Resolving individual atoms of protein complex by cryo-electron microscopy.
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ABSTRACT:
SUBMITTER: Zhang K
PROVIDER: S-EPMC7605492 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Publications
Resolving individual atoms of protein complex by cryo-electron microscopy.
Zhang Kaiming K Pintilie Grigore D GD Li Shanshan S Schmid Michael F MF Chiu Wah W
Cell research 20201102 12
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Project description:Single-particle cryo-electron microscopy is widely used to study the structure of macromolecular assemblies. Tens of thousands of noisy two-dimensional images of the macromolecular assembly viewed from different directions are used to infer its three-dimensional structure. The first step is to estimate a low-resolution initial model and initial image orientations. This is a challenging global optimization problem with many unknowns, including an unknown orientation for each two-dimensional image. Obtaining a good initial model is crucial for the success of the subsequent refinement step. We introduce a probabilistic algorithm for estimating an initial model. The algorithm is fast, has very few algorithmic parameters, and yields information about the precision of estimated model parameters in addition to the parameters themselves. Our algorithm uses a pseudo-atomic model to represent the low-resolution three-dimensional structure, with isotropic Gaussian components as moveable pseudo-atoms. This leads to a significant reduction in the number of parameters needed to represent the three-dimensional structure, and a simplified way of computing two-dimensional projections. It also contributes to the speed of the algorithm. We combine the estimation of the unknown three-dimensional structure and image orientations in a Bayesian framework. This ensures that there are very few parameters to set, and specifies how to combine different types of prior information about the structure with the given data in a systematic way. To estimate the model parameters we use Markov chain Monte Carlo sampling. The advantage is that instead of just obtaining point estimates of model parameters, we obtain an ensemble of models revealing the precision of the estimated parameters. We demonstrate the algorithm on both simulated and real data.
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Project description:A variety of techniques involving the use of mass spectrometry (MS) have been developed to obtain structural information on proteins and protein complexes. One example of these techniques, surface-induced dissociation (SID), has been used to study the oligomeric state and connectivity of protein complexes. Recently, we demonstrated that appearance energies (AE) could be extracted from SID experiments and that they correlate with structural features of specific protein-protein interfaces. While SID AE provides some structural information, the AE data alone are not sufficient to determine the structures of the complexes. For this reason, we sought to supplement the data with computational modeling, through protein-protein docking. In a previous study, we demonstrated that the scoring of structures generated from protein-protein docking could be improved with the inclusion of SID data; however, this work relied on knowledge of the correct tertiary structure and only built full complexes for a few cases. Here, we performed docking using input structures that require less prior knowledge, using homology models, unbound crystal structures, and bound+perturbed crystal structures. Using flexible ensemble docking (to build primarily subcomplexes from an ensemble of backbone structures), the RMSD100 of all (15/15) predicted structures using the combined Rosetta, cryo-electron microscopy (cryo-EM), and SID score was less than 4 Å, compared to only 7/15 without SID and cryo-EM. Symmetric docking (which used symmetry to build full complexes) resulted in predicted structures with RMSD100 less than 4 Å for 14/15 cases with experimental data, compared to only 5/15 without SID and cryo-EM. Finally, we also developed a confidence metric for which all (26/26) proteins flagged as high confidence were accurately predicted.
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Project description:ObjectiveThe αIIbβ3 antagonist antiplatelet drug abciximab is the chimeric antigen-binding fragment comprising the variable regions of murine monoclonal antibody 7E3 and the constant domains of human IgG1 and light chain κ. Previous mutagenesis studies suggested that abciximab binds to the β3 C177-C184 specificity-determining loop (SDL) and Trp129 on the adjacent β1-α1 helix. These studies could not, however, assess whether 7E3 or abciximab prevents fibrinogen binding by steric interference, disruption of either the αIIbβ3-binding pocket for fibrinogen or the β3 SDL (which is not part of the binding pocket but affects fibrinogen binding), or some combination of these effects. To address this gap, we used cryo-electron microscopy to determine the structure of the αIIbβ3-abciximab complex at 2.8 Å resolution. Approach and Results: The interacting surface of abciximab is comprised of residues from all 3 complementarity-determining regions of both the light and heavy chains, with high representation of aromatic residues. Binding is primarily to the β3 SDL and neighboring residues, the β1-α1 helix, and β3 residues Ser211, Val212 and Met335. Unexpectedly, the structure also indicated several interactions with αIIb. As judged by the cryo-electron microscopy model, molecular-dynamics simulations, and mutagenesis, the binding of abciximab does not appear to rely on the interaction with the αIIb residues and does not result in disruption of the fibrinogen-binding pocket; it does, however, compress and reduce the flexibility of the SDL.ConclusionsWe deduce that abciximab prevents ligand binding by steric interference, with a potential contribution via displacement of the SDL and limitation of the flexibility of the SDL residues.
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