Are current atomistic force fields accurate enough to study proteins in crowded environments?

Modeling Crowded Environment in Molecular Simulations

Biomolecules perform their various functions in living cells, namely in an environment that is crowded by many macromolecules. Thus, simulating the dynamics and interactions of biomolecules should take into account not only water and ions but also other binding partners, metabolites, lipids and macromolecules found in cells. In the last decade, research on how to model macromolecular crowders around proteins in order to simulate their dynamics in models of cellular environments has gained a lot of attention. In this mini-review we focus on the models of crowding agents that have been used in computer modeling studies of proteins and peptides, especially via molecular dynamics simulations.

Recent Advances in Coarse-Grained Models for Biomolecules and Their Applications

Molecular dynamics simulations have emerged as a powerful tool to study biological systems at varied length and timescales. The conventional all-atom molecular dynamics simulations are being used by the wider scientific community in routine to capture the conformational dynamics and local motions. In addition, recent developments in coarse-grained models have opened the way to study the macromolecular complexes for time scales up to milliseconds. In this review, we have discussed the principle, applicability and recent development in coarse-grained models for biological systems. The potential of coarse-grained simulation has been reviewed through state-of-the-art examples of protein folding and structure prediction, self-assembly of complexes, membrane systems and carbohydrates fiber models. The multiscale simulation approaches have also been discussed in the context of their emerging role in unravelling hierarchical level information of biosystems. We conclude this review with the future scope of coarse-grained simulations as a constantly evolving tool to capture the dynamics of biosystems.

Modified Potential Functions Result in Enhanced Predictions of a Protein Complex by All-Atom Molecular Dynamics Simulations, Confirming a Stepwise Association Process for Native Protein-Protein Interactions

The relative prevalence of native protein-protein interactions (PPIs) are the cornerstone for understanding the structure, dynamics and mechanisms of function of protein complexes. In this study, we develop a scheme for scaling the protein-water interaction in the CHARMM36 force field, in order to better fit the solvation free energy of amino acids side-chain analogues. We find that the molecular dynamics simulation with the scaled force field, CHARMM36s, as well as a recently released version, CHARMM36m, effectively improve on the overly sticky association of proteins, such as ubiquitin. We investigate the formation of a heterodimer protein complex between the SAM domains of the EphA2 receptor and the SHIP2 enzyme by performing a combined total of 48 μs simulations with the different potential functions. While the native SAM heterodimer is only predicted at a low rate of 6.7% with the original CHARMM36 force field, the yield is increased to 16.7% with CHARMM36s, and to 18.3% with CHARMM36m. By analyzing the 25 native SAM complexes formed in the simulations, we find that their formation involves a preorientation guided by Coulomb interactions, consistent with an electrostatic steering mechanism. In 12 cases, the complex could directly transform to the native protein interaction surfaces with only small adjustments in domain orientation. In the other 13 cases, orientational and/or translational adjustments are needed to reach the native complex. Although the tendency for non-native complexes to dissociate has nearly doubled with the modified potential functions, a dissociation followed by a reassociation to the correct complex structure is still rare. Instead, the remaining non-native complexes undergo configurational changes/surface searching, which, however, rarely leads to native structures on a time scale of 250 ns. These observations provide a rich picture of the mechanisms of protein-protein complex formation and suggest that computational predictions of native complex PPIs could be improved further.

Hydration dynamics of proteins in reverse micelles probed by 1H-NOESY/1H-ROESY NMR and 17O-nuclear quadrupole resonance (NQR)

This study is based on extensive MD simulations of a protein in a reverse micelle to mimic the effect of confinement on biomolecules. This permits the calculation of measurable quantities appearing in NQR and Nuclear Overhauser-NMR despite the high computational effort. We address the long-standing debate about the intermolecular NOE showing that absolute quantities derived from NOESY and ROESY spectra do indeed contain considerable long-range contributions, while ratios thereof are effectively short-ranged due to almost perfect compensation effects. Based on NQR relaxation times, we predict strong rotational retardation of interstitial water between the protein and the surfactant surface. The computed NOE to ROE ratio correlates fairly with experimental results. The solvation dynamics mapped onto the protein surface reflects the spatial heterogeneity of a cell-like system with slow water dynamics in proximity to the cell wall and almost bulk-like behaviour in the water core.

