models of most proteins generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another, reducing the depth of the input multiple sequence alignments (MSAs) by stochastic subsampling led to the generation of accurate models in multiple conformations. In our benchmark, these conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average template modeling (TM)-score of 0.94). These results suggest a straightforward approach to identifying native-like alternative states, while also highlighting the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states.

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Section: Resultsmentioning

confidence: 99%

Sampling alternative conformational states of transporters and receptors with AlphaFold2

Alamo

Sala

Mchaourab

et al. 2022

eLife

298

317

View full text Add to dashboard Cite

show abstract

“…The flexibility of the CRD relative to the core of FZDs and its relevance for signal initiation has been a matter of debate, and it remains unclear how the information flow from WNT binding to the CRD is transduced to the core of the receptor provided that the linker domain is freely flexible. The ligand-free CRD of FZD 7 contributes to constitutive receptor activation toward heterotrimeric G s proteins (55). However, the underlying structural aspects of CRD-mediated constitutive activity remain obscure.…”

Section: Discussionmentioning

confidence: 99%

Frizzled BRET sensors based on bioorthogonal labeling of unnatural amino acids reveal WNT-induced dynamics of the cysteine-rich domain

et al. 2021

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“…We selected five transporters that not only met these criteria but also reflected a range of transport mechanisms characterized in the literature 5 , including rocking-bundle (LAT1 19,20 , ZnT8 21 ), rocker-switch (MCT1 22 , STP10 23 ), and elevator (ASCT2 24,25 ). We also included three GPCRs, which were distributed across classes A (CGRPR 26,27 ), B1 (PTH1R 28,29 ), and F (FZD7 30 ; to serve as points of comparison, we used the active conformation of FZD7 and the inactive conformation of the nearly identical FZD4 31 ).…”

Section: Resultsmentioning

confidence: 99%

Sampling the conformational landscapes of transporters and receptors with AlphaFold2

Alamo

Sala²,

Mchaourab

et al. 2021

Preprint

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Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward-facing (IF) and outward-facing (OF) states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy. Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity. Here, we present an approach to drive AF2 to sample alternative conformations of topologically diverse transporters and G-protein coupled receptors (GPCRs) that are absent from the AF2 training set. Whereas models generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another, reducing the depth of the input multiple sequence alignments (MSAs) led to the generation of accurate models in multiple conformations. In our benchmark, these conformations were observed to span the range between two experimental structures of interest, suggesting that our protocol allows sampling of the conformational landscape at the energy minimum. Nevertheless, our results also highlight the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states.

show abstract

Cryo-EM structure of constitutively active human Frizzled 7 in complex with heterotrimeric Gs

Cited by 33 publications

References 15 publications

Sampling alternative conformational states of transporters and receptors with AlphaFold2

Sampling alternative conformational states of transporters and receptors with AlphaFold2

Frizzled BRET sensors based on bioorthogonal labeling of unnatural amino acids reveal WNT-induced dynamics of the cysteine-rich domain

Sampling the conformational landscapes of transporters and receptors with AlphaFold2

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