On the calculation of ³Jαβ-coupling constants for side chains in proteins

On the Validation of Protein Force Fields Based on Structural Criteria

Molecular dynamics simulations depend critically on the quality of the force field used to describe the interatomic interactions and the extent to which it has been validated for use in a specific application. Using a curated test set of 52 high-resolution structures, 39 derived from X-ray diffraction and 13 solved using NMR, we consider the extent to which different parameter sets of the GROMOS protein force field can be distinguished based on comparing a range of structural criteria, including the number of backbone hydrogen bonds, the number of native hydrogen bonds, polar and nonpolar solvent-accessible surface area, radius of gyration, the prevalence of secondary structure elements, J-coupling constants, nuclear Overhauser effect (NOE) intensities, positional root-mean-square deviations (RMSD), and the distribution of backbone ϕ and ψ dihedral angles. It is shown that while statistically significant differences between the average values of individual metrics could be detected, these were in general small. Furthermore, improvements in agreement in one metric were often offset by loss of agreement in another. The work establishes a framework and test set against which protein force fields can be validated. It also highlights the danger of inferring the relative quality of a given force field based on a small range of structural properties or small number of proteins.

On the use of 3J-coupling NMR data to derive structural information on proteins

Values of ³J-couplings as obtained from NMR experiments on proteins cannot easily be used to determine protein structure due to the difficulty of accounting for the high sensitivity of intermediate ³J-coupling values (4-8 Hz) to the averaging period that must cover the conformational variability of the torsional angle related to the ³J-coupling, and due to the difficulty of handling the multiple-valued character of the inverse Karplus relation between torsional angle and ³J-coupling. Both problems can be solved by using ³J-coupling time-averaging local-elevation restraining MD simulation. Application to the protein hen egg white lysozyme using 213 backbone and side-chain ³J-coupling restraints shows that a conformational ensemble compatible with the experimental data can be obtained using this technique, and that accounting for averaging and the ability of the algorithm to escape from local minima for the torsional angle induced by the Karplus relation, are essential for a comprehensive use of ³J-coupling data in protein structure determination.

On the Use of Side-Chain NMR Relaxation Data to Derive Structural and Dynamical Information on Proteins: A Case Study Using Hen Lysozyme

Values of S2CH and S2NH order parameters derived from NMR relaxation measurements on proteins cannot be used straightforwardly to determine protein structure because they cannot be related to a single protein structure, but are defined in terms of an average over a conformational ensemble. Molecular dynamics simulation can generate a conformational ensemble and thus can be used to restrain S2CH and S2NH order parameters towards experimentally derived target values S2CH (exp) and S2NH (exp). Application of S2CH and S2NH order‐parameter restraining MD simulation to bond vectors in 63 side chains of the protein hen egg white lysozyme using 51 S2CH (exp) target values and 28 S2NH (exp) target values shows that a conformational ensemble compatible with the experimentally derived data can be obtained by using this technique. It is observed that S2CH order‐parameter restraining of C−H bonds in methyl groups is less reliable than S2NH order‐parameter restraining because of the possibly less valid assumptions and approximations used to derive experimental S2CH (exp) values from NMR relaxation measurements and the necessity to adopt the assumption of uniform rotational motion of methyl C−H bonds around their symmetry axis and of the independence of these motions from each other. The restrained simulations demonstrate that side chains on the protein surface are highly dynamic. Any hydrogen bonds they form and that appear in any of four different crystal structures, are fluctuating with short lifetimes in solution.

Hamiltonian Reweighing To Refine Protein Backbone Dihedral Angle Parameters in the GROMOS Force Field

Molecular dynamics simulations of proteins depend critically on the underlying force field, which may be parameterized against experimental data or high-quality quantum calculations. Here, we develop search algorithms based on Monte Carlo and steepest descent calculations to optimize the backbone dihedral angle parameters from a single reference simulation. We apply these tools to improve the agreement between simulations of single, capped amino acids and experimentally determined J values and secondary structure propensities of these molecules. The parameters are further refined based on simulations of a set of seven proteins and finally validated in simulations on a large set of 52 protein structures. Improvements in the dihedral angle distributions are observed, and structural propensities of the proteins are reproduced very well. Overall, the GROMOS 54A8_bb parameter set forms an improvement to previous parameter sets, both for small molecules and for protein simulations.

A critical assessment of force field accuracy against NMR data for cyclic peptides containing β-amino acids

Hybrid cyclic α/β-peptides, in which one or more β-amino acids are incorporated into the backbone, are gaining increasing interest as potential therapeutics, thanks to their ability to achieve enhanced binding affinities for a biological target through pre-organization in solution. The in silico prediction of their three dimensional structure through strategies such as MD simulations would substantially advance the rational design process. However, whether the molecular mechanics force fields are accurate in sampling highly constrained cyclopeptides containing β-amino acids remains to be verified. Here, we present a systematic assessment of the ability of 8 widely used force fields to reproduce 79 NMR observables (including chemical shifts and 3J scalar couplings) on five cyclic α/β-peptides that contain the integrin recognition motif isoDGR. Most of the investigated force fields, which include force fields from AMBER, OPLS, CHARMM and GROMOS families, display very good agreement with experimental 3J(HN,Hα), suggesting that MD simulations could be an appropriate tool in the rational design of therapeutic cyclic α-peptides. However, for NMR observables directly related to β-amino acids, we observed a poor agreement with experiments and a remarkable dependence of our evaluation on the choice of Karplus parameters. The force field weaknesses herein unveiled might constitute a source of inspiration for further force field optimization.