Emerging Diversity in Lipid-Protein Interactions

Membrane lipids interact with proteins in a variety of ways, ranging from providing a stable membrane environment for proteins to being embedded in to detailed roles in complicated and well-regulated protein functions. Experimental and computational advances are converging in a rapidly expanding research area of lipid-protein interactions. Experimentally, the database of high-resolution membrane protein structures is growing, as are capabilities to identify the complex lipid composition of different membranes, to probe the challenging time and length scales of lipid-protein interactions, and to link lipid-protein interactions to protein function in a variety of proteins. Computationally, more accurate membrane models and more powerful computers now enable a detailed look at lipid-protein interactions and increasing overlap with experimental observations for validation and joint interpretation of simulation and experiment. Here we review papers that use computational approaches to study detailed lipid-protein interactions, together with brief experimental and physiological contexts, aiming at comprehensive coverage of simulation papers in the last five years. Overall, a complex picture of lipid-protein interactions emerges, through a range of mechanisms including modulation of the physical properties of the lipid environment, detailed chemical interactions between lipids and proteins, and key functional roles of very specific lipids binding to well-defined binding sites on proteins. Computationally, despite important limitations, molecular dynamics simulations with current computer power and theoretical models are now in an excellent position to answer detailed questions about lipid-protein interactions.

Evolution of All-Atom Protein Force Fields to Improve Local and Global Properties

Experimental studies on intrinsically disordered and unfolded proteins have shown that in isolation they typically have low populations of secondary structure and exhibit distance scalings suggesting that they are at near-theta-solvent conditions. Until recently, however, all-atom force fields failed to reproduce these fundamental properties of intrinsically disordered proteins (IDPs). Recent improvements by refining against ensemble-averaged experimental observables for polypeptides in aqueous solution have addressed deficiencies including secondary structure bias, global conformational properties, and thermodynamic parameters of biophysical reactions such as folding and collapse. To date, studies utilizing these improved all-atom force fields have mostly been limited to a small set of unfolded or disordered proteins. Here, we present data generated for a diverse library of unfolded or disordered proteins using three progressively improved generations of Amber03 force fields, and we explore how global and local properties are affected by each successive change in the force field. We find that the most recent force field refinements significantly improve the agreement of the global properties such as radii of gyration and end-to-end distances with experimental estimates. However, these global properties are largely independent of the local secondary structure propensity. This result stresses the need to validate force fields with reference to a combination of experimental data providing information about both local and global structure formation.

Atomistic Force Fields for Proteins

All-atom, classical force fields for protein molecular dynamics (MD) simulations currently occupy a sweet spot in the universe of computational models, sufficiently detailed to be of predictive value in many cases, yet also simple enough that some biologically relevant time scales (microseconds or more) can now be sampled via specialized hardware or enhanced sampling methods. However, due to their long evolutionary history, there is now a myriad of force field branches in current use, which can make it hard for those entering the simulation field to know which would be the best set of parameters for a given application. In this chapter, I try to give an overview of the historical motivation for the different force fields available, suggestions for how to determine the most appropriate model and what to do if the results are in conflict with experimental evidence.

Towards capturing cellular complexity: combining encapsulation and macromolecular crowding in a reverse micelle

This paper studies the orientational structure and dynamics of multi-protein systems under confinement and discusses the implications on biological cells.

Changes in protein hydration dynamics by encapsulation or crowding of ubiquitin: strong correlation between time-dependent Stokes shift and intermolecular nuclear Overhauser effect

The local changes in protein hydration dynamics upon encapsulation of the protein or macromolecular crowding are essential to understand protein function in cellular environments. We were able to obtain a spatially-resolved picture of the influence of confinement and crowding on the hydration dynamics of the protein ubiquitin by analyzing the time-dependent Stokes shift (TDSS), as well as the intermolecular Nuclear Overhauser Effect (NOE) at different sites of the protein by large-scale computer simulation of single and multiple proteins in water and confined in reverse micelles. Besides high advanced space resolved information on hydration dynamics we found a strong correlation of the change in NOE upon crowding or encapsulation and the change in the integral TDSS relaxation times in all investigated systems relative to the signals in a diluted protein solution.

Changes in local protein hydration dynamics caused by encapsulation or crowding are reflected in the TDSS and the intermolecular NOE alike.

Molecular Determinants of Aβ42 Adsorption to Amyloid Fibril Surfaces

The long lag times and subsequent rapid growth of Alzheimer's Aβ₄₂ fibrils can be explained by a secondary nucleation step, in which existing fibril surfaces are able to nucleate the formation of new fibrils via an autocatalytic process. The molecular mechanism of secondary nucleation, however, is still unknown. Here we investigate the first step, namely, adsorption of the Aβ₄₂ peptide monomers onto the fibril surface. Using long all-atom molecular simulations and an enhanced sampling scheme, we are able to generate a diverse ensemble of binding events. The resulting thermodynamics of adsorption are consistent with experiment as well as with the requirements for effective autocatalysis determined from coarse-grained simulations. We identify the key interactions stabilizing the adsorbed state, which are predominantly polar in nature, and relate them to the effects of known disease-causing mutations.

