Synergistic applications of MD and NMR for the study of biological systems

Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data

Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets.

Wiggle and Shake: Managing and Exploiting Conformational Dynamics during Proteasome Biogenesis

The 26S proteasome is the largest and most complicated protease known, and changes to proteasome assembly or function contribute to numerous human diseases. Assembly of the 26S proteasome from its ~66 individual polypeptide subunits is a highly orchestrated process requiring the concerted actions of both intrinsic elements of proteasome subunits, as well as assistance by extrinsic, dedicated proteasome assembly chaperones. With the advent of near-atomic resolution cryo-electron microscopy, it has become evident that the proteasome is a highly dynamic machine, undergoing numerous conformational changes in response to ligand binding and during the proteolytic cycle. In contrast, an appreciation of the role of conformational dynamics during the biogenesis of the proteasome has only recently begun to emerge. Herein, we review our current knowledge of proteasome assembly, with a particular focus on how conformational dynamics guide particular proteasome biogenesis events. Furthermore, we highlight key emerging questions in this rapidly expanding area.

Molecular dynamics simulations and solid-state nuclear magnetic resonance spectroscopy measurements of C-H bond order parameters and effective correlation times in a POPC-GM3 bilayer

Glycolipids such as gangliosides affect the properties of lipid membranes and in extension the interactions between membranes and other biomolecules like proteins. To better understand how the properties of individual lipid molecules can contribute to shape the functional aspects of a membrane, the spatial restriction and dynamics of C-H bond segments can be measured using nuclear magnetic resonance (NMR) spectroscopy. We combine solid-state NMR spectroscopy with all-atom molecular dynamics (MD) simulations to investigate how ganglioside GM3 affects the bilayer structure and dynamics of C-H bond segments. These two methods yield reorientational correlation functions, molecular profiles of C-H bond order parameters |S_CH| and effective correlation times τ_e, which we compare for lipids in POPC bilayers with and without 30 mol% GM3. Our results revealed that all C-H segments of POPC reorient slower in the presence of GM3 and that the defining features of the GM3-POPC bilayer lie in the GM3 headgroup; it gives the bilayer an extended headgroup layer with high order (|S_CH| up to 0.3-0.4) and slow dynamics (τ_e up to 100 ns), a character that may be mechanistically important in ganglioside interactions with other biomolecules.

Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and "promiscuous" interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.

Out-of-Equilibrium Biophysical Chemistry: The Case for Multidimensional, Integrated Single-Molecule Approaches

Out-of-equilibrium processes are ubiquitous across living organisms and all structural hierarchies of life. At the molecular scale, out-of-equilibrium processes (for example, enzyme catalysis, gene regulation, and motor protein functions) cause biological macromolecules to sample an ensemble of conformations over a wide range of time scales. Quantifying and conceptualizing the structure–dynamics to function relationship is challenging because continuously evolving multidimensional energy landscapes are necessary to describe nonequilibrium biological processes in biological macromolecules. In this perspective, we explore the challenges associated with state-of-the-art experimental techniques to understanding biological macromolecular function. We argue that it is time to revisit how we probe and model functional out-of-equilibrium biomolecular dynamics. We suggest that developing integrated single-molecule multiparametric force–fluorescence instruments and using advanced molecular dynamics simulations to study out-of-equilibrium biomolecules will provide a path towards understanding the principles of and mechanisms behind the structure–dynamics to function paradigm in biological macromolecules.

Allosteric and dynamic control of RNA-dependent RNA polymerase function and fidelity

All RNA viruses encode an RNA-dependent RNA polymerase (RdRp) responsible for genome replication. It is now recognized that enzymes in general, and RdRps specifically, are dynamic macromolecular machines such that their moving parts, including active site loops, play direct functional roles. While X-ray crystallography has provided deep insight into structural elements important for RdRp function, this methodology generally provides only static snapshots, and so is limited in its ability to report on dynamic fluctuations away from the lowest energy conformation. Nuclear magnetic resonance (NMR), molecular dynamics (MD) simulations and other biophysical techniques have brought new insight into RdRp function by their ability to characterize the trajectories, kinetics and thermodynamics of conformational motions. In particular, these methodologies have identified coordinated motions among conserved structural motifs necessary for nucleotide selection and incorporation. Disruption of these motions through amino acid substitutions or inhibitor binding impairs RdRp function. Understanding and re-engineering these motions thus provides exciting new avenues for anti-viral strategies. This chapter outlines the basics of these methodologies, summarizes the dynamic motions observed in different RdRps important for nucleotide selection and incorporation, and illustrates how this information can be leveraged towards rational vaccine strain development and anti-viral drug design.

