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Chemical shifts-based similarity restraints improve accuracy of RNA structures determined via NMR.

ABSTRACT: Determination of structure of RNA via NMR is complicated in large part by the lack of a precise parameterization linking the observed chemical shifts to the underlying geometric parameters. In contrast to proteins, where numerous high-resolution crystal structures serve as coordinate templates for this mapping, such models are rarely available for smaller oligonucleotides accessible via NMR, or they exhibit crystal packing and counter-ion binding artifacts that prevent their use for the chemical shifts analysis. On the other hand, NMR-determined structures of RNA often are not solved at the density of restraints required to precisely define the variable degrees of freedom. In this study we sidestep the problems of direct parameterization of the RNA chemical shifts/structure relationship and examine the effects of imposing local fragmental coordinate similarity restraints based on similarities of the experimental secondary ribose ¹³C/¹H chemical shifts instead. The effect of such chemical shift similarity (CSS) restraints on the structural accuracy is assessed via residual dipolar coupling (RDC)-based cross-validation. Improvements in the coordinate accuracy are observed for all of the six RNA constructs considered here as test cases, which argues for routine inclusion of these terms during NMR-based oligonucleotide structure determination. Such accuracy improvements are expected to facilitate derivation of the chemical shift/structure relationships for RNA.

SUBMITTER: Lawrence C

PROVIDER: S-EPMC7668244 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Cryo-EM has become one of the prime methods for protein structure elucidation, frequently yielding density maps with near-atomic or medium resolution. If protein structures cannot be deduced unambiguously from the density maps, computational structure refinement tools are needed to generate protein structural models. We have previously developed an iterative Rosetta-MDFF protocol that used cryo-EM densities to refine protein structures. Here we show that, in addition to cryo-EM densities, incorporation of other experimental restraints into the Rosetta-MDFF protocol further improved refined structures. We used NMR chemical shift (CS) data integrated with cryo-EM densities in our hybrid protocol in both the Rosetta step and the molecular dynamics (MD) simulations step. In 15 out of 18 cases for all MD rounds, the refinement results obtained when density maps and NMR chemical shift data were used in combination outperformed those of density map-only refinement. Notably, the improvement in refinement was highest when medium and low-resolution density maps were used. With our hybrid method, the RMSDs of final models obtained were always better than the RMSDs obtained by our previous protocol with just density refinement for both medium (6.9 Å) and low (9 Å) resolution maps. For all the six test proteins with medium resolution density maps (6.9 Å), the final refined structure RMSDs were lower for the hybrid method than for the cryo-EM only refinement. The final refined RMSDs were less than 1.5 Å when our hybrid protocol was used with 4 Å density maps. For four out of the six proteins the final RMSDs were even less than 1 Å. This study demonstrates that by using a combination of cryo-EM and NMR restraints, it is possible to refine structures to atomic resolution, outperforming single restraint refinement. This hybrid protocol will be a valuable tool when only low-resolution cryo-EM density data and NMR chemical shift data are available to refine structures.

Project description:Two major techniques have been used to determine the three-dimensional structures of proteins: X-ray diffraction and NMR spectroscopy. In particular, the validation of NMR-derived protein structures is one of the most challenging problems in NMR spectroscopy. Therefore, researchers have proposed a plethora of methods to determine the accuracy and reliability of protein structures. Despite these proposals, there is a growing need for more sophisticated, physics-based structure validation methods. This approach will enable us to (a) characterize the "quality" of the NMR-derived ensemble as a whole by a single parameter, (b) unambiguously identify flaws in the sequence at a residue level, and (c) provide precise information, such as sets of backbone and side-chain torsional angles, that we can use to detect local flaws. Rather than reviewing all of the existing validation methods, this Account describes the contributions of our research group toward a solution of the long-standing problem of both global and local structure validation of NMR-derived protein structures. We emphasize a recently introduced physics-based methodology that makes use of observed and computed (13)C(alpha) chemical shifts (at the density functional theory (DFT) level of theory) for an accurate validation of protein structures in solution and in crystals. By assessing the ability of computed (13)C(alpha) chemical shifts to reproduce observed (13)C(alpha) chemical shifts of a single structure or ensemble of structures in solution and in crystals, we accomplish a global validation by using the conformationally averaged root-mean-square deviation, ca-rmsd, as a scoring function. In addition, the method enables us to provide local validation by identifying a set of individual amino acid conformations for which the computed and observed (13)C(alpha) chemical shifts do not agree within a certain error range and may represent a nonreliable fold of the protein model. Although it is computationally intensive, our validation method has several advantages, which we illustrate through a series of applications. This method makes use of the (13)C(alpha) chemical shifts, not shielding, that are ubiquitous to proteins and can be computed precisely from the phi, psi, and chi torsional angles. There is no need for a priori knowledge of the oligomeric state of the protein, and no knowledge-based information or additional NMR data are required. The primary limitation at this point is the computational cost of such calculations. However, we anticipate that enhancements in the speed of calculating these chemical shifts coupled with the ever-increasing computational power should soon make this a standard method accessible to the general NMR community.

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Chemical shifts-based similarity restraints improve accuracy of RNA structures determined via NMR.

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