Iterative Molecular Dynamics–Rosetta Membrane Protein Structure Refinement Guided by Cryo-EM Densities

Abstract

Knowing atomistic details of proteins is essential not only for the understanding of protein function but also for the development of drugs. Experimental methods such as X-ray crystallography, NMR and cryo-EM are the preferred form of protein structure determination and have achieved great success over the last decades. Computational methods may be an alternative when experimental techniques fail. However, computational methods are severely limited when it comes to predicting larger macromolecule structures with little sequence similarity to known structures. The incorporation of experimental restraints in computational methods is becoming increasingly important to more reliably predict protein structure. One such experimental input used in structure prediction and refinement is cryo-EM densities. Recent advances in cryo-EM have arguably revolutionized the field of structural biology. Our previously developed cryo-EM guided Rosetta-MD protocol has shown great promise in the refinement of soluble protein structures. In this study, we extended cryo-EM density-guided iterative Rosetta-MD to membrane proteins. We also improved the methodology in general by picking models based on a combination of their score and fit-to-density during the Rosetta model selection. By doing so, we have been able to pick superior models than with the previous selection based on Rosetta score only and we have been able to further improve our previously refined models of soluble proteins. The method was tested with five membrane spanning protein structures. By applying density-guided Rosetta-MD iteratively we were able to refine the predicted structures of these membrane proteins to atomic resolutions. We also showed that the resolution of the density maps determines the improvement and quality of the refined models. By incorporating high resolution density maps (~ 4 Å) we were able to more significantly improve the quality of the models than when medium resolution maps (6.9 Å) were used. Beginning from an average starting structure RMSD to native of 4.66 Å, our protocol was able to refine the structures to bring the average refined structure RMSD to 1.66 Å when 4 Å density maps were used.

Accurate protein structure prediction with hydroxyl radical protein footprinting data

“…We have previously shown that the correlation between experimentally determined PFs and residue neighbor count can be exploited as a Rosetta scoring term to improve protein structure prediction 20 . In addition, computational protein structure prediction guided with sparse experimental data has been successfully implemented for a wide range of experimental data 21 – 27 . Our previous HRPF modeling work, however, relied on static protein structures.…”

Energetics and J-coupling constants for Ala, Gly, and Val peptides demonstrated using ABEEM polarizable force field in vacuo and an aqueous solution

“…Various experimental methods, such as cryo-electron microscopy (cryo-EM), [1][2][3][4][5][6] X-ray crystallography, [7][8][9][10][11][12][13][14] and nuclear magnetic resonance (NMR) [15][16][17][18][19][20][21] spectroscopy have been widely used together with other methods, such as small-angle X-ray scattering, to study the conformations of peptides and proteins. However, due to the sample preparation requirements and artefacts of crystallisation, the structures observed using X-ray techniques do not adequately reflect the relevant biological conformations of proteins.…”

Three-Dimensional Graph Matching to Identify Secondary Structure Correspondence of Medium-Resolution Cryo-EM Density Maps

“…However, for the low (~10–25 Å) or medium-resolution (~4–10 Å) density maps, the backbone of the protein and the atomic information cannot be directly achieved from the cryo-EM maps. This limitation has motivated the development of many computational methods that use the medium-resolution cryo-EM map to collect protein structural information [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. In the cryo-EM modeling pipeline, some major steps should be handled, such as extracting the secondary structure elements on a cryo-EM density map and matching them to a sequence/model, the placement of SSEs, building an atomic structure, and structure optimization [ 6 ].…”

“…In the presence of a high-resolution structure for an insufficient resolution cryo-EM map, the fitting methods, which are categorized into flexible and rigid-body fitting, could be utilized to derive the atomic structure from the cryo-EM map [ 9 , 12 , 14 , 17 ]. Early studies have concentrated on searching for the optimal position and orientation of a protein’s secondary structure components with the best overlaps with the SSEs extracted from a cryo-EM density map [ 23 , 24 , 25 , 26 ].…”