Discovery through the computational microscope

Beyond Cell-Cell Adhesion: Sensational Cadherins for Hearing and Balance

Cadherins form a large family of proteins often involved in calcium-dependent cellular adhesion. Although classical members of the family can provide a physical bond between cells, a subset of special cadherins use their extracellular domains to interlink apical specializations of single epithelial sensory cells. Two of these cadherins, cadherin-23 (CDH23) and protocadherin-15 (PCDH15), form extracellular "tip link" filaments that connect apical bundles of stereocilia on hair cells essential for inner-ear mechanotransduction. As these bundles deflect in response to mechanical stimuli from sound or head movements, tip links gate hair-cell mechanosensitive channels to initiate sensory perception. Here, we review the unusual and diverse structural properties of these tip-link cadherins and the functional significance of their deafness-related missense mutations. Based on the structural features of CDH23 and PCDH15, we discuss the elasticity of tip links and models that bridge the gap between the nanomechanics of cadherins and the micromechanics of hair-cell bundles during inner-ear mechanotransduction.

Substrate-Dependent Morphology and Its Effect on Electrical Mobility of Doped Poly(3,4-ethylenedioxythiophene) (PEDOT) Thin Films

Deposition dynamics, crystallization, molecular packing, and electronic mobility of poly(3,4-ethylenedioxythiophene) (PEDOT) thin films are affected by the nature of the substrate. Computational microscopy has been carried out to reveal the morphology-substrate dependence for PEDOT thin films doped with molecular tosylate deposited on different substrates including graphite, Si₃N₄, silicon, and amorphous SiO₂. It is shown that the substrate is instrumental in formation of the lamellar structure. PEDOT films on the ordered substrates (graphite, Si₃N₄, and silicon) exhibit preferential face-on orientation, with graphite showing the most ordered and pronounced face-on packing. In contrast, PEDOT on amorphous SiO₂ exhibits the dominant edge-on orientation, except in the dry state where both packings are equally presented. The role of water and the porosity of the substrate in formation of the edge-on structure on SiO₂ is outlined. On the basis of the calculated morphology, the multiscale calculations of the electronic transport and percolative analysis are performed outlining how the character of the substrate affects the electron mobility. It is demonstrated that good crystallinity (PEDOT on graphite substrate) and high content of edge-on (PEDOT on SiO₂ substrate) are not enough to achieve the highest electrical in-plane mobility. Instead, the least ordered material with lower degree of the edge-on content (PEDOT on silicon substrate) provides the highest mobility because it exhibits an efficient network of π-π stacked chain extending throughout the entire sample.

Role of Resultant Dipole Moment in Mechanical Dissociation of Biological Complexes

Protein-peptide interactions play essential roles in many cellular processes and their structural characterization is the major focus of current experimental and theoretical research. Two decades ago, it was proposed to employ the steered molecular dynamics (SMD) to assess the strength of protein-peptide interactions. The idea behind using SMD simulations is that the mechanical stability can be used as a promising and an efficient alternative to computationally highly demanding estimation of binding affinity. However, mechanical stability defined as a peak in force-extension profile depends on the choice of the pulling direction. Here we propose an uncommon choice of the pulling direction along resultant dipole moment (RDM) vector, which has not been explored in SMD simulations so far. Using explicit solvent all-atom MD simulations, we apply SMD technique to probe mechanical resistance of ligand-receptor system pulled along two different vectors. A novel pulling direction—when ligand unbinds along the RDM vector—results in stronger forces compared to commonly used ligand unbinding along center of masses vector. Our observation that RDM is one of the factors influencing the mechanical stability of protein-peptide complex can be used to improve the ranking of binding affinities by using mechanical stability as an effective scoring function.

Determination of the conformational states of strychnine in solution using NMR residual dipolar couplings in a tensor-free approach

Small molecules with rotatable bonds can occupy different conformational states in solution as a consequence of their thermal fluctuations. The accurate determination of the structures of such states, as well as of their statistical weights, has been challenging because of the technical difficulties in extracting information from experimental measurements, which are normally averaged over the conformational space available. Here, to achieve this objective, we present an approach based on a recently proposed tensor-free method for incorporating NMR residual dipolar couplings as structural restraints in replica-averaged molecular dynamics simulations. This approach enables the information provided by the experimental data to be used in the spirit of the maximum entropy principle to determine the structural ensembles of small molecules. Furthermore, in order to enhance the sampling of the conformational space we incorporated the metadynamics method in the simulations. We illustrate the method in the case of strychnine, determining the three major conformational states of this small molecule and their associated occupation probabilities.

MCMap-A Computational Tool for Mapping Energy Landscapes of Transient Protein-Protein Interactions

MCMap is a tool particularly well-suited for analyzing energy landscapes of transient macromolecular complexes. The program applies a Monte Carlo strategy, where the ligand moves randomly in the electrostatic field of the receptor. By applying importance sampling, the major interaction sites are mapped, resulting in a global distribution of ligand-receptor complexes. This approach displays the dynamic character of transiently interacting protein complexes where not a single complex but an ensemble of complexes better describes the protein interactions. The software provides a broad range of analysis options which allow for relating the simulations to experimental data and for interpreting them on a structural level. The application of MCMap is exemplified by the electron-transfer complex of cytochrome c peroxidase and cytochrome c from baker's yeast. The functionality of MCMap and the visualization of simulation data are in particular demonstrated by studying the dependence of the association on ionic strength and on the oxidation state of the binding partner. Furthermore, microscopically, a repulsion of a second ligand can be seen in the ternary complex upon the change of the oxidation state of the bound cytochrome c. The software is made available as open source software together with the example and can be downloaded free of charge from http://www.bisb.uni-bayreuth.de/index.php?page=downloads.