On the Applicability of Force Fields To Study the Aggregation of Amyloidogenic Peptides Using Molecular Dynamics Simulations

Molecular dynamics simulations play an essential role in understanding biomolecular processes such as protein aggregation at temporal and spatial resolutions which are not attainable by experimental methods. For a correct modeling of protein aggregation, force fields must accurately represent molecular interactions. Here, we study the effect of five different force fields on the oligomer formation of Alzheimer's Aβ_16-22 peptide and two of its mutants: Aβ_16-22(F19V,F20V), which does not form fibrils, and Aβ_16-22(F19L) which forms fibrils faster than the wild type. We observe that while oligomer formation kinetics depends strongly on the force field, structural properties, such as the most relevant protein-protein contacts, are similar between them. The oligomer formation kinetics obtained with different force fields differ more from each other than the kinetics between aggregating and nonaggregating peptides simulated with a single force field. We discuss the difficulties in comparing atomistic simulations of amyloid oligomer formation with experimental observables.

Role of the Interaction Motif in Maintaining the Open Gate of an Open Sodium Channel

Voltage-gated sodium channels undergo transitions between open, closed, and inactivated states, enabling regulation of the translocation of sodium ions across membranes. A recently published crystal structure of the full-length prokaryotic NavMs crystal structure in the activated open conformation has revealed the presence of a novel motif consisting of an extensive network of salt bridges involving residues in the voltage sensor, S4-S5 linker, pore, and C-terminal domains. This motif has been proposed to be responsible for maintaining an open conformation that enables ion translocation through the channel. In this study, we have used long-time molecular dynamics calculations without artificial restraints to demonstrate that the interaction network of full-length NavMs indeed prevents a rapid collapse and closure of the gate, in marked difference to earlier studies of the pore-only construct in which the gate had to be restrained to remain open. Interestingly, a frequently discussed "hydrophobic gating" mechanism at nanoscopic level is also observed in our simulations, in which the discontinuous water wire close to the gate region leads to an energetic barrier for ion conduction. In addition, we demonstrate the effects of in silico mutations of several of the key residues in the motif on the open channel's stability and functioning, correlating them with existing functional studies on this channel and homologous disease-associated mutations in human sodium channels; we also examine the effects of truncating/removing the voltage sensor and C-terminal domains in maintaining an open gate.

Dynamics and Thermodynamics of Transthyretin Association from Molecular Dynamics Simulations

Molecular dynamics simulations are used in this work to probe the structural stability and the dynamics of engineered mutants of transthyretin (TTR), i.e., the double mutant F87M/L110M (MT-TTR) and the triple mutant F87M/L110M/S117E (3M-TTR), in relation to wild-type. Free energy analysis from end-point simulations and statistical effective energy functions are used to analyze trajectories, revealing that mutations do not have major impact on protein structure but rather on protein association, shifting the equilibria towards dissociated species. The result is confirmed by the analysis of 3M-TTR which shows dissociation within the first 10 ns of the simulation, indicating that contacts are lost at the dimer-dimer interface, whereas dimers (formed by monomers which pair to form two extended β-sheets) appear fairly stable. Overall the simulations provide a detailed view of the dynamics and thermodynamics of wild-type and mutant transthyretins and a rationale of the observed effects.

Recent advances in automated protein design and its future challenges

INTRODUCTION

Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered: The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion: Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.

New tricks for old dogs: improving the accuracy of biomolecular force fields by pair-specific corrections to non-bonded interactions

In contrast to ordinary polymers, the vast majority of biological macromolecules adopt highly ordered three-dimensional structures that define their functions. The key to folding of a biopolymer into a unique 3D structure or to an assembly of several biopolymers into a functional unit is a delicate balance between the attractive and repulsive forces that also makes such self-assembly reversible under physiological conditions. The all-atom molecular dynamics (MD) method has emerged as a powerful tool for studies of individual biomolecules and their functional assemblies, encompassing systems of ever increasing complexity. However, advances in parallel computing technology have outpaced the development of the underlying theoretical models-the molecular force fields, pushing the MD method into an untested territory. Recent tests of the MD method have found the most commonly used molecular force fields to be out of balance, overestimating attractive interactions between charged and hydrophobic groups, which can promote artificial aggregation in MD simulations of multi-component protein, nucleic acid, and lipid systems. One route towards improving the force fields is through the NBFIX corrections method, in which the intermolecular forces are calibrated against experimentally measured quantities such as osmotic pressure by making atom pair-specific adjustments to the non-bonded interactions. In this article, we review development of the NBFIX (Non-Bonded FIX) corrections to the AMBER and CHARMM force fields and discuss their implications for MD simulations of electrolyte solutions, dense DNA systems, Holliday junctions, protein folding, and lipid bilayer membranes.