Validation of Molecular Simulation: An Overview of Issues

Computer simulation of molecular systems enables structure-energy-function relationships of molecular processes to be described at the sub-atomic, atomic, supra-atomic, or supra-molecular level. To interpret results of such simulations appropriately, the quality of the calculated properties must be evaluated. This depends on the way the simulations are performed and on the way they are validated by comparison to values Q^exp of experimentally observable quantities Q. One must consider 1) the accuracy of Q^exp , 2) the accuracy of the function Q(r^N ) used to calculate a Q-value based on a molecular configuration r^N of N particles, 3) the sensitivity of the function Q(r^N ) to the configuration r^N , 4) the relative time scales of the simulation and experiment, 5) the degree to which the calculated and experimental properties are equivalent, and 6) the degree to which the system simulated matches the experimental conditions. Experimental data is limited in scope and generally corresponds to averages over both time and space. A critical analysis of the various factors influencing the apparent degree of (dis)agreement between simulations and experiment is presented and illustrated using examples from the literature. What can be done to enhance the validation of molecular simulation is also discussed.

Deriving Structural Information from Experimentally Measured Data on Biomolecules

During the past half century, the number and accuracy of experimental techniques that can deliver values of observables for biomolecular systems have been steadily increasing. The conversion of a measured value Q^exp of an observable quantity Q into structural information is, however, a task beset with theoretical and practical problems: 1) insufficient or inaccurate values of Q^exp , 2) inaccuracies in the function Q(r→) used to relate the quantity Q to structure r→ , 3) how to account for the averaging inherent in the measurement of Q^exp , 4) how to handle the possible multiple-valuedness of the inverse r→(Q) of the function Q(r→) , to mention a few. These apply to a variety of observable quantities Q and measurement techniques such as X-ray and neutron diffraction, small-angle and wide-angle X-ray scattering, free-electron laser imaging, cryo-electron microscopy, nuclear magnetic resonance, electron paramagnetic resonance, infrared and Raman spectroscopy, circular dichroism, Förster resonance energy transfer, atomic force microscopy and ion-mobility mass spectrometry. The process of deriving structural information from measured data is reviewed with an eye to non-experts and newcomers in the field using examples from the literature of the effect of the various choices and approximations involved in the process. A list of choices to be avoided is provided.

Rapid Sampling of Folding Equilibria of β-Peptides in Methanol Using a Supramolecular Solvent Model

Molecular dynamics simulation of biomolecules in solvent using an atomic model for both the biomolecules and the solvent molecules is still computationally rather demanding considering the time scale of the biomolecular motions. The use of a supramolecular coarse-grained (CG) model can speed up the simulation considerably, but it also reduces the accuracy inevitably. Combining an atomic fine-grained (FG) level of modeling for the biomolecules and a supramolecular CG level for the solvent into a hybrid system, the increased computational efficiency may outweigh the loss of accuracy with respect to the biomolecular properties in the hybrid FG/CG simulation. Here, a previously published CG methanol model is reparametrized, and then a 1:1 mixture of FG and CG methanol is used to calibrate the FG-CG interactions using thermodynamic and dielectric screening data for liquid methanol. The FG-CG interaction parameter set is applied in hybrid FG/CG solute/solvent simulations of the folding equilibria of three β-peptides that adopt different folds. The properties of the peptides are compared with those obtained in FG solvent simulations and with experimental NMR data. The comparison shows that the folding equilibria in the pure CG solvent simulations are different from those in the FG solvent simulations because of the lack of hydrogen-bonding partners in the supramolecular CG solvent. Next, we introduced an FG methanol layer around the peptides in CG solvent to recover the hydrogen-bonding pattern of the FG solvent simulations. The result shows that with the FG methanol layer, the folding equilibria of the three β-peptides are very similar to those in the FG solvent simulations, while the computational efficiency is at least 3 times higher and the cutoff radius for nonbonded interactions could be increased from 1.4 to 2.0 nm.

