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

Nanobody Paratope Ensembles in Solution Characterized by MD Simulations and NMR

Variable domains of camelid antibodies (so-called nanobodies or V_HH) are the smallest antibody fragments that retain complete functionality and therapeutic potential. Understanding of the nanobody-binding interface has become a pre-requisite for rational antibody design and engineering. The nanobody-binding interface consists of up to three hypervariable loops, known as the CDR loops. Here, we structurally and dynamically characterize the conformational diversity of an anti-GFP-binding nanobody by using molecular dynamics simulations in combination with experimentally derived data from nuclear magnetic resonance (NMR) spectroscopy. The NMR data contain both structural and dynamic information resolved at various timescales, which allows an assessment of the quality of protein MD simulations. Thus, in this study, we compared the ensembles for the anti-GFP-binding nanobody obtained from MD simulations with results from NMR. We find excellent agreement of the NOE-derived distance maps obtained from NMR and MD simulations and observe similar conformational spaces for the simulations with and without NOE time-averaged restraints. We also compare the measured and calculated order parameters and find generally good agreement for the motions observed in the ps–ns timescale, in particular for the CDR3 loop. Understanding of the CDR3 loop dynamics is especially critical for nanobodies, as this loop is typically critical for antigen recognition.

Combining Experimental Data and Computational Methods for the Non-Computer Specialist

Experimental methods are indispensable for the study of the function of biological macromolecules, not just as static structures, but as dynamic systems that change conformation, bind partners, perform reactions, and respond to different stimulus. However, providing a detailed structural interpretation of the results is often a very challenging task. While experimental and computational methods are often considered as two different and separate approaches, the power and utility of combining both is undeniable. The integration of the experimental data with computational techniques can assist and enrich the interpretation, providing new detailed molecular understanding of the systems. Here, we briefly describe the basic principles of how experimental data can be combined with computational methods to obtain insights into the molecular mechanism and expand the interpretation through the generation of detailed models.

NMR Methods to Study Dynamic Allostery

Nuclear magnetic resonance (NMR) spectroscopy provides a unique toolbox of experimental probes for studying dynamic processes on a wide range of timescales, ranging from picoseconds to milliseconds and beyond. Along with NMR hardware developments, recent methodological advancements have enabled the characterization of allosteric proteins at unprecedented detail, revealing intriguing aspects of allosteric mechanisms and increasing the proportion of the conformational ensemble that can be observed by experiment. Here, we present an overview of NMR spectroscopic methods for characterizing equilibrium fluctuations in free and bound states of allosteric proteins that have been most influential in the field. By combining NMR experimental approaches with molecular simulations, atomistic-level descriptions of the mechanisms by which allosteric phenomena take place are now within reach.

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.

Information flow and protein dynamics: the interplay between nuclear magnetic resonance spectroscopy and molecular dynamics simulations

Proteins participate in information pathways in cells, both as links in the chain of signals, and as the ultimate effectors. Upon ligand binding, proteins undergo conformation and motion changes, which can be sensed by the following link in the chain of information. Nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics (MD) simulations represent powerful tools for examining the time-dependent function of biological molecules. The recent advances in NMR and the availability of faster computers have opened the door to more detailed analyses of structure, dynamics, and interactions. Here we briefly describe the recent applications that allow NMR spectroscopy and MD simulations to offer unique insight into the basic motions that underlie information transfer within and between cells.

NXO beta structure mimicry: an ultrashort turn/hairpin mimic that folds in water

An NXO building block derived tetrapeptide mimic emulates a natural proline-glycine β-turn/hairpin in polar media, including water at room temperature.

The structure of neuronal calcium sensor-1 in solution revealed by molecular dynamics simulations

Neuronal calcium sensor-1 (NCS-1) is a protein able to trigger signal transduction processes by binding a large number of substrates and re-shaping its structure depending on the environmental conditions. The X-ray crystal structure of the unmyristoilated NCS-1 shows a large solvent-exposed hydrophobic crevice (HC); this HC is partially occupied by the C-terminal tail and thus elusive to the surrounding solvent. We studied the native state of NCS-1 by performing room temperature molecular dynamics (MD) simulations starting from the crystal and the solution structures. We observe relaxation to a state independent of the initial structure, in which the C-terminal tail occupies the HC. We suggest that the C-terminal tail shields the HC binding pocket and modulates the affinity of NCS-1 for its natural targets. By analyzing the topology and nature of the inter-residue potential energy, we provide a compelling description of the interaction network that determines the three-dimensional organization of NCS-1.

Ensemble-based interpretations of NMR structural data to describe protein internal dynamics

NMR spectroscopy is the leading technique to characterize protein internal dynamics at the atomic level and on multiple time scales. However, the structural interpretation of the observables obtained by various measurements is not always straightforward and in many cases dynamics-related parameters are only used to “decorate” static structural models without offering explicit description of conformational heterogeneity. To overcome such limitations, several computational techniques have been developed to generate ensemble-based representations of protein structure and dynamics with the use of NMR-derived data. An important common aspect of the methods is that NMR observables and derived parameters are interpreted as properties of the ensemble instead of individual conformers. The resulting ensembles reflect the experimentally determined internal mobility of proteins at a given time scale and can be used to understand the role of internal motions in biological processes at atomic detail. In this review we provide an overview of the calculation methods currently available and examples of biological insights obtained by the ensemble-based models of the proteins investigated.

Conformational Sampling by Ab Initio Molecular Dynamics Simulations Improves NMR Chemical Shift Predictions

Car-Parrinello molecular dynamics simulations were performed for N-methyl acetamide as a small test system for amide groups in protein backbones, and NMR chemical shifts were calculated based on the generated ensemble. If conformational sampling and explicit solvent molecules are taken into account, excellent agreement between the calculated and experimental chemical shifts is obtained. These results represent a landmark improvement over calculations based on classical molecular dynamics (MD) simulations especially for amide protons, which are predicted too high-field shifted based on the latter ensembles. We were able to show that the better results are caused by the solute-solvents interactions forming shorter hydrogen bonds as well as by the internal degrees of freedom of the solute. Inspired by these results, we propose our approach as a new tool for the validation of force fields due to its power of identifying the structural reasons for discrepancies between the experimental and calculated data.