Validating Molecular Dynamics Simulations against Experimental Observables in Light of Underlying Conformational Ensembles

Far from the static, idealized conformations deposited into structural databases, proteins are highly dynamic molecules that undergo conformational changes on temporal and spatial scales that may span several orders of magnitude. These conformational changes, often intimately connected to the functional roles that proteins play, may be obscured by traditional biophysical techniques. Over the past 40 years, molecular dynamics (MD) simulations have complemented these techniques by providing the "hidden" atomistic details that underlie protein dynamics. However, there are limitations of the degree to which molecular simulations accurately and quantitatively describe protein motions. Here we show that although four molecular dynamics simulation packages (AMBER, GROMACS, NAMD, and ilmm) reproduced a variety of experimental observables for two different proteins (engrailed homeodomain and RNase H) equally well overall at room temperature, there were subtle differences in the underlying conformational distributions and the extent of conformational sampling obtained. This leads to ambiguity about which results are correct, as experiment cannot always provide the necessary detailed information to distinguish between the underlying conformational ensembles. However, the results with different packages diverged more when considering larger amplitude motion, for example, the thermal unfolding process and conformational states sampled, with some packages failing to allow the protein to unfold at high temperature or providing results at odds with experiment. While most differences between MD simulations performed with different packages are attributed to the force fields themselves, there are many other factors that influence the outcome, including the water model, algorithms that constrain motion, how atomic interactions are handled, and the simulation ensemble employed. Here four different MD packages were tested each using best practices as established by the developers, utilizing three different protein force fields and three different water models. Differences between the simulated protein behavior using two different packages but the same force field, as well as two different packages with different force fields but the same water models and approaches to restraining motion, show how other factors can influence the behavior, and it is incorrect to place all the blame for deviations and errors on force fields or to expect improvements in force fields alone to solve such problems.

Dynamic coarse-graining fills the gap between atomistic simulations and experimental investigations of mechanical unfolding

We present a dynamic coarse-graining technique that allows one to simulate the mechanical unfolding of biomolecules or molecular complexes on experimentally relevant time scales. It is based on Markov state models (MSMs), which we construct from molecular dynamics simulations using the pulling coordinate as an order parameter. We obtain a sequence of MSMs as a function of the discretized pulling coordinate, and the pulling process is modeled by switching among the MSMs according to the protocol applied to unfold the complex. This way we cover seven orders of magnitude in pulling speed. In the region of rapid pulling, we additionally perform steered molecular dynamics simulations and find excellent agreement between the results of the fully atomistic and the dynamically coarse-grained simulations. Our technique allows the determination of the rates of mechanical unfolding in a dynamical range from approximately 10^-8/ns to 1/ns thus reaching experimentally accessible time regimes without abandoning atomistic resolution.

Multiscale Methods in Drug Design Bridge Chemical and Biological Complexity in the Search for Cures

Drug action is inherently multiscale: it connects molecular interactions to emergent properties at cellular and larger scales. Simulation techniques at each of these different scales are already central to drug design and development, but methods capable of connecting across these scales will extend understanding of complex mechanisms and the ability to predict biological effects. Improved algorithms, ever-more-powerful computing architectures and the accelerating growth of rich datasets are driving advances in multiscale modeling methods capable of bridging chemical and biological complexity from the atom to the cell. Particularly exciting is the development of highly detailed, structure-based, physical simulations of biochemical systems, which are now able to access experimentally relevant timescales for large systems and, at the same time, achieve unprecedented accuracy. In this Perspective, we discuss how emerging data-rich, physics-based multiscale approaches are of the cusp of realizing long-promised impact in the discovery, design and development of novel therapeutics. We highlight emerging methods and applications in this growing field, and outline how different scales can be combined in practical modelling and simulation strategies.

Structural insights into positive and negative allosteric regulation of a G protein-coupled receptor through protein-lipid interactions

Lipids are becoming known as essential allosteric modulators of G protein-coupled receptor (GPCRs). However, how they exert their effects on GPCR conformation at the atomic level is still unclear. In light of recent experimental data, we have performed several long-timescale molecular dynamics (MD) simulations, totalling 24 μs, to rigorously map allosteric modulation and conformational changes in the β₂ adrenergic receptor (β2AR) that occur as a result of interactions with three different phospholipids. In particular, we identify different sequential mechanisms behind receptor activation and deactivation, respectively, mediated by specific lipid interactions with key receptor regions. We show that net negatively charged lipids stabilize an active-like state of β2AR that is able to dock G_sα protein. Clustering of anionic lipids around the receptor with local distortion of membrane thickness is also apparent. On the other hand, net-neutral zwitterionic lipids inactivate the receptor, generating either fully inactive or intermediate states, with kinetics depending on lipid headgroup charge distribution and hydrophobicity. These chemical differences alter membrane thickness and density, which differentially destabilize the β2AR active state through lateral compression effects.

