Comparative binding of kaempferol and kaempferide on inhibiting the aggregate formation of mutant (G85R) SOD1 protein in familial amyotrophic lateral sclerosis: A quantum chemical and molecular mechanics study

Mutation in Cu/Zn superoxide dismutase (SOD1) at position 85 from glycine to arginine was found to be a prominent cause of aggregation characterized by an increased content of β-sheets in familial amyotrophic lateral sclerosis (fALS). Various literatures reported that natural polyphenols could act as a β-sheet breaker and therefore, treated as a potential therapeutics against various aggregated proteins involved in neurodegenerative disorders. Through computational perspective, molecular docking, quantum chemical studies, and discrete molecular dynamics were implemented to study the binding and structural effect of natural polyphenols, kaempferol, and kaempferide on mutant SOD1. Kaempferol exhibited significant binding and greater residual energy contribution with mutant SOD1 than kaempferide. More interestingly, kaempferol was found to reduce the β-sheet content augmenting the mutant conformational stability and flexibility relative to that of kaempferide. Hence, the inhibition of mutant SOD1 aggregation by kaempferol was explored, thereby suggesting kaempferol could act as a drug candidate for the design of the natural therapeutics against fALS. © 2018 BioFactors, 44(5):431-442, 2018.

β-barrel Oligomers as Common Intermediates of Peptides Self-Assembling into Cross-β Aggregates

Oligomers populated during the early amyloid aggregation process are more toxic than mature fibrils, but pinpointing the exact toxic species among highly dynamic and heterogeneous aggregation intermediates remains a major challenge. β-barrel oligomers, structurally-determined recently for a slow-aggregating peptide derived from αB crystallin, are attractive candidates for exerting amyloid toxicity due to their well-defined structures as therapeutic targets and compatibility to the "amyloid-pore" hypothesis of toxicity. To assess whether β-barrel oligomers are common intermediates to amyloid peptides - a necessary step toward associating β-barrel oligomers with general amyloid cytotoxicity, we computationally studied the oligomerization and fibrillization dynamics of seven well-studied fragments of amyloidogenic proteins with different experimentally-determined aggregation morphologies and cytotoxicity. In our molecular dynamics simulations, β-barrel oligomers were only observed in five peptides self-assembling into the characteristic cross-β aggregates, but not the other two that formed polymorphic β-rich aggregates as reported experimentally. Interestingly, the latter two peptides were previously found nontoxic. Hence, the observed correlation between β-barrel oligomers formation and cytotoxicity supports the hypothesis of β-barrel oligomers as the common toxic intermediates of amyloid aggregation.

Structural complexity and functional diversity of plant NADPH oxidases

Plant NADPH oxidases also known as respiratory burst oxidase homologs (Rbohs) are a family of membrane-bound enzymes that play diverse roles in the defense response and morphogenetic processes via regulated generation of reactive oxygen species. Rbohs are associated with a variety of functions, although the reason for this is not clear. To evaluate using bioinformatics, the possible mechanisms for the observed functional diversity within the plant kingdom, 127 Rboh protein sequences representing 26 plant species were analyzed. Multiple clusters were identified with gene duplications that were both dicot as well as monocot-specific. The N-terminal sequences were observed to be highly variable. The conserved cysteine (equivalent of Cys890) in C-terminal of AtRbohD suggested that the redox-based modification like S-nitrosylation may regulate the activity of other Rbohs. Three-dimensional models corresponding to the N-terminal domain for Rbohs from Arabidopsis thaliana and Oryza sativa were constructed and molecular dynamics studies were carried out to study the role of Ca2+ in the folding of Rboh proteins. Certain mutations indicated possibly affect the structure and function of the plant NADPH oxidases, thereby providing the rationale for further experimental validation.