Molecular Dynamics-From Small Molecules to Macromolecules

All molecular systems, from small molecules to macromolecules, exhibit specific characteristics for a specific environment and time. In order to gain an accurate understanding of the functions of all types of molecules, studies of their structure and dynamics are essential. Through dynamic studies, using techniques such as spectroscopy, structure determination, and computer analysis, it is possible to collect functional information on molecules at specific times and in specific environments. Such information not only reveals the properties and mechanisms of action of molecules but also provides insights that can be applied to various industries, such as the development of new materials and drugs. Herein, I discuss the importance of molecular dynamics studies, present the time scale of molecular motion, and review techniques for analyzing molecular dynamics.

Transient Diffusive Interactions with a Protein Crowder Affect Aggregation Processes of Superoxide Dismutase 1 β-Barrel

Aggregate formation of superoxide dismutase 1 (SOD1) inside motor neurons is known as a major factor in onset of amyotrophic lateral sclerosis. The thermodynamic stability of the SOD1 β-barrel has been shown to decrease in crowded environments such as inside a cell, but it remains unclear how the thermodynamics of crowding-induced protein destabilization relate to SOD1 aggregation. Here we have examined the effects of a protein crowder, lysozyme, on fibril aggregate formation of the SOD1 β-barrel. We found that aggregate formation of SOD1 is decelerated even in mildly crowded solutions. Intriguingly, transient diffusive interactions with lysozyme do not significantly affect the static structure of the SOD1 β-barrel but stabilize an alternative excited "invisible" state. The net effect of crowding is to favor species off the aggregation pathway, thereby explaining the decelerated aggregation in the crowded environment. Our observations suggest that the intracellular environment may have a similar negative (inhibitory) effect on fibril formation of other amyloidogenic proteins in living cells. Deciphering how crowded intracellular environments affect aggregation and fibril formation of such disease-associated proteins will probably become central in understanding the exact role of aggregation in the etiology of these enigmatic diseases.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

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.

Stochastic modeling of macromolecules in solution. I. Relaxation processes

A framework for the stochastic description of relaxation processes in flexible macromolecules, including dissipative effects, is introduced from an atomistic point of view. Projection-operator techniques are employed to obtain multidimensional Fokker-Planck operators governing the relaxation of internal coordinates and global degrees of freedom and depending upon parameters fully recoverable from classic force fields (energetics) and continuum models (friction tensors). A hierarchy of approaches of different complexity is proposed in this unified context, aimed primarily at the interpretation of magnetic resonance relaxation experiments. In particular, a model based on a harmonic internal Hamiltonian is discussed as a test case.

Structural dynamics of lytic polysaccharide monoxygenases reveals a highly flexible substrate binding region

Lytic polysaccharide monooxygenases (LPMOs), which are found in fungi, bacteria, and viruses, are redox enzymes utilizing copper to break glycosidic bonds in recalcitrant crystalline form of polysaccharides, such as chitin and cellulose. They are classified by the Carbohydrate-Active enZYmes (CAZy) database under various families. LPMOs's structure with a flat substrate binding region has been shown to contribute to its function, however, the role that LPMOs structural dynamics play during polysaccharide degradation and its mechanism of binding towards substrate are relatively unknown. Here, we report an exhaustive implementation of coarse-grained simulations using Elastic Network Models on multiple LPMO structures to shed light on how their structural dynamics contribute to their chemical function. Using Gaussian network models and Anisotropic network models, we show that the substrate binding region is highly flexible with significant and sustained micro-scale level conformational changes. Significantly, the loops on the binding side of the substrate are most mobile, in concert with the dynamic modes influencing the motions during binding. We also observed dynamic differences between four families of LPMO, namely AA9, AA10, AA11, and AA13 that consist of more than one structure. Specifically, the patterns of motion in the loop regions among the AA9 structures are distinct from those in the AA10 structures.