Using molecular simulation to explore the nanoscale dynamics of the plant kinome

Eukaryotic protein kinases (PKs) are a large family of proteins critical for cellular response to external signals, acting as molecular switches. PKs propagate biochemical signals by catalyzing phosphorylation of other proteins, including other PKs, which can undergo conformational changes upon phosphorylation and catalyze further phosphorylations. Although PKs have been studied thoroughly across the domains of life, the structures of these proteins are sparsely understood in numerous groups of organisms, including plants. In addition to efforts towards determining crystal structures of PKs, research on human PKs has incorporated molecular dynamics (MD) simulations to study the conformational dynamics underlying the switching of PK function. This approach of experimental structural biology coupled with computational biophysics has led to improved understanding of how PKs become catalytically active and why mutations cause pathological PK behavior, at spatial and temporal resolutions inaccessible to current experimental methods alone. In this review, we argue for the value of applying MD simulation to plant PKs. We review the basics of MD simulation methodology, the successes achieved through MD simulation in animal PKs, and current work on plant PKs using MD simulation. We conclude with a discussion of the future of MD simulations and plant PKs, arguing for the importance of molecular simulation in the future of plant PK research.

Complexation of short ds RNA/DNA oligonucleotides with Gemini micelles: a time resolved SAXS and computational study

Gene therapy is based on nucleic acid delivery to pathogenic cells in order to modulate their gene expression. The most used non viral vectors are lipid-based nanoaggregates, which are safer than viral carriers and have been shown to assemble easily with both DNA and RNA. However, the transfection efficiency of non viral carriers still needs to be improved before intensive practise in clinical trials can be implemented. For this purpose, the in depth characterization of the complexes formed by nucleic acids and their transporters is of great relevance. In particular, information on the structure and assembly mechanism can be useful to improve our general knowledge of these artificial transfection agents. In this paper, the complexation mechanism of short interfering RNA and DNA molecules (siRNA and siDNA, respectively) with cationic micelles is investigated by combining small angle X-ray scattering experiments and molecular dynamics simulations. Micelles were obtained by Gemini surfactants with different spacer lengths (12-3-12, 12-6-12). The siRNA and siDNA used were double strand molecules characterized by the same length and homologous sequence, in order to perform a close comparison. We showed that complexes appear in solution immediately after mixing and, therefore, the investigation of complex formation requires fast experimental techniques, such as time resolved synchrotron SAXS (Tr-SAXS). The obtained systems had internal arrangement constituted by layers of squeezed micelles alternating the nucleic acids. Both SAXS and MD analyses allowed us to evaluate the mean size of complexes in the range of a few nanometers, with looser and less ordered stacking for the DNA containing aggregates.

Molecular Dynamics Simulations of Protein-Drug Complexes: A Computational Protocol for Investigating the Interactions of Small-Molecule Therapeutics with Biological Targets and Biosensors

MD simulations provide a powerful tool for the investigation of protein-drug complexes. The following chapter uses the aryl acylamidase-acetaminophen system as an example to describe a general protocol for preparing and running simulations of protein-drug complexes, complete with a step-by-step tutorial. The described approach is broadly applicable toward the study of drug interactions in the context of both biological targets and biosensing enzymes.

Molecular Modeling of Multidrug Properties of Resistance Nodulation Division (RND) Transporters

Efflux pumps of the resistance nodulation division (RND) superfamily are among the major contributors to intrinsic and acquired multidrug resistance in Gram-negative bacteria. Structural information on AcrAB-TolC and MexAB-OprM, major efflux pumps of Escherichia coli and Pseudomonas aeruginosa respectively, boosted intensive research aimed at understanding the molecular mechanisms ruling the active extrusion processes. In particular, several studies were devoted to the understanding of the determinants behind the extraordinary broad specificity of the RND transporters AcrB and MexB. In this chapter, we discuss the ever-growing role computational methods have been playing in deciphering key structural and dynamical features of these transporters and of their interaction with substrates and inhibitors. We further discuss and illustrate examples from our lab of how molecular docking, homology modeling, all-atom molecular dynamics simulations and in silico free energy estimations can all together give precious insights into the processes of recognition and extrusion of substrates, as well as on the possible inhibition strategies.

Molecular simulations of cellular processes

It is, nowadays, possible to simulate biological processes in conditions that mimic the different cellular compartments. Several groups have performed these calculations using molecular models that vary in performance and accuracy. In many cases, the atomistic degrees of freedom have been eliminated, sacrificing both structural complexity and chemical specificity to be able to explore slow processes. In this review, we will discuss the insights gained from computer simulations on macromolecule diffusion, nuclear body formation, and processes involving the genetic material inside cell-mimicking spaces. We will also discuss the challenges to generate new models suitable for the simulations of biological processes on a cell scale and for cell-cycle-long times, including non-equilibrium events such as the co-translational folding, misfolding, and aggregation of proteins. A prominent role will be played by the wise choice of the structural simplifications and, simultaneously, of a relatively complex energetic description. These challenging tasks will rely on the integration of experimental and computational methods, achieved through the application of efficient algorithms. Graphical abstract.