Molecular Mechanisms of the R61T Mutation in Apolipoprotein E4: A Dynamic Rescue

The apolipoprotein E4 (ApoE4) gene is the strongest genetic risk factor for Alzheimer's disease (AD). With respect to the other common isoforms of this protein (ApoE2 and ApoE3), ApoE4 is characterized by lower stability that underlies the formation of a stable interaction between the protein's N- and C-terminal domains. AD-related cellular dysfunctions have been linked to this ApoE4 misfolded state. In this regard, it has been reported that the mutation R61T is able to rescue the deleterious cellular effects of ApoE4 by preventing the formation of the misfolded intermediate state. However, a clear description of the structural features at the basis of the R61T-ApoE4 mutant's protective effect is still missing. Recently, using extensive molecular dynamics simulations, we have identified a structural model of an ApoE4 misfolded intermediate state. Building on our previous work, here we explore the dynamical changes induced by the R61T mutation in the ApoE4 native and misfolded states. Notably, we do not observe any local changes in the domains in the R61T-ApoE4 system, rather a general loss of correlated movements in the entire protein structure. More specifically, we detect increased dynamics in the hinge region, which is essential for ApoE4 domain-domain interaction. Consistent with previously reported data on altered phospholipid and receptor binding, we hypothesize that mutations destabilizing the ApoE4 intermediate state change hinge region dynamics, which propagates to distal functional regions of the protein and modifies ApoE4's functional properties. This unique behavior of the ApoE4 hinge region provides, to our knowledge, a novel understanding of ApoE4's role in AD.

Mechanistic Insights from Discrete Molecular Dynamics Simulations of Pesticide-Nanoparticle Interactions

Nanoscale particles have the potential to modulate the transport, lifetimes, and ultimate uptake of pesticides that may otherwise be bound to agricultural soils. Engineered nanoparticles provide a unique platform for studying these interactions. In this study, we utilized discrete molecular dynamics (DMD) as a screening tool for examining nanoparticle-pesticide adsorptive interactions. As a proof-of-concept, we selected a library of 15 pesticides common in the United States and 4 nanomaterials with likely natural or incidental sources, and simulated all possible nanoparticle-pesticide pairs. The resulting adsorption coefficients derived from DMD simulations ranged over several orders of magnitude, and in many cases were significantly stronger than pesticide adsorption on clay surfaces, highlighting the significance of specific nanoscale phases as a preferential media with which pesticides may associate. Binding was found to be significantly enhanced by the capacity to form hydrogen bonds with slightly hydroxylated fullerols, highlighting the importance of considering the precise nature of weathered nanomaterials as opposed to pristine precursors. Results were compared to experimental adsorption studies using selected pesticides, with a Pearson correlation coefficient of 0.97.

Comparative Visualization of the RNA Suboptimal Conformational Ensemble In Vivo

When a ribonucleic acid (RNA) molecule folds, it often does not adopt a single, well-defined conformation. The folding energy landscape of an RNA is highly dependent on its nucleotide sequence and molecular environment. Cellular molecules sometimes alter the energy landscape, thereby changing the ensemble of likely low-energy conformations. The effects of these energy landscape changes on the conformational ensemble are particularly challenging to visualize for large RNAs. We have created a robust approach for visualizing the conformational ensemble of RNAs that is well suited for in vitro versus in vivo comparisons. Our method creates a stable map of conformational space for a given RNA sequence. We first identify single point mutations in the RNA that maximally sample suboptimal conformational space based on the ensemble’s partition function. Then, we cluster these diverse ensembles to identify the most diverse partition functions for Boltzmann stochastic sampling. By using, to our knowledge, a novel nestedness distance metric, we iteratively add mutant suboptimal ensembles to converge on a stable 2D map of conformational space. We then compute the selective 2′ hydroxyl acylation by primer extension (SHAPE)-directed ensemble for the RNA folding under different conditions, and we project these ensembles on the map to visualize. To validate our approach, we established a conformational map of the Vibrio vulnificus add adenine riboswitch that reveals five classes of structures. In the presence of adenine, projection of the SHAPE-directed sampling correctly identified the on-conformation; without the ligand, only off-conformations were visualized. We also collected the whole-transcript in vitro and in vivo SHAPE-MaP for human β-actin messenger RNA that revealed similar global folds in both conditions. Nonetheless, a comparison of in vitro and in vivo data revealed that specific regions exhibited significantly different SHAPE-MaP profiles indicative of structural rearrangements, including rearrangement consistent with binding of the zipcode protein in a region distal to the stop codon.