Identifying Conformational-Selection and Induced-Fit Aspects in the Binding-Induced Folding of PMI from Markov State Modeling of Atomistic Simulations

Unstructured proteins and peptides typically fold during binding to ligand proteins. A challenging problem is to identify the mechanism and kinetics of these binding-induced folding processes in experiments and atomistic simulations. In this Article, we present a detailed picture for the folding of the inhibitor peptide PMI into a helix during binding to the oncoprotein fragment ^25-109Mdm2 obtained from atomistic, explicit-water simulations and Markov state modeling. We find that binding-induced folding of PMI is highly parallel and can occur along a multitude of pathways. Some pathways are induced-fit-like with binding occurring prior to PMI helix formation, while other pathways are conformational-selection-like with binding after helix formation. On the majority of pathways, however, binding is intricately coupled to folding, without clear temporal ordering. A central feature of these pathways is PMI motion on the Mdm2 surface, along the binding groove of Mdm2 or over the rim of this groove. The native binding groove of Mdm2 thus appears as an asymmetric funnel for PMI binding. Overall, binding-induced folding of PMI does not fit into the classical picture of induced fit or conformational selection that implies a clear temporal ordering of binding and folding events. We argue that this holds in general for binding-induced folding processes because binding and folding events in these processes likely occur on similar time scales and do exhibit the time-scale separation required for temporal ordering.

Revival of collective water structure and dynamics in reverse micelles brought about by protein encapsulation

A novel mechanism of depolarization in reverse micelles with zwitterionic surfactants and containing polar species but lacking ions is reported.

Macromolecular crowding and the importance of proper hydration for the structure and dynamics of protein solutions

Extensive computational studies of ubiquitin crowding with a special focus on protein hydration directly visible in dielectric spectra.

A molecular dynamics model for glycosylphosphatidyl-inositol anchors: “flop down” or “lollipop”?

Computational model for GPI anchors tested in DMPC and POPC bilayers. The free anchor rarely occurs as an erected “lollipop-like” conformation, it rather “flops down” onto the bilayer surface. Yet an attached protein (here green fluorescent protein) exhibits extensive orientational flexibility due to the phospho-ethanolamine linker.

Effects of force fields on the conformational and dynamic properties of amyloid β(1-40) dimer explored by replica exchange molecular dynamics simulations

The conformational space and structural ensembles of amyloid beta (Aβ) peptides and their oligomers in solution are inherently disordered and proven to be challenging to study. Optimum force field selection for molecular dynamics (MD) simulations and the biophysical relevance of results are still unknown. We compared the conformational space of the Aβ(1-40) dimers by 300 ns replica exchange MD simulations at physiological temperature (310 K) using: the AMBER-ff99sb-ILDN, AMBER-ff99sb*-ILDN, AMBER-ff99sb-NMR, and CHARMM22* force fields. Statistical comparisons of simulation results to experimental data and previously published simulations utilizing the CHARMM22* and CHARMM36 force fields were performed. All force fields yield sampled ensembles of conformations with collision cross sectional areas for the dimer that are statistically significantly larger than experimental results. All force fields, with the exception of AMBER-ff99sb-ILDN (8.8 ± 6.4%) and CHARMM36 (2.7 ± 4.2%), tend to overestimate the α-helical content compared to experimental CD (5.3 ± 5.2%). Using the AMBER-ff99sb-NMR force field resulted in the greatest degree of variance (41.3 ± 12.9%). Except for the AMBER-ff99sb-NMR force field, the others tended to under estimate the expected amount of β-sheet and over estimate the amount of turn/bend/random coil conformations. All force fields, with the exception AMBER-ff99sb-NMR, reproduce a theoretically expected β-sheet-turn-β-sheet conformational motif, however, only the CHARMM22* and CHARMM36 force fields yield results compatible with collapse of the central and C-terminal hydrophobic cores from residues 17-21 and 30-36. Although analyses of essential subspace sampling showed only minor variations between force fields, secondary structures of lowest energy conformers are different.