Force field-dependent solution properties of glycine oligomers

Molecular simulations can be used to study disordered polypeptide systems and to generate hypotheses on the underlying structural and thermodynamic mechanisms that govern their function. As the number of disordered protein systems investigated with simulations increase, it is important to understand how particular force fields affect the structural properties of disordered polypeptides in solution. To this end, we performed a comparative structural analysis of Gly(3) and Gly(10) in aqueous solution from all atom, microsecond molecular dynamics (MD) simulations using the CHARMM 27 (C27), CHARMM 36 (C36), and Amber ff12SB force fields. For each force field, Gly(3) and Gly(10) were simulated for at least 300 ns and 1 μs, respectively. Simulating oligoglycines of two different lengths allows us to evaluate how force field effects depend on polypeptide length. Using a variety of structural metrics (e.g., end-to-end distance, radius of gyration, dihedral angle distributions), we characterize the distribution of oligoglycine conformers for each force field and show that each sample conformation space differently, yielding considerably different structural tendencies of the same oligoglycine model in solution. Notably, we find that C36 samples more extended oligoglycine structures than both C27 and ff12SB.

Conformational dynamics and distribution of nitroxide spin labels

Long-range distance measurements based on paramagnetic relaxation enhancement (PRE) in NMR, quantification of surface water dynamics near biomacromolecules by Overhauser dynamic nuclear polarization (DNP) and sensitivity enhancement by solid-state DNP all depend on introducing paramagnetic species into an otherwise diamagnetic NMR sample. The species can be introduced by site-directed spin labeling, which offers precise control for positioning the label in the sequence of a biopolymer. However, internal flexibility of the spin label gives rise to dynamic processes that potentially influence PRE and DNP behavior and leads to a spatial distribution of the electron spin even in solid samples. Internal dynamics of spin labels and their static conformational distributions have been studied mainly by electron paramagnetic resonance spectroscopy and molecular dynamics simulations, with a large body of results for the most widely applied methanethiosulfonate spin label MTSL. These results are critically discussed in a unifying picture based on rotameric states of the group that carries the spin label. Deficiencies in our current understanding of dynamics and conformations of spin labeled groups and of their influence on NMR observables are highlighted and directions for further research suggested.

Assessing and refining molecular dynamics simulations of proteins with nuclear magnetic resonance data

The sophistication of the force fields, algorithms and hardware used for molecular dynamics (MD) simulations of proteins is continuously increasing. No matter how advanced the methodology, however, it is essential to evaluate the appropriateness of the structures sampled in a simulation by comparison with quantitative experimental data. Solution nuclear magnetic resonance (NMR) data are particularly useful for checking the quality of protein simulations, as they provide both structural and dynamic information on a variety of temporal and spatial scales. Here, various features and implications of using NMR data to validate and bias MD simulations are outlined, including an overview of the different types of NMR data that report directly on structural properties and of relevant simulation techniques. The focus throughout is on how to properly account for conformational averaging, particularly within the context of the assumptions inherent in the relationships that link NMR data to structural properties.

Solvating atomic level fine-grained proteins in supra-molecular level coarse-grained water for molecular dynamics simulations

Simulation of the dynamics of a protein in aqueous solution using an atomic model for both the protein and the many water molecules is still computationally extremely demanding considering the time scale of protein motions. The use of supra-atomic or supra-molecular coarse-grained (CG) models may enhance the computational efficiency, but inevitably at the cost of reduced accuracy. Coarse-graining solvent degrees of freedom is likely to yield a favourable balance between reduced accuracy and enhanced computational speed. Here, the use of a supra-molecular coarse-grained water model that largely preserves the thermodynamic and dielectric properties of atomic level fine-grained (FG) water in molecular dynamics simulations of an atomic model for four proteins is investigated. The results of using an FG, a CG, an implicit, or a vacuum solvent environment of the four proteins are compared, and for hen egg-white lysozyme a comparison to NMR data is made. The mixed-grained simulations do not show large differences compared to the FG atomic level simulations, apart from an increased tendency to form hydrogen bonds between long side chains, which is due to the reduced ability of the supra-molecular CG beads that represent five FG water molecules to make solvent-protein hydrogen bonds. But, the mixed-grained simulations are at least an order of magnitude faster than the atomic level ones.

An improved structural characterisation of reduced French bean plastocyanin based on NMR data and local-elevation molecular dynamics simulation

Deriving structural information about a protein from NMR experimental data is still a non-trivial challenge to computational biochemistry. This is because of the low ratio of the number of independent observables to the number of molecular degrees of freedom, the approximations involved in the different relationships between particular observable quantities and molecular conformation, and the averaged character of the experimental data. For example, protein (3)J-coupling data are seldom used for structure refinement because of the multiple-valuedness and limited accuracy of the Karplus relationship linking a (3)J-coupling to a torsional angle. Moreover, sampling of the large conformational space is still problematic. Using the 99-residue protein plastocyanin as an example we investigated whether use of a thermodynamically calibrated force field, inclusion of solvent degrees of freedom, and application of adaptive local-elevation sampling that accounts for conformational averaging produces a more realistic representation of the ensemble of protein conformations than standard single-structure refinement in a non-explicit solvent using restraints that do not account for averaging and are partly based on non-observed data. Yielding better agreement with observed experimental data, the protein conformational ensemble is less restricted than when using standard single-structure refinement techniques, which are likely to yield a picture of the protein which is too rigid.