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme

RNA-Puzzles is a collective experiment in blind 3D RNA structure prediction. We report here a third round of RNA-Puzzles. Five puzzles, 4, 8, 12, 13, 14, all structures of riboswitch aptamers and puzzle 7, a ribozyme structure, are included in this round of the experiment. The riboswitch structures include biological binding sites for small molecules (S-adenosyl methionine, cyclic diadenosine monophosphate, 5-amino 4-imidazole carboxamide riboside 5'-triphosphate, glutamine) and proteins (YbxF), and one set describes large conformational changes between ligand-free and ligand-bound states. The Varkud satellite ribozyme is the most recently solved structure of a known large ribozyme. All puzzles have established biological functions and require structural understanding to appreciate their molecular mechanisms. Through the use of fast-track experimental data, including multidimensional chemical mapping, and accurate prediction of RNA secondary structure, a large portion of the contacts in 3D have been predicted correctly leading to similar topologies for the top ranking predictions. Template-based and homology-derived predictions could predict structures to particularly high accuracies. However, achieving biological insights from de novo prediction of RNA 3D structures still depends on the size and complexity of the RNA. Blind computational predictions of RNA structures already appear to provide useful structural information in many cases. Similar to the previous RNA-Puzzles Round II experiment, the prediction of non-Watson-Crick interactions and the observed high atomic clash scores reveal a notable need for an algorithm of improvement. All prediction models and assessment results are available at http://ahsoka.u-strasbg.fr/rnapuzzles/.

Protein Aggregation and Molecular Crowding: Perspectives From Multiscale Simulations

Cells are extremely crowded environments, thus the use of diluted salted aqueous solutions containing a single protein is too simplistic to mimic the real situation. Macromolecular crowding might affect protein structure, folding, shape, conformational stability, binding of small molecules, enzymatic activity, interactions with cognate biomolecules, and pathological aggregation. The latter phenomenon typically leads to the formation of amyloid fibrils that are linked to several lethal neurodegenerative diseases, but that can also play a functional role in certain organisms. The majority of molecular simulations performed before the last few years were conducted in diluted solutions and were restricted both in the timescales and in the system dimensions by the available computational resources. In recent years, several computational solutions were developed to get close to physiological conditions. In this review we summarize the main computational techniques used to tackle the issue of protein aggregation both in a diluted and in a crowded environment.

A Discontinuous Potential Model for Protein-Protein Interactions

Protein-protein interactions play an important role in many biologic and industrial processes. In this work, we develop a two-bead-per-residue model that enables us to account for protein-protein interactions in a multi-protein system using discontinuous molecular dynamics simulations. This model deploys discontinuous potentials to describe the non-bonded interactions and virtual bonds to keep proteins in their native state. The geometric and energetic parameters are derived from the potentials of mean force between sidechain-sidechain, sidechain-backbone, and backbone-backbone pairs. The energetic parameters are scaled with the aim of matching the second virial coefficient of lysozyme reported in experiment. We also investigate the performance of several bond-building strategies.