Therapeutic Inhibition of Myc in Cancer. Structural Bases and Computer-Aided Drug Discovery Approaches

Myc (avian myelocytomatosis viral oncogene homolog) represents one of the most sought after drug targets in cancer. Myc transcription factor is an essential regulator of cell growth, but in most cancers it is overexpressed and associated with treatment-resistance and lethal outcomes. Over 40 years of research and drug development efforts did not yield a clinically useful Myc inhibitor. Drugging the "undruggable" is problematic, as Myc inactivation may negatively impact its physiological functions. Moreover, Myc is a disordered protein that lacks effective binding pockets on its surface. It is well established that the Myc function is dependent on dimerization with its obligate partner, Max (Myc associated factor X), which together form a functional DNA-binding domain to activate genomic targets. Herein, we provide an overview of the knowledge accumulated to date on Myc regulation and function, its critical role in cancer, and summarize various strategies that are employed to tackle Myc-driven malignant transformation. We focus on important structure-function relationships of Myc with its interactome, elaborating structural determinants of Myc-Max dimer formation and DNA recognition exploited for therapeutic inhibition. Chronological development of small-molecule Myc-Max prototype inhibitors and corresponding binding sites are comprehensively reviewed and particular emphasis is placed on modern computational drug design methods. On the outlook, technological advancements may soon provide the so long-awaited Myc-Max clinical candidate.

Rotational Dynamics of Proteins from Spin Relaxation Times and Molecular Dynamics Simulations

Conformational fluctuations and rotational tumbling of proteins can be experimentally accessed with nuclear spin relaxation experiments. However, interpretation of molecular dynamics from the experimental data is often complicated, especially for molecules with anisotropic shape. Here, we apply classical molecular dynamics simulations to interpret the conformational fluctuations and rotational tumbling of proteins with arbitrarily anisotropic shape. The direct calculation of spin relaxation times from simulation data did not reproduce the experimental data. This was successfully corrected by scaling the overall rotational diffusion coefficients around the protein inertia axes with a constant factor. The achieved good agreement with experiments allowed the interpretation of the internal and overall dynamics of proteins with significantly anisotropic shape. The overall rotational diffusion was found to be Brownian, having only a short subdiffusive region below 0.12 ns. The presented methodology can be applied to interpret rotational dynamics and conformation fluctuations of proteins with arbitrary anisotropic shape. However, a water model with more realistic dynamical properties is probably required for intrinsically disordered proteins.

Permeating disciplines: Overcoming barriers between molecular simulations and classical structure-function approaches in biological ion transport

Ion translocation across biological barriers is a fundamental requirement for life. In many cases, controlling this process-for example with neuroactive drugs-demands an understanding of rapid and reversible structural changes in membrane-embedded proteins, including ion channels and transporters. Classical approaches to electrophysiology and structural biology have provided valuable insights into several such proteins over macroscopic, often discontinuous scales of space and time. Integrating these observations into meaningful mechanistic models now relies increasingly on computational methods, particularly molecular dynamics simulations, while surfacing important challenges in data management and conceptual alignment. Here, we seek to provide contemporary context, concrete examples, and a look to the future for bridging disciplinary gaps in biological ion transport. This article is part of a Special Issue entitled: Beyond the Structure-Function Horizon of Membrane Proteins edited by Ute Hellmich, Rupak Doshi and Benjamin McIlwain.

Applications of NMR and computational methodologies to study protein dynamics

Overwhelming evidence now illustrates the defining role of atomic-scale protein flexibility in biological events such as allostery, cell signaling, and enzyme catalysis. Over the years, spin relaxation nuclear magnetic resonance (NMR) has provided significant insights on the structural motions occurring on multiple time frames over the course of a protein life span. The present review article aims to illustrate to the broader community how this technique continues to shape many areas of protein science and engineering, in addition to being an indispensable tool for studying atomic-scale motions and functional characterization. Continuing developments in underlying NMR technology alongside software and hardware developments for complementary computational approaches now enable methodologies to routinely provide spatial directionality and structural representations traditionally harder to achieve solely using NMR spectroscopy. In addition to its well-established role in structural elucidation, we present recent examples that illustrate the combined power of selective isotope labeling, relaxation dispersion experiments, chemical shift analyses, and computational approaches for the characterization of conformational sub-states in proteins and enzymes.