An in-silico glimpse into the pH dependent structural changes of T7 RNA polymerase: a protein with simplicity

The capability of performing an array of functions with its single subunit structure makes T7 RNA polymerase (T7RNAP) as one of the simplest yet attractive target for various investigations ranging from structure determinations to several biological tests. In this study, with the help of molecular dynamics (MD) calculations and molecular docking, we investigated the effect of varying pH conditions on conformational flexibility of T7RNAP. We also studied its effect on the interactions with a well established inhibitor (heparin), substrate GTP and T7 promoter of T7RNAP. The simulation studies were validated with the help of three dimensional reconstructions of the polymerase at different pH environments using transmission electron microscopy and single particle analysis. On comparing the simulated structures, it was observed that the structure of T7RNAP changes considerably and interactions with its binding partners also changes as the pH shifts from basic to acidic. Further, it was observed that the C-terminal end plays a vital role in the inefficiency of the polymerase at low pH. Thus, this in-silico study may provide a significant insight into the structural investigations on T7RNAP as well as in designing potent inhibitors against it in varying pH environments.

Kinetic Ductility and Force-Spike Resistance of Proteins from Single-Molecule Force Spectroscopy

Ductile materials can absorb spikes in mechanical force, whereas brittle ones fail catastrophically. Here we develop a theory to quantify the kinetic ductility of single molecules from force spectroscopy experiments, relating force-spike resistance to the differential responses of the intact protein and the unfolding transition state to an applied mechanical force. We introduce a class of unistable one-dimensional potential surfaces that encompass previous models as special cases and continuously cover the entire range from ductile to brittle. Compact analytic expressions for force-dependent rates and rupture-force distributions allow us to analyze force-clamp and force-ramp pulling experiments. We find that the force-transmitting protein domains of filamin and titin are kinetically ductile when pulled from their two termini, making them resistant to force spikes. For the mechanostable muscle protein titin, a highly ductile model reconciles data over 10 orders of magnitude in force loading rate from experiment and simulation.

Evaluation of the hybrid resolution PACE model for the study of folding, insertion, and pore formation of membrane associated peptides

The PACE force field presents an attractive model for conducting molecular dynamics simulations of membrane-protein systems. PACE is a hybrid model, in which lipids and solvents are coarse-grained consistent with the MARTINI mapping, while proteins are described by a united atom model. However, given PACE is linked to MARTINI, which is widely used to study membranes, the behavior of proteins interacting with membranes has only been limitedly examined in PACE. In this study, PACE is used to examine the behavior of several peptides in membrane environments, namely WALP peptides, melittin and influenza hemagglutinin fusion peptide (HAfp). Overall, we find PACE provides an improvement over MARTINI for modeling helical peptides, based on the membrane insertion energetics for WALP16 and more realistic melittin pore dynamics. Our studies on HAfp, which forms a helical hairpin structure, do not show the hairpin structure to be stable, which may point toward a deficiency in the model. © 2017 Wiley Periodicals, Inc.

Molecular dynamics simulations reveal the conformational dynamics of Arabidopsis thaliana BRI1 and BAK1 receptor-like kinases

The structural motifs responsible for activation and regulation of eukaryotic protein kinases in animals have been studied extensively in recent years, and a coherent picture of their activation mechanisms has begun to emerge. In contrast, non-animal eukaryotic protein kinases are not as well understood from a structural perspective, representing a large knowledge gap. To this end, we investigated the conformational dynamics of two key Arabidopsis thaliana receptor-like kinases, brassinosteroid-insensitive 1 (BRI1) and BRI1-associated kinase 1 (BAK1), through extensive molecular dynamics simulations of their fully phosphorylated kinase domains. Molecular dynamics simulations calculate the motion of each atom in a protein based on classical approximations of interatomic forces, giving researchers insight into protein function at unparalleled spatial and temporal resolutions. We found that in an otherwise "active" BAK1 the αC helix is highly disordered, a hallmark of deactivation, whereas the BRI1 αC helix is moderately disordered and displays swinging behavior similar to numerous animal kinases. An analysis of all known sequences in the A. thaliana kinome found that αC helix disorder may be a common feature of plant kinases.

The Extended Generalized Adaptive Biasing Force Algorithm for Multidimensional Free-Energy Calculations

Free-energy calculations in multiple dimensions constitute a challenging problem, owing to the significant computational cost incurred to achieve ergodic sampling. The generalized adaptive biasing force (gABF) algorithm calculates n one-dimensional lists of biasing forces to approximate the n-dimensional matrix by ignoring the coupling terms ordinarily taken into account in classical ABF simulations, thereby greatly accelerating sampling in the multidimensional space. This approximation may however occasionally lead to poor, incomplete exploration of the conformational space compared to classical ABF, especially when the selected coarse variables are strongly coupled. It has been found that introducing extended potentials coupled to the coarse variables of interest can virtually eliminate this shortcoming, and, thus, improve the efficiency of gABF simulations. In the present contribution, we propose a new free-energy method, coined extended generalized ABF (egABF), combining gABF with an extended Lagrangian strategy. The results for three illustrative examples indicate that (i) egABF can explore the transition coordinate much more efficiently compared with classical ABF, eABF, and gABF, in both simple and complex cases and (ii) egABF can achieve a higher accuracy than gABF, with a root mean-squared deviation between egABF and eABF free-energy profiles on the order of k_BT. Furthermore, the new egABF algorithm outruns the previous ABF-based algorithms in high-dimensional free-energy calculations and, hence, represents a powerful importance-sampling alternative for the investigation of complex chemical and biological processes.

