Molecular dynamics-based renement and validation with Resolution Exchange MDFF for sub-5 Å cryo-electron microscopy maps
- University of Illinois at Urbana Champaign, United States
- University of Illinois at Urbana-Champaign, United States
- University of California, San Francisco, United States
Abstract
Two structure determination methods, based on the molecular dynamics flexible fitting (MDFF) paradigm, are presented that resolve sub-5-Å cryo-electron microscopy (EM) maps with either single structures or ensembles of such structures. The methods, denoted cascade MDFF and resolution exchange MDFF, sequentially re-refine a search model against a series of maps of progressively higher resolutions, which ends with the original experimental resolution. Application of sequential re-refinement enables MDFF to achieve a convergence radius of ~25Å demonstrated with the accurate modeling of β-galactosidase and TRPV1 proteins at 3.2Å and 3.4Å resolution. The MDFF refinements uniquely offer map-model validation and B-factor determination criteria based on the inherent dynamics of the respective macromolecules studied, captured employing local root mean square fluctuations. The MDFF tools are made available to researchers through an easy-to-use and cost-effective cloud computing resource on Amazon Web Services.
Data availability
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NIZN[FE4S4] AND NINI[FE4S4] CLUSTERS IN CLOSED AND OPEN ALPHA SUBUNITS OF ACETYL-COA SYNTHASE/CARBON MONOXIDE DEHYDROGENASEPublicly available at the Protien Data Bank (accession no. 1OAO).
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Structure of TRPV1 ion channel determined by single particle electron cryo-microscopyPublicly available at the Protien Data Bank (accession no. 3J5P).
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Structure of the capsaicin receptor, TRPV1, determined by single particle electron cryo-microscopyPublicly available at the EMDataBank (accesion no. EMD-5778).
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2.2 A resolution cryo-EM structure of beta-galactosidase in complex with a cell-permeant inhibitorPublicly available at the Protien Data Bank (accession no. 5A1A).
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2.2 A resolution cryo-EM structure of beta-galactosidase in complex with a cell-permeant inhibitorPublicly available at the EMDataBank (accesion no. EMD-2984).
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Cryo-EM structure of the human gamma-secretase complex at 3.4 angstrom resolution.Publicly available at the Protien Data Bank (accession no. 5A63).
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Cryo-EM structure of the human gamma-secretase complex at 3.4 angstrom resolutionPublicly available at the EMDataBank (accesion no. EMD-3061).
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Structure of a extracellular domainPublicly available at the Protien Data Bank (accession no. 4UPC).
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Three-dimensional structure of human gamma-secretase at 4.5 angstrom resolutionPublicly available at the EMDataBank (accesion no. EMD-2677).
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Thermoplasma acidophilum 20S proteasomePublicly available at the Protien Data Bank (accession no. 3J9I).
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3D reconstruction of archaeal 20S proteasomePublicly available at the EMDataBank (accesion no. EMD-5623).
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Structure of beta-galactosidase at 3.2-A resolution obtained by cryo-electron microscopyPublicly available at the Protien Data Bank (accession no. 3J7H).
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Structure of beta-galactosidase at 3.2-A resolution obtained by cryo-electron microscopyPublicly available at the EMDataBank (accesion no. EMD-5995).
Article and author information
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© 2016, singharoy et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Molecular dynamics-based renement and validation with Resolution Exchange MDFF for sub-5 Å cryo-electron microscopy maps
eLife 5:e16105.
https://doi.org/10.7554/eLife.16105
Further reading
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- Structural Biology and Molecular Biophysics
Under physiological conditions, proteins continuously undergo structural fluctuations on different timescales. Some conformations are only sparsely populated, but still play a key role in protein function. Thus, meaningful structure–function frameworks must include structural ensembles rather than only the most populated protein conformations. To detail protein plasticity, modern structural biology combines complementary experimental and computational approaches. In this review, we survey available computational approaches that integrate sparse experimental data from electron paramagnetic resonance spectroscopy with molecular modeling techniques to derive all-atom structural models of rare protein conformations. We also propose strategies to increase the reliability and improve efficiency using deep learning approaches, thus advancing the field of integrative structural biology.
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- Structural Biology and Molecular Biophysics
Experimental detection of residues critical for protein–protein interactions (PPI) is a time-consuming, costly, and labor-intensive process. Hence, high-throughput PPI-hot spot prediction methods have been developed, but they have been validated using relatively small datasets, which may compromise their predictive reliability. Here, we introduce PPI-hotspotID, a novel method for identifying PPI-hot spots using the free protein structure, and validated it on the largest collection of experimentally confirmed PPI-hot spots to date. We explored the possibility of detecting PPI-hot spots using (i) FTMap in the PPI mode, which identifies hot spots on protein–protein interfaces from the free protein structure, and (ii) the interface residues predicted by AlphaFold-Multimer. PPI-hotspotID yielded better performance than FTMap and SPOTONE, a webserver for predicting PPI-hot spots given the protein sequence. When combined with the AlphaFold-Multimer-predicted interface residues, PPI-hotspotID yielded better performance than either method alone. Furthermore, we experimentally verified several PPI-hotspotID-predicted PPI-hot spots of eukaryotic elongation factor 2. Notably, PPI-hotspotID can reveal PPI-hot spots not obvious from complex structures, including those in indirect contact with binding partners. PPI-hotspotID serves as a valuable tool for understanding PPI mechanisms and aiding drug design. It is available as a web server (https://ppihotspotid.limlab.dnsalias.org/) and open-source code (https://github.com/wrigjz/ppihotspotid/).