Slow-Down in Diffusion in Crowded Protein Solutions Correlates with Transient Cluster Formation

For a long time, the effect of a crowded cellular environment on protein dynamics has been largely ignored. Recent experiments indicate that proteins diffuse more slowly in a living cell than in a diluted solution, and further studies suggest that the diffusion depends on the local surroundings. Here, detailed insight into how diffusion depends on protein-protein contacts is presented based on extensive all-atom molecular dynamics simulations of concentrated villin headpiece solutions. After force field adjustments in the form of increased protein-water interactions to reproduce experimental data, translational and rotational diffusion was analyzed in detail. Although internal protein dynamics remained largely unaltered, rotational diffusion was found to slow down more significantly than translational diffusion as the protein concentration increased. The decrease in diffusion is interpreted in terms of a transient formation of protein clusters. These clusters persist on sub-microsecond time scales and follow distributions that increasingly shift toward larger cluster size with increasing protein concentrations. Weighting diffusion coefficients estimated for different clusters extracted from the simulations with the distribution of clusters largely reproduces the overall observed diffusion rates, suggesting that transient cluster formation is a primary cause for a slow-down in diffusion upon crowding with other proteins.

Ion-induced modification of the sucrose network and its impact on melting of freeze-dried liposomes. DSC and molecular dynamics study

Disaccharides play an important role in survival of anhydrobiotic organisms during extreme environmental conditions. A key protection feature is their capability to form the hydrogen bond (HB) network in a similar fashion as the one made by water. Since various ions also affect the HB network in completely hydrated systems, it is of a great interest to understand how they impact preservation when incorporated in a disaccharide network. To address this, we employ a combination of experimental and modeling techniques to study behavior of multilamellar 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) liposomes freeze-dried with sucrose in presence of NaCl or NaH₂PO₄·H₂O at various concentrations (0.01-1M). Differential scanning calorimetry (DSC) was employed in order to determine the cooperative unit size (CUS), the number of lipid molecules that constitute a domain of cooperative motion in the liposome, and the melting temperature (T_m). In the absence of salt CUS was estimated to be 122±12, whereas in the presence of NaCl CUS increases more (347±34 for c=1M) than for NaH₂PO₄·H₂O (193±26 for 1M). When it comes to T_m, the situation is reversed; NaCl induces increase by about 1K, while NaH₂PO₄·H₂O by about 10K. These findings clearly demonstrate how different interaction forces-hydrogen bonding, charge pairing, and van der Waals interactions between acyl chains-affect CUS and T_m. Their interplay and contribution of particular interaction was further analyzed with molecular dynamics (MD) simulations. This analysis demonstrated that the HB network of DMPC and sucrose is partially disrupted in the presence of NaCl ions, and even to a greater extent in the case of NaH₂PO₄·H₂O ions. Notably, H₂PO₄^- ions outcompete and replace the sucrose molecules at the DMPC surface, which in turn alters the nature of the DMPC-surrounding interactions, from a weaker HB-dominated to a stronger CP-dominated interaction network.

Enhanced unbiased sampling of protein dynamics using evolutionary coupling information

One of the major challenges in atomistic simulations of proteins is efficient sampling of pathways associated with rare conformational transitions. Recent developments in statistical methods for computation of direct evolutionary couplings between amino acids within and across polypeptide chains have allowed for inference of native residue contacts, informing accurate prediction of protein folds and multimeric structures. In this study, we assess the use of distances between evolutionarily coupled residues as natural choices for reaction coordinates which can be incorporated into Markov state model-based adaptive sampling schemes and potentially used to predict not only functional conformations but also pathways of conformational change, protein folding, and protein-protein association. We demonstrate the utility of evolutionary couplings in sampling and predicting activation pathways of the β 2-adrenergic receptor (β 2-AR), folding of the FiP35 WW domain, and dimerization of the E. coli molybdopterin synthase subunits. We find that the time required for β 2-AR activation and folding of the WW domain are greatly diminished using evolutionary couplings-guided adaptive sampling. Additionally, we were able to identify putative molybdopterin synthase association pathways and near-crystal structure complexes from protein-protein association simulations.

Crowding in Cellular Environments at an Atomistic Level from Computer Simulations

The effects of crowding in biological environments on biomolecular structure, dynamics, and function remain not well understood. Computer simulations of atomistic models of concentrated peptide and protein systems at different levels of complexity are beginning to provide new insights. Crowding, weak interactions with other macromolecules and metabolites, and altered solvent properties within cellular environments appear to remodel the energy landscape of peptides and proteins in significant ways including the possibility of native state destabilization. Crowding is also seen to affect dynamic properties, both conformational dynamics and diffusional properties of macromolecules. Recent simulations that address these questions are reviewed here and discussed in the context of relevant experiments.