A computational study suggests that replacing PEG with PMOZ may increase exposure of hydrophobic targeting moiety

In a previous study we showed that the cause of failure of a new, proposed, targeting ligand, the AETP moiety, when attached to a PEGylated liposome, was occlusion by the poly(ethylene glycol) (PEG) layer due to its hydrophobic nature, given that PEG is not entirely hydrophilic. At the time we proposed that possible replacement with a more hydrophilic protective polymer could alleviate this problem. In this study we have used computational molecular dynamics modelling, using a model with all atom resolution, to suggest that a specific alternative protective polymer, poly(2-methyloxazoline) (PMOZ), would perform exactly this function. Our results show that when PEG is replaced by PMOZ the relative exposure to the solvent of AETP is increased to a level even greater than that we found in previous simulations for the RGD peptide, a targeting moiety that has previously been used successfully in PEGylated liposome based therapies. While the AETP moiety itself is no longer under consideration, the results of this computational study have broader significance: the use of PMOZ as an alternative polymer coating to PEG could be efficacious in the context of more hydrophobic targeting ligands. In addition to PMOZ we studied another polyoxazoline, poly(2-ethyloxazoline) (PEOZ), that has also been mooted as a possible alternate protective polymer. It was also found that the RDG peptide occlusion was significantly greater for the case of both oxazolines as opposed to PEG and that, unlike PEG, neither oxazoline entered the membrane. As far as we are aware this is the first time that polyoxazolines have been studied using molecular dynamics simulation with all atom resolution.

MutSα's Multi-Domain Allosteric Response to Three DNA Damage Types Revealed by Machine Learning

MutSα is a key component in the mismatch repair (MMR) pathway. This protein is responsible for initiating the signaling pathways for DNA repair or cell death. Herein we investigate this heterodimer's post-recognition, post-binding response to three types of DNA damage involving cytotoxic, anti-cancer agents-carboplatin, cisplatin, and FdU. Through a combination of supervised and unsupervised machine learning techniques along with more traditional structural and kinetic analysis applied to all-atom molecular dynamics (MD) calculations, we predict that MutSα has a distinct response to each of the three damage types. Via a binary classification tree (a supervised machine learning technique), we identify key hydrogen bond motifs unique to each type of damage and suggest residues for experimental mutation studies. Through a combination of a recently developed clustering (unsupervised learning) algorithm, RMSF calculations, PCA, and correlated motions we predict that each type of damage causes MutSα to explore a specific region of conformation space. Detailed analysis suggests a short range effect for carboplatin-primarily altering the structures and kinetics of residues within 10 angstroms of the damaged DNA-and distinct longer-range effects for cisplatin and FdU. In our simulations, we also observe that a key phenylalanine residue-known to stack with a mismatched or unmatched bases in MMR-stacks with the base complementary to the damaged base in 88.61% of MD frames containing carboplatinated DNA. Similarly, this Phe71 stacks with the base complementary to damage in 91.73% of frames with cisplatinated DNA. This residue, however, stacks with the damaged base itself in 62.18% of trajectory frames with FdU-substituted DNA and has no stacking interaction at all in 30.72% of these frames. Each drug investigated here induces a unique perturbation in the MutSα complex, indicating the possibility of a distinct signaling event and specific repair or death pathway (or set of pathways) for a given type of damage.

Force generation by titin folding

Titin is a giant protein that provides elasticity to muscle. As the sarcomere is stretched, titin extends hierarchically according to the mechanics of its segments. Whether titin's globular domains unfold during this process and how such unfolded domains might contribute to muscle contractility are strongly debated. To explore the force-dependent folding mechanisms, here we manipulated skeletal-muscle titin molecules with high-resolution optical tweezers. In force-clamp mode, after quenching the force (<10 pN), extension fluctuated without resolvable discrete events. In position-clamp experiments, the time-dependent force trace contained rapid fluctuations and a gradual increase of average force, indicating that titin can develop force via dynamic transitions between its structural states en route to the native conformation. In 4 M urea, which destabilizes H-bonds hence the consolidated native domain structure, the net force increase disappeared but the fluctuations persisted. Thus, whereas net force generation is caused by the ensemble folding of the elastically-coupled domains, force fluctuations arise due to a dynamic equilibrium between unfolded and molten-globule states. Monte-Carlo simulations incorporating a compact molten-globule intermediate in the folding landscape recovered all features of our nanomechanics results. The ensemble molten-globule dynamics delivers significant added contractility that may assist sarcomere mechanics, and it may reduce the dissipative energy loss associated with titin unfolding/refolding during muscle contraction/relaxation cycles.

