Alternate states of proteins revealed by detailed energy landscape mapping

By Michael D. Tyka, Daniel A. Keedy, Ingemar André, Frank DiMaio, Yifan Song, David C Richardson, Jane S. Richardsonb, David Baker. 2010

405 citations recorded by the source corpus.

What conformations do protein molecules populate in solution? Crystallography provides a high-resolution description of protein structure in the crystal environment, while NMR describes structure in solution but using less data. NMR structures display more variability, but is this because crystal contacts are absent or because of fewer data constraints? Here we report unexpected insight into this issue obtained through analysis of detailed protein energy landscapes generated by large-scale, native-enhanced sampling of conformational space with Rosetta@HOME for 111 protein domains. In the absence of tightly associating binding partners or ligands, the lowest-energy Rosetta models were nearly all <2.5Å CαRMSD from the experimental structure; this result demonstrates that structure prediction accuracy for globular proteins is limited mainly by the ability to sample close to the native structure. While the lowest-energy models are similar to deposited structures, they are not identical; the largest deviations are most often in regions involved in ligand, quaternary, or crystal contacts. For ligand binding proteins, the low energy models may resemble the apo structures, and for oligomeric proteins, the monomeric assembly intermediates. The deviations between the low energy models and crystal structures largely disappear when landscapes are computed in the context of the crystal lattice or multimer. The computed low-energy ensembles, with tight crystal-structure-like packing in the core, but more NMR-structure-like variability in loops, may in some cases resemble the native state ensembles of proteins better than individual crystal or NMR structures, and can suggest experimentally testable hypotheses relating alternative states and structural heterogeneity to function.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy, Computational Biology & Bioinformatics

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Citations in the corpus

Protein sequence design with a learned potential (2022, methodology): “Backbone relaxes for designs were done with the RosettaRelax application 61 – 64 , with the ex1 ex2 options for extra χ 1 and χ 2 rotamer sampling.”
Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, background): “To reduce the required amount of sampling, Tyka et al. (2010) curated four sets of low-resolution decoys for 114 proteins by taking the low-resolution structures that generated low-energy structures after being put through high-resolution refinement.”
The Rosetta all-atom energy function for macromolecular modeling and design (2017, background): “…Prediction of loop conformations [98] High-resolution refinement Discrimination of native-like decoys upon refinement of ab initio protein models [99] Docking Prediction of protein-protein interfaces [100,101] Homology Modeling Structure prediction incorporating homologous information from…”
High resolution comparative modeling with RosettaCM (2013, methodology): “( Tyka et al., 2011) Annealing is carried out by ramping up and down the strength of the repulsive interactions and at each iteration repacking the sidechains and subjecting the whole structure to quasi-Newton optimization of the sidechain and backbone coordinates first in internal coordinates and…”
One contact for every twelve residues allows robust and accurate topology-level protein structure modeling (2013, methodology): “Comformational sampling proceeds by Monte Carlo symmetric fragment replacement, supplemented with two types of symmetric rigid body subunit perturbations for every 10 fragment moves, (1) random rotation and translation, and (2) translation along the symmetry axis into atomic contact.”
High-accuracy refinement using Rosetta in CASP13 (2019, methodology): “14,15 In our earlier work, the above framework was applied to a subset of CASP challenges with starting model GDT-HA less than 50.”
Computational Design of Self-Assembling Cyclic Protein Homo-oligomers (2016, methodology): “100 trajectories of a Rosetta protocol that previously showed to sample the local energy landscape 11 was then performed on each extended model.”
Automatically fixing errors in glycoprotein structures with Rosetta (2018, positive): “This was followed by idealizing the anomeric hydrogen, in order to resolve any incorrect anomers, and then relaxing the system using the Rosetta ‘‘fastrelax’’ protocol (Tyka et al., 2011).”
Reconfigurable asymmetric protein assemblies through implicit negative design (2021, positive): “After removal of the aligned clashing strand, the docked strand was trimmed at the N and/or C terminus to remove potential clashes and subsequently minimized using Rosetta FastRelax ( 35 ) to optimize backbone-to-backbone hydrogen bonds.”
Experimental accuracy in protein structure refinement via molecular dynamics simulations (2018, background): “The presence of a significant number of native-like states with energies similar to the true native state has been described in earlier studies (48).”