Specific DNA sequences allosterically enhance protein-protein interaction in a transcription factor through modulation of protein dynamics: implications for specificity of gene regulation

Most genes are regulated by multiple transcription factors, often assembling into multi-protein complexes in the gene regulatory region. Understanding of the molecular origin of specificity of gene regulatory complex formation in the context of the whole genome is currently inadequate. A phage transcription factor λ-CI forms repressive multi-protein complexes by binding to multiple binding sites in the genome to regulate the lifecycle of the phage. The protein-protein interaction between two DNA-bound λ-CI molecules is stronger when they are bound to the correct pair of binding sites, suggesting allosteric transmission of recognition of correct DNA sequences to the protein-protein interaction interface. Exploration of conformation and dynamics by time-resolved fluorescence anisotropy decay and molecular dynamics suggests a change in protein dynamics to be a crucial factor in mediating allostery. A lattice-based model suggests that DNA-sequence induced allosteric effects could be crucial underlying factors in differentially stabilizing the correct site-specific gene regulatory complexes. We conclude that transcription factors have evolved multiple mechanisms to augment the specificity of DNA-protein interactions in order to achieve an extraordinarily high degree of spatial and temporal specificities of gene regulatory complexes, and DNA-sequence induced allostery plays an important role in the formation of sequence-specific gene regulatory complexes.

Reparametrization of Protein Force Field Nonbonded Interactions Guided by Osmotic Coefficient Measurements from Molecular Dynamics Simulations

There is a small, but growing, body of literature describing the use of osmotic coefficient measurements to validate and reparametrize simulation force fields. Here we have investigated the ability of five very commonly used force field and water model combinations to reproduce the osmotic coefficients of seven neutral amino acids and five small molecules. The force fields tested include AMBER ff99SB-ILDN, CHARMM36, GROMOS54a7, and OPLS-AA, with the first of these tested in conjunction with the TIP3P and TIP4P-Ew water models. In general, for both the amino acids and the small molecules, the tested force fields produce computed osmotic coefficients that are lower than experiment; this is indicative of excessively favorable solute-solute interactions. The sole exception to this general trend is provided by GROMOS54a7 when applied to amino acids: in this case, the computed osmotic coefficients are consistently too high. Importantly, we show that all of the force fields tested can be made to accurately reproduce the experimental osmotic coefficients of the amino acids when minor modifications-some previously reported by others and some that are new to this study-are made to the van der Waals interactions of the charged terminal groups. Special care is required, however, when simulating Proline with a number of the force fields, and a hydroxyl-group specific modification is required in order to correct Serine and Threonine when simulated with AMBER ff99SB-ILDN. Interestingly, an alternative parametrization of the van der Waals interactions in the latter force field, proposed by the Nerenberg and Head-Gordon groups, is shown to immediately produce osmotic coefficients that are in excellent agreement with experiment. Overall, this study reinforces the idea that osmotic coefficient measurements can be used to identify general shortcomings in commonly used force fields' descriptions of solute-solute interactions and further demonstrates that modifications to van der Waals parameters provide a simple route to optimizing agreement with experiment.

Transient helicity in intrinsically disordered Axin-1 studied by NMR spectroscopy and molecular dynamics simulations

Many natural proteins are, as a whole or in part, intrinsically disordered. Frequently, such intrinsically disordered regions (IDRs) undergo a transition to a defined and often helical conformation upon binding to partner molecules. The intrinsic propensity of an IDR sequence to fold into a helical conformation already in the absence of a binding partner can have a decisive influence on the binding process and affinity. Using a combination of NMR spectroscopy and molecular dynamics (MD) simulations we have investigated the tendency of regions of Axin-1, an intrinsically disordered scaffolding protein of the WNT signaling pathway, to form helices in segments interacting with binding partners. Secondary chemical shifts from NMR measurements show an increased helical population in these regions. Systematic application of MD advanced sampling approaches on peptide segments of Axin-1 reproduces the experimentally observed tendency and allows insights into the distribution of segment conformations and free energies of helix formation. The results, however, were found to dependent on the force field water model. Recent water models specifically designed for IDRs significantly reduce the predicted helical content and do not improve the agreement with experiment.

Discrete Molecular Dynamics Approach to the Study of Disordered and Aggregating Proteins

We present a refinement of the Coarse Grained PACSAB force field for Discrete Molecular Dynamics (DMD) simulations of proteins in aqueous conditions. As the original version, the refined method provides good representation of the structure and dynamics of folded proteins but provides much better representations of a variety of unfolded proteins, including some very large, impossible to analyze by atomistic simulation methods. The PACSAB/DMD method also reproduces accurately aggregation properties, providing good pictures of the structural ensembles of proteins showing a folded core and an intrinsically disordered region. The combination of accuracy and speed makes the method presented here a good alternative for the exploration of unstructured protein systems.