Mutual A domain interactions in the force sensing protein von Willebrand factor

The von Willebrand factor (VWF) is a glycoprotein in the blood that plays a central role in hemostasis. Among other functions, VWF is responsible for platelet adhesion at sites of injury via its A1 domain. Its adjacent VWF domain A2 exposes a cleavage site under shear to degrade long VWF fibers in order to prevent thrombosis. Recently, it has been shown that VWF A1/A2 interactions inhibit the binding of platelets to VWF domain A1 in a force-dependent manner prior to A2 cleavage. However, whether and how this interaction also takes place in longer VWF fragments as well as the strength of this interaction in the light of typical elongation forces imposed by the shear flow of blood remained elusive. Here, we addressed these questions by using single molecule force spectroscopy (SMFS), Brownian dynamics (BD), and molecular dynamics (MD) simulations. Our SMFS measurements demonstrate that the A2 domain has the ability to bind not only to single A1 domains but also to VWF A1A2 fragments. SMFS experiments of a mutant [A2] domain, containing a disulfide bond which stabilizes the domain against unfolding, enhanced A1 binding. This observation suggests that the mutant adopts a more stable conformation for binding to A1. We found intermolecular A1/A2 interactions to be preferred over intramolecular A1/A2 interactions. Our data are also consistent with the existence of two cooperatively acting binding sites for A2 in the A1 domain. Our SMFS measurements revealed a slip-bond behavior for the A1/A2 interaction and their lifetimes were estimated for forces acting on VWF multimers at physiological shear rates using BD simulations. Complementary fitting of AFM rupture forces in the MD simulation range adequately reproduced the force response of the A1/A2 complex spanning a wide range of loading rates. In conclusion, we here characterized the auto-inhibitory mechanism of the intramolecular A1/A2 bond as a shear dependent safeguard of VWF, which prevents the interaction of VWF with platelets.

The role of intramolecular nonbonded interaction and angle sampling in single-step free energy perturbation

Single-step free energy perturbation (sFEP) has often been proposed as an efficient tool for a quick free energy scan due to its straightforward protocol and the ability to recycle an existing molecular dynamics trajectory for free energy calculations. Although sFEP is expected to fail when the sampling of a system is inefficient, it is often expected to hold for an alchemical transformation between ligands with a moderate difference in their sizes, e.g., transforming a benzene into an ethylbenzene. Yet, exceptions were observed in calculations for anisole and methylaniline, which have similar physical sizes as ethylbenzene. In this study, we show that such exceptions arise from the sampling inefficiency on an unexpected rigid degree of freedom, namely, the bond angle θ. The distributions of θ differ dramatically between two end states of a sFEP calculation, i.e., the conformation of the ligand changes significantly during the alchemical transformation process. Our investigation also reveals the interrelation between the ligand conformation and the intramolecular nonbonded interactions. This knowledge suggests a best combination of the ghost ligand potential and the dual topology setting, which improves the accuracy in a single reference sFEP calculation by bringing down its error from around 5k_BT to k_BT.

Quantitative FRET studies and integrative modeling unravel the structure and dynamics of biomolecular systems

Förster Resonance Energy Transfer (FRET) combined with single-molecule spectroscopy probes macromolecular structure and dynamics and identifies coexisting conformational states. We review recent methodological developments in integrative structural modeling by satisfying spatial restraints on networks of FRET pairs (hybrid-FRET). We discuss procedures to incorporate prior structural knowledge and to obtain optimal distance networks. Finally, a workflow for hybrid-FRET is presented that automates integrative structural modeling and experiment planning to put hybrid-FRET on rails. To test this workflow, we simulate realistic single-molecule experiments and resolve three protein conformers, exchanging at 30μs and 10ms, with accuracies of 1-3Å RMSD versus the target structure. Incorporation of data from other spectroscopies and imaging is also discussed.

New perspectives on the computational characterization of the kinetics of binding-unbinding in drug design: implications for novel therapies

The efficiency and the propensity of a drug to be bound to its target protein have been inseparable concepts for decades now. The correlation between the pharmacological activity and the binding affinity has been the first rule to design and optimize a new drug rationally. However, this argument does not prove to be infallible when the results of in vivo assays have to be confronted. Only recently, we understand that other magnitudes as the kinetic rates of binding and unbinding, or the mean residence time of the complex drug-protein, are equally relevant to draw a more accurate model of the mechanism of action of a drug. It is in this scenario where new computational techniques to simulate the all-atom dynamics of the biomolecular system find its valuable place on the challenge of designing new molecules for more effective and less toxic therapies.

The cellular membrane as a mediator for small molecule interaction with membrane proteins

The cellular membrane constitutes the first element that encounters a wide variety of molecular species to which a cell might be exposed. Hosting a large number of structurally and functionally diverse proteins associated with this key metabolic compartment, the membrane not only directly controls the traffic of various molecules in and out of the cell, it also participates in such diverse and important processes as signal transduction and chemical processing of incoming molecular species. In this article, we present a number of cases where details of interaction of small molecular species such as drugs with the membrane, which are often experimentally inaccessible, have been studied using advanced molecular simulation techniques. We have selected systems in which partitioning of the small molecule with the membrane constitutes a key step for its final biological function, often binding to and interacting with a protein associated with the membrane. These examples demonstrate that membrane partitioning is not only important for the overall distribution of drugs and other small molecules into different compartments of the body, it may also play a key role in determining the efficiency and the mode of interaction of the drug with its target protein. This article is part of a Special Issue entitled: Biosimulations edited by Ilpo Vattulainen and Tomasz Róg.

