A novel high-dimensional NMR experiment for resolving protein backbone dihedral angle ambiguities

A complete set of cross-correlated relaxation experiments for determining the protein backbone dihedral angles

The investigation of structural propensities of proteins is essential for understanding how they function at the molecular level. NMR, offering atomic-scale information, is often the method of choice. One of the available techniques relies on the cross-correlated relaxation (CCR) effect, whose magnitude is related to local spatial conformation. Application of these methods is difficult if the protein under investigation exhibits high mobility, because NMR observables like CCR rates and chemical shifts present themselves as mere averages of an underlying ensemble distribution. Furthermore, relaxation observables are a convolution of structural and dynamical components. Despite these challenges, it is possible to infer the underlying structural ensemble by combining information from several CCR rates with a different geometrical dependence. In this paper, we present a set of eight CCR experiments tailored for proteins of a highly dynamic nature. Analyzed together, they yield a distribution of backbone dihedral angles for each residue of the protein. The experiments were validated on the folded protein ubiquitin using PDB-deposited NMR structures for comparison. Extraordinary peak separation, achieved by evolving four different chemical shifts, allows for the application of this method to intrinsically disordered proteins in future studies.

nus-tool: A unified program for generating and analyzing sample schedules for nonuniformly sampled NMR experiments

Increases in digital resolution achieved by high-field NMR require increases in spectral width. Additionally, the ability to resolve two overlapping peaks requires a sufficiently long acquisition time. These constraints combine, so that achieving high resolution spectra on high-field magnets requires long experiment times when employing uniform sampling and Fourier Transform processing. These limitations may be addressed by using nonuniform sampling (NUS), but the complexity of the parameter space across the variety of available NUS schemes greatly hinders the establishment of optimal approaches and best practices. We address these challenges with nus-tool, which is a software package for generating and analyzing NUS schedules. The nus-tool software internally implements random sampling and exponentially biased sampling. Through pre-configured plug-ins, it also provides access to quantile sampling and Poisson gap sampling. The software computes the relative sensitivity, mean evolution time, point spread function, and peak-to-sidelobe ratio; all of which can be determined for a candidate sample schedule prior to running an experiment to verify expected sensitivity, resolution, and artifact suppression. The nus-tool package is freely available on the NMRbox platform through an interactive GUI and via the command line, which is especially useful for scripted workflows that investigate the effectiveness of various NUS schemes.

How much metagenome data is needed for protein structure prediction: The advantages of targeted approach from the ecological and evolutionary perspectives

It has been proven that three-dimensional protein structures could be modeled by supplementing homologous sequences with metagenome sequences. Even though a large volume of metagenome data is utilized for such purposes, a significant proportion of proteins remain unsolved. In this review, we focus on identifying ecological and evolutionary patterns in metagenome data, decoding the complicated relationships of these patterns with protein structures, and investigating how these patterns can be effectively used to improve protein structure prediction. First, we proposed the metagenome utilization efficiency and marginal effect model to quantify the divergent distribution of homologous sequences for the protein family. Second, we proposed that the targeted approach effectively identifies homologous sequences from specified biomes compared with the untargeted approach's blind search. Finally, we determined the lower bound for metagenome data required for predicting all the protein structures in the Pfam database and showed that the present metagenome data is insufficient for this purpose. In summary, we discovered ecological and evolutionary patterns in the metagenome data that may be used to predict protein structures effectively. The targeted approach is promising in terms of effectively extracting homologous sequences and predicting protein structures using these patterns.

Tandem domain structure determination based on a systematic enumeration of conformations

Protein structure determination is undergoing a change of perspective due to the larger importance taken in biology by the disordered regions of biomolecules. In such cases, the convergence criterion is more difficult to set up and the size of the conformational space is a obstacle to exhaustive exploration. A pipeline is proposed here to exhaustively sample protein conformations using backbone angle limits obtained by nuclear magnetic resonance (NMR), and then to determine the populations of conformations. The pipeline is applied to a tandem domain of the protein whirlin. An original approach, derived from a reformulation of the Distance Geometry Problem is used to enumerate the conformations of the linker connecting the two domains. Specifically designed procedure then permit to assemble the domains to the linker conformations and to optimize the tandem domain conformations with respect to two sets of NMR measurements: residual dipolar couplings and paramagnetic resonance enhancements. The relative populations of optimized conformations are finally determined by fitting small angle X-ray scattering (SAXS) data. The most populated conformation of the tandem domain is a semi-closed one, fully closed and more extended conformations being in minority, in agreement with previous observations. The SAXS and NMR data show different influences on the determination of populations.

Using Cross-Correlated Spin Relaxation to Characterize Backbone Dihedral Angle Distributions of Flexible Protein Segments

Crucial to the function of proteins is their existence as conformational ensembles sampling numerous and structurally diverse substates. Despite this widely accepted notion there is still a high demand for meaningful and reliable approaches to characterize protein ensembles in solution. As it is usually conducted in solution, NMR spectroscopy offers unique possibilities to address this challenge. Particularly, cross-correlated relaxation (CCR) effects have long been established to encode both protein structure and dynamics in a compelling manner. However, this wealth of information often limits their use in practice as structure and dynamics might prove difficult to disentangle. Using a modern Maximum Entropy (MaxEnt) reweighting approach to interpret CCR rates of Ubiquitin, we demonstrate that these uncertainties do not necessarily impair resolving CCR-encoded structural information. Instead, a suitable balance between complementary CCR experiments and prior information is found to be the most crucial factor in mapping backbone dihedral angle distributions. Experimental and systematic deviations such as oversimplified dynamics appear to be of minor importance. Using Ubiquitin as an example, we demonstrate that CCR rates are capable of characterizing rigid and flexible residues alike, indicating their unharnessed potential in studying disordered proteins.