Evaluation of the Coupled Two-Dimensional Main Chain Torsional Potential in Modeling Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) carry out crucial biological functions in essential biological processes of life. Because of the highly dynamic and conformationally heterogeneous nature of the disordered states of IDPs, molecular dynamics simulations are becoming an indispensable tool for the investigation of the conformational ensembles and dynamic properties of IDPs. Nevertheless, there is still no consensus on the most reliable force field in molecular dynamics simulations for IDPs hitherto. In this work, the recently proposed AMBER99SB^2D force field is evaluated in modeling some disordered polypeptides and proteins by checking its ability to reproduce experimental NMR data. The results highlight that when the ildn side-chain corrections are included, AMBER99SB^2D-ildn exhibits reliable results that agree with experiments compared with its predecessors, the AMBER14SB, AMBER99SB, AMBER99SB-ildn, and AMBER99SB^2D force fields, and that decreasing the overall magnitude of protein-protein interactions in favor of protein-water interactions is a key ingredient behind the improvement.

Distribution of dopant ions around poly(3,4-ethylenedioxythiophene) chains: a theoretical study

The effect of counterions and multiple polymer chains on the properties and structure of poly(3,4-ethylenedioxythiophene) (PEDOT) doped with ClO₄⁻ has been examined using density functional theory (DFT) calculations with periodic boundary conditions (PBCs).

Simulations of disordered proteins and systems with conformational heterogeneity

Intrinsically disordered proteins (IDPs) and protein regions can facilitate a wide variety of complex physiological processes such as binding, signaling, and formation of membraneless organelles. They can however also play pathological roles by aggregating into cytotoxic oligomers and fibrils. Characterizing the structure and function of disordered proteins is an onerous task, primarily because these proteins adopt transient structures, which are difficult to capture in experiments. Simulations have emerged as a powerful tool for interpreting and augmenting experimental measurements of IDPs. In this review we focus on computer simulations of disordered protein structures, functions, assemblies, and emerging questions that, taken together, give an overview of the field as it exists today.

Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterial cytoplasm

Biological macromolecules function in highly crowded cellular environments. The structure and dynamics of proteins and nucleic acids are well characterized in vitro, but in vivo crowding effects remain unclear. Using molecular dynamics simulations of a comprehensive atomistic model cytoplasm we found that protein-protein interactions may destabilize native protein structures, whereas metabolite interactions may induce more compact states due to electrostatic screening. Protein-protein interactions also resulted in significant variations in reduced macromolecular diffusion under crowded conditions, while metabolites exhibited significant two-dimensional surface diffusion and altered protein-ligand binding that may reduce the effective concentration of metabolites and ligands in vivo. Metabolic enzymes showed weak non-specific association in cellular environments attributed to solvation and entropic effects. These effects are expected to have broad implications for the in vivo functioning of biomolecules. This work is a first step towards physically realistic in silico whole-cell models that connect molecular with cellular biology.

A multi-resolution model to capture both global fluctuations of an enzyme and molecular recognition in the ligand-binding site

In multi-resolution simulations, different system components are simultaneously modeled at different levels of resolution, these being smoothly coupled together. In the case of enzyme systems, computationally expensive atomistic detail is needed in the active site to capture the chemistry of ligand binding. Global properties of the rest of the protein also play an essential role, determining the structure and fluctuations of the binding site; however, these can be modeled on a coarser level. Similarly, in the most computationally efficient scheme only the solvent hydrating the active site requires atomistic detail. We present a methodology to couple atomistic and coarse-grained protein models, while solvating the atomistic part of the protein in atomistic water. This allows a free choice of which protein and solvent degrees of freedom to include atomistically. This multi-resolution methodology can successfully model stable ligand binding, and we further confirm its validity by exploring the reproduction of system properties relevant to enzymatic function. In addition to a computational speedup, such an approach can allow the identification of the essential degrees of freedom playing a role in a given process, potentially yielding new insights into biomolecular function. Proteins 2016; 84:1902-1913. © 2016 Wiley Periodicals, Inc.