The Power of Force: Insights into the Protein Folding Process Using Single-Molecule Force Spectroscopy

One of the major challenges in modern biophysics is observing and understanding conformational changes during complex molecular processes, from the fundamental protein folding to the function of molecular machines. Single-molecule techniques have been one of the major driving forces of the huge progress attained in the last few years. Recent advances in resolution of the experimental setups, aided by theoretical developments and molecular dynamics simulations, have revealed a much higher degree of complexity inside these molecular processes than previously reported using traditional ensemble measurements. This review sums up the evolution of these developments and gives an outlook on prospective discoveries.

Molecular basis of RNA guanine-7 methyltransferase (RNMT) activation by RAM

Maturation and translation of mRNA in eukaryotes requires the addition of the 7-methylguanosine cap. In vertebrates, the cap methyltransferase, RNA guanine-7 methyltransferase (RNMT), has an activating subunit, RNMT-Activating Miniprotein (RAM). Here we report the first crystal structure of the human RNMT in complex with the activation domain of RAM. A relatively unstructured and negatively charged RAM binds to a positively charged surface groove on RNMT, distal to the active site. This results in stabilisation of a RNMT lobe structure which co-evolved with RAM and is required for RAM binding. Structure-guided mutagenesis and molecular dynamics simulations reveal that RAM stabilises the structure and positioning of the RNMT lobe and the adjacent α-helix hinge, resulting in optimal positioning of helix A which contacts substrates in the active site. Using biophysical and biochemical approaches, we observe that RAM increases the recruitment of the methyl donor, AdoMet (S-adenosyl methionine), to RNMT. Thus we report the mechanism by which RAM allosterically activates RNMT, allowing it to function as a molecular rheostat for mRNA cap methylation.

Microscopic Characterization of Membrane Transporter Function by In Silico Modeling and Simulation

Membrane transporters mediate one of the most fundamental processes in biology. They are the main gatekeepers controlling active traffic of materials in a highly selective and regulated manner between different cellular compartments demarcated by biological membranes. At the heart of the mechanism of membrane transporters lie protein conformational changes of diverse forms and magnitudes, which closely mediate critical aspects of the transport process, most importantly the coordinated motions of remotely located gating elements and their tight coupling to chemical processes such as binding, unbinding and translocation of transported substrate and cotransported ions, ATP binding and hydrolysis, and other molecular events fueling uphill transport of the cargo. An increasing number of functional studies have established the active participation of lipids and other components of biological membranes in the function of transporters and other membrane proteins, often acting as major signaling and regulating elements. Understanding the mechanistic details of these molecular processes require methods that offer high spatial and temporal resolutions. Computational modeling and simulations technologies empowered by advanced sampling and free energy calculations have reached a sufficiently mature state to become an indispensable component of mechanistic studies of membrane transporters in their natural environment of the membrane. In this article, we provide an overview of a number of major computational protocols and techniques commonly used in membrane transporter modeling and simulation studies. The article also includes practical hints on effective use of these methods, critical perspectives on their strengths and weak points, and examples of their successful applications to membrane transporters, selected from the research performed in our own laboratory.

Elucidation of Lipid Binding Sites on Lung Surfactant Protein A Using X-ray Crystallography, Mutagenesis, and Molecular Dynamics Simulations

Surfactant protein A (SP-A) is a collagenous C-type lectin (collectin) that is critical for pulmonary defense against inhaled microorganisms. Bifunctional avidity of SP-A for pathogen-associated molecular patterns (PAMPs) such as lipid A and for dipalmitoylphosphatidylcholine (DPPC), the major component of surfactant membranes lining the air-liquid interface of the lung, ensures that the protein is poised for first-line interactions with inhaled pathogens. To improve our understanding of the motifs that are required for interactions with microbes and surfactant structures, we explored the role of the tyrosine-rich binding surface on the carbohydrate recognition domain of SP-A in the interaction with DPPC and lipid A using crystallography, site-directed mutagenesis, and molecular dynamics simulations. Critical binding features for DPPC binding include a three-walled tyrosine cage that binds the choline headgroup through cation-π interactions and a positively charged cluster that binds the phosphoryl group. This basic cluster is also critical for binding of lipid A, a bacterial PAMP and target for SP-A. Molecular dynamics simulations further predict that SP-A binds lipid A more tightly than DPPC. These results suggest that the differential binding properties of SP-A favor transfer of the protein from surfactant DPPC to pathogen membranes containing appropriate lipid PAMPs to effect key host defense functions.