Role of Conformational Motions in Enzyme Function: Selected Methodologies and Case Studies

It is now common knowledge that enzymes are mobile entities relying on complex atomic-scale dynamics and coordinated conformational events for proper ligand recognition and catalysis. However, the exact role of protein dynamics in enzyme function remains either poorly understood or difficult to interpret. This mini-review intends to reconcile biophysical observations and biological significance by first describing a number of common experimental and computational methodologies employed to characterize atomic-scale residue motions on various timescales in enzymes, and second by illustrating how the knowledge of these motions can be used to describe the functional behavior of enzymes and even act upon it. Two biologically relevant examples will be highlighted, namely the HIV-1 protease and DNA polymerase β enzyme systems.

Surface enhanced Raman scattering of amino acids assisted by gold nanoparticles and Gd(3+) ions

The surface enhanced raman scattering (SERS) signal from the l-tyrosine (tyr) molecule adsorbed on gold nanoparticles (Au-tyr) is compared with the SERS signal assisted by the presence of gadolinium ions (Gd(3+)) coordinated with the Au-tyr system. An enhancement factor of the SERS signal in the presence of Gd(3+) ions was ∼5 times higher than that produced by l-tyrosine adsorbed on gold nanoparticles. The enhancement of the SERS signal can be attributed to a corresponding increase in the local electric field due to the presence of Gd(3+) ions in the vicinity of a gold dimer configuration. This scenario was confirmed by solving numerically Maxwell equations, showing an increase of 1 order of magnitude in the local electric scattered field when the Gd(3+) ion is located in between a gold dimer compared with naked gold nanoparticles.

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.

Targeting structural dynamics of the RNA-dependent RNA polymerase for anti-viral strategies

The RNA-dependent RNA polymerase is responsible for genome replication of RNA viruses. Nuclear magnetic resonance experiments and molecular dynamics simulations have indicated that efficient and faithful polymerase function requires highly coordinated internal protein motions. Interference with these motions, either through amino acid substitutions or small molecule binding, can disrupt polymerase and virus function. In particular, these studies have pointed toward highly conserved structural elements, like the motif-D active-site loop, that can be modified to generate polymerases with desired properties. Viruses encoding engineered polymerases might serve as live, attenuated vaccine strains. Further elucidation of polymerase structural dynamics will also provide new avenues for anti-viral drug design.

CABS-flex predictions of protein flexibility compared with NMR ensembles

Motivation: Identification of flexible regions of protein structures is important for understanding of their biological functions. Recently, we have developed a fast approach for predicting protein structure fluctuations from a single protein model: the CABS-flex. CABS-flex was shown to be an efficient alternative to conventional all-atom molecular dynamics (MD). In this work, we evaluate CABS-flex and MD predictions by comparison with protein structural variations within NMR ensembles.

Results: Based on a benchmark set of 140 proteins, we show that the relative fluctuations of protein residues obtained from CABS-flex are well correlated to those of NMR ensembles. On average, this correlation is stronger than that between MD and NMR ensembles. In conclusion, CABS-flex is useful and complementary to MD in predicting protein regions that undergo conformational changes as well as the extent of such changes.

Availability and implementation: The CABS-flex is freely available to all users at http://biocomp.chem.uw.edu.pl/CABSflex.

Contact: sekmi@chem.uw.edu.pl

Supplementary information:Supplementary data are available at Bioinformatics online.

On the binding affinity of macromolecular interactions: daring to ask why proteins interact

Interactions between proteins are orchestrated in a precise and time-dependent manner, underlying cellular function. The binding affinity, defined as the strength of these interactions, is translated into physico-chemical terms in the dissociation constant (K_d), the latter being an experimental measure that determines whether an interaction will be formed in solution or not. Predicting binding affinity from structural models has been a matter of active research for more than 40 years because of its fundamental role in drug development. However, all available approaches are incapable of predicting the binding affinity of protein–protein complexes from coordinates alone. Here, we examine both theoretical and experimental limitations that complicate the derivation of structure–affinity relationships. Most work so far has concentrated on binary interactions. Systems of increased complexity are far from being understood. The main physico-chemical measure that relates to binding affinity is the buried surface area, but it does not hold for flexible complexes. For the latter, there must be a significant entropic contribution that will have to be approximated in the future. We foresee that any theoretical modelling of these interactions will have to follow an integrative approach considering the biology, chemistry and physics that underlie protein–protein recognition.