Temperature-sensitive gating of TRPV1 channel as probed by atomistic simulations of its trans- and juxtamembrane domains

Heat-activated transient receptor potential channel TRPV1 is one of the most studied eukaryotic proteins involved in temperature sensation. Upon heating, it exhibits rapid reversible pore gating, which depolarizes neurons and generates action potentials. Underlying molecular details of such effects in the pore region of TRPV1 is of a crucial importance to control temperature responses of the organism. Despite the spatial structure of the channel in both open (O) and closed (C) states is known, microscopic nature of channel gating and mechanism of thermal sensitivity are still poorly understood. In this work, we used unrestrained atomistic molecular dynamics simulations of TRPV1 (without N- and C-terminal cytoplasmic domains) embedded into explicit lipid bilayer in its O- and C-states. We found that the pore domain with its neighboring loops undergoes large temperature-dependent conformational transitions in an asymmetric way, when fragments of only one monomer move with large amplitude, freeing the pore upon heating. Such an asymmetrical gating looks rather biologically relevant because it is faster and more reliable than traditionally proposed “iris-like” symmetric scheme of channel opening. Analysis of structural, dynamic, and hydrophobic organization of the pore domain revealed entropy growth upon TRPV1 gating, which is in line with current concepts of thermal sensitivity.

Molecular Dynamics Simulations of Intrinsically Disordered Proteins: On the Accuracy of the TIP4P-D Water Model and the Representativeness of Protein Disorder Models

Here, we first present a follow-up to a previous work by our group on the problematic of molecular dynamics simulations of intrinsically disordered proteins (IDPs) [ Henriques et al. J. Chem. Theory Comput. 2015 , 11 , 3420 - 3431 ], using the recently developed TIP4P-D water model. When used in conjunction with the standard AMBER ff99SB-ILDN force field and applied to the simulation of Histatin 5, our IDP model, we obtain results which are in excellent agreement with the best performing IDP-suitable force field from the earlier study and with experiment. We then assess the representativeness of the IDP models used in these and similar studies, finding that most are too short in comparison to the average IDP and contain a bias toward hydrophilic amino acid residues. Moreover, several key order- and disorder-promoting residues are also found to be misrepresented. It seems appropriate for future studies to address these issues.

Osmotic Pressure Simulations of Amino Acids and Peptides Highlight Potential Routes to Protein Force Field Parameterization

Recent molecular dynamics (MD) simulations of proteins have suggested that common force fields overestimate the strength of amino acid interactions in aqueous solution. In an attempt to determine the causes of these effects, we have measured the osmotic coefficients of a number of amino acids using the AMBER ff99SB-ILDN force field with two popular water models, and compared the results with available experimental data. With TIP4P-Ew water, interactions between aliphatic residues agree well with experiment, but interactions of the polar residues serine and threonine are found to be excessively attractive. For all tested amino acids, the osmotic coefficients are lower when the TIP3P water model is used. Additional simulations performed on charged amino acids indicate that the osmotic coefficients are strongly dependent on the parameters assigned to the salt ions, with a reparameterization of the sodium/carboxylate interaction reported by the Aksimentiev group significantly improving description of the osmotic coefficient for glutamate. For five neutral amino acids, we also demonstrate a decrease in solute-solute attractions using the recently reported TIP4P-D water model and using the KBFF force field. Finally, we show that for four two-residue peptides improved agreement with experiment can be achieved by rederiving the partial charges for each peptide.

Prediction and clarification of structures of (bio)molecules on surfaces

The design of future materials for biotechnological applications via deposition of molecules on surfaces will require not only exquisite control of the deposition procedure, but of equal importance will be our ability to predict the shapes and stability of individual molecules on various surfaces. Furthermore, one will need to be able to predict the structure patterns generated during the self-organization of whole layers of (bio)molecules on the surface. In this review, we present an overview over the current state of the art regarding the prediction and clarification of structures of biomolecules on surfaces using theoretical and computational methods.

Advances in the Simulation of Protein Aggregation at the Atomistic Scale

Protein aggregation into highly structured amyloid fibrils is associated with various diseases including Alzheimer's disease, Parkinson's disease, and type II diabetes. Amyloids can also have normal biological functions and, in the future, could be used as the basis for novel nanoscale materials. However, a full understanding of the physicochemical forces that drive protein aggregation is still lacking. Such understanding is crucial for the development of drugs that can effectively inhibit aberrant amyloid aggregation and for the directed design of functional amyloids. Atomistic simulations can help understand protein aggregation. In particular, atomistic simulations can be used to study the initial formation of toxic oligomers which are hard to characterize experimentally and to understand the difference in aggregation behavior between different amyloidogenic peptides. Here, we review the latest atomistic simulations of protein aggregation, concentrating on amyloidogenic protein fragments, and provide an outlook for the future in this field.