Spatial Heat Maps from Fast Information Matching of Fast and Slow Degrees of Freedom: Application to Molecular Dynamics Simulations

We introduce a fast information matching (FIM) method for transforming time domain data into spatial images through handshaking between fast and slow degrees of freedom. The analytics takes advantage of the detailed time series available from biomolecular computer simulations, and it yields spatial heat maps that can be visualized on 3D molecular structures or in the form of interaction networks. The speed of our efficient mutual information solver is on the order of a basic Pearson cross-correlation calculation. We demonstrate that the FIM method is superior to linear cross-correlation for the detection of nonlinear dependence in challenging situations where measures for the global dynamics (the "activity") diverge. The analytics is applied to the detection of hinge-bending hot spots and to the prediction of pairwise contacts between residues that are relevant for the global activity exhibited by the molecular dynamics (MD) trajectories. Application examples from various MD laboratories include the millisecond bovine pancreatic trypsin inhibitor (BPTI) trajectory using canonical MD, a Gaussian accelerated MD folding trajectory of chignolin, and the heat-induced unfolding of engrailed homeodomain (EnHD). The FIM implementation will be freely disseminated with our open-source package, TimeScapes.

The structure of misfolded amyloidogenic dimers: computational analysis of force spectroscopy data

Progress in understanding the molecular mechanism of self-assembly of amyloidogenic proteins and peptides requires knowledge about their structure in misfolded states. Structural studies of amyloid aggregates formed during the early aggregation stage are very limited. Atomic force microscopy (AFM) spectroscopy is widely used to analyze misfolded proteins and peptides, but the structural characterization of transiently formed misfolded dimers is limited by the lack of computational approaches that allow direct comparison with AFM experiments. Steered molecular dynamics (SMD) simulation is capable of modeling force spectroscopy experiments, but the modeling requires pulling rates 10(7) times higher than those used in AFM experiments. In this study, we describe a computational all-atom Monte Carlo pulling (MCP) approach that enables us to model results at pulling rates comparable to those used in AFM pulling experiments. We tested the approach by modeling pulling experimental data for I91 from titin I-band (PDB ID: 1TIT) and ubiquitin (PDB ID: 1UBQ). We then used MCP to analyze AFM spectroscopy experiments that probed the interaction of the peptides [Q6C] Sup35 (6-13) and [H13C] Aβ (13-23). A comparison of experimental results with the computational data for the Sup35 dimer with out-of-register and in-register arrangements of β-sheets suggests that Sup35 monomers adopt an out-of-register arrangement in the dimer. A similar analysis performed for Aβ peptide demonstrates that the out-of-register antiparallel β-sheet arrangement of monomers also occurs in this peptide. Although the rupture of hydrogen bonds is the major contributor to dimer dissociation, the aromatic-aromatic interaction also contributes to the dimer rupture process.

Understanding and Designing the Gold-Bio Interface: Insights from Simulations

Gold nanoparticles (AuNPs) are an integral part of many exciting and novel biomedical applications, sparking the urgent need for a thorough understanding of the physicochemical interactions occurring between these inorganic materials, their functional layers, and the biological species they interact with. Computational approaches are instrumental in providing the necessary molecular insight into the structural and dynamic behavior of the Au-bio interface with spatial and temporal resolutions not yet achievable in the laboratory, and are able to facilitate a rational approach to AuNP design for specific applications. A perspective of the current successes and challenges associated with the multiscale computational treatment of Au-bio interfacial systems, from electronic structure calculations to force field methods, is provided to illustrate the links between different approaches and their relationship to experiment and applications.

A hierarchical Bayesian framework for force field selection in molecular dynamics simulations

We present a hierarchical Bayesian framework for the selection of force fields in molecular dynamics (MD) simulations. The framework associates the variability of the optimal parameters of the MD potentials under different environmental conditions with the corresponding variability in experimental data. The high computational cost associated with the hierarchical Bayesian framework is reduced by orders of magnitude through a parallelized Transitional Markov Chain Monte Carlo method combined with the Laplace Asymptotic Approximation. The suitability of the hierarchical approach is demonstrated by performing MD simulations with prescribed parameters to obtain data for transport coefficients under different conditions, which are then used to infer and evaluate the parameters of the MD model. We demonstrate the selection of MD models based on experimental data and verify that the hierarchical model can accurately quantify the uncertainty across experiments; improve the posterior probability density function estimation of the parameters, thus, improve predictions on future experiments; identify the most plausible force field to describe the underlying structure of a given dataset. The framework and associated software are applicable to a wide range of nanoscale simulations associated with experimental data with a hierarchical structure.

The good, the bad and the user in soft matter simulations

Molecular dynamics (MD) simulations have become popular in materials science, biochemistry, biophysics and several other fields. Improvements in computational resources, in quality of force field parameters and algorithms have yielded significant improvements in performance and reliability. On the other hand, no method of research is error free. In this review, we discuss a few examples of errors and artifacts due to various sources and discuss how to avoid them. Besides bringing attention to artifacts and proper practices in simulations, we also aim to provide the reader with a starting point to explore these issues further. In particular, we hope that the discussion encourages researchers to check software, parameters, protocols and, most importantly, their own practices in order to minimize the possibility of errors. The focus here is on practical issues. This article is part of a Special Issue entitled: Biosimulations edited by Ilpo Vattulainen and Tomasz Róg.