Controlling Allosteric Networks in Proteins

Allosteric transition, defined as conformational changes induced by ligand binding, is one of the fundamental properties of proteins. Allostery has been observed and characterized in many proteins, and has been recently utilized to control protein function via regulation of protein activity. Here, we review the physical and evolutionary origin of protein allostery, as well as its importance to protein regulation, drug discovery, and biological processes in living systems. We describe recently developed approaches to identify allosteric pathways, connected sets of pairwise interactions that are responsible for propagation of conformational change from the ligand-binding site to a distal functional site. We then present experimental and computational protein engineering approaches for control of protein function by modulation of allosteric sites. As an example of application of these approaches, we describe a synergistic computational and experimental approach to rescue the cystic-fibrosis-associated protein cystic fibrosis transmembrane conductance regulator, which upon deletion of a single residue misfolds and causes disease. This example demonstrates the power of allosteric manipulation in proteins to both elucidate mechanisms of molecular function and to develop therapeutic strategies that rescue those functions. Allosteric control of proteins provides a tool to shine a light on the complex cascades of cellular processes and facilitate unprecedented interrogation of biological systems.

Dynamic Docking of Conformationally Constrained Macrocycles: Methods and Applications

Many natural products consist of large and flexible macrocycles that engage their targets via multiple contact points. This combination of contained flexibility and large contact area often allows natural products to bind at target surfaces rather than deep pockets, making them attractive scaffolds for inhibiting protein-protein interactions and other challenging therapeutic targets. The increasing ability to manipulate such compounds either biosynthetically or via semisynthetic modification means that these compounds can now be considered as starting points for medchem campaigns rather than solely as ends. Modern medchem benefits substantially from rational improvements made on the basis of molecular docking. As such, docking methods have been enhanced in recent years to deal with the complicated binding modalities and flexible scaffolds of macrocyclic natural products and natural product-like structures. Here, we comprehensively review methods for treating and docking these large macrocyclic scaffolds and discuss some of the resulting advances in medicinal chemistry.

Access Path to the Ligand Binding Pocket May Play a Role in Xenobiotics Selection by AhR

Understanding of multidrug binding at the atomic level would facilitate drug design and strategies to modulate drug metabolism, including drug transport, oxidation, and conjugation. Therefore we explored the mechanism of promiscuous binding of small molecules by studying the ligand binding domain, the PAS-B domain of the aryl hydrocarbon receptor (AhR). Because of the low sequence identities of PAS domains to be used for homology modeling, structural features of the widely employed HIF-2α and a more recent suitable template, CLOCK were compared. These structures were used to build AhR PAS-B homology models. We performed molecular dynamics simulations to characterize dynamic properties of the PAS-B domain and the generated conformational ensembles were employed in in silico docking. In order to understand structural and ligand binding features we compared the stability and dynamics of the promiscuous AhR PAS-B to other PAS domains exhibiting specific interactions or no ligand binding function. Our exhaustive in silico binding studies, in which we dock a wide spectrum of ligand molecules to the conformational ensembles, suggest that ligand specificity and selection may be determined not only by the PAS-B domain itself, but also by other parts of AhR and its protein interacting partners. We propose that ligand binding pocket and access channels leading to the pocket play equally important roles in discrimination of endogenous molecules and xenobiotics.

Gain-of-Function Mutation W493R in the Epithelial Sodium Channel Allosterically Reconfigures Intersubunit Coupling

Sodium absorption in epithelial cells is rate-limited by the epithelial sodium channel (ENaC) activity in lung, kidney, and the distal colon. Pathophysiological conditions, such as cystic fibrosis and Liddle syndrome, result from water-electrolyte imbalance partly due to malfunction of ENaC regulation. Because the quaternary structure of ENaC is yet undetermined, the bases of pathologically linked mutations in ENaC subunits α, β, and γ are largely unknown. Here, we present a structural model of heterotetrameric ENaC α1βα2γ that is consistent with previous cross-linking results and site-directed mutagenesis experiments. By using this model, we show that the disease-causing mutation αW493R rewires structural dynamics of the intersubunit interfaces α1β and α2γ. Changes in dynamics can allosterically propagate to the channel gate. We demonstrate that cleavage of the γ-subunit, which is critical for full channel activation, does not mediate activation of ENaC by αW493R. Our molecular dynamics simulations led us to identify a channel-activating electrostatic interaction between α2Arg-493 and γGlu-348 at the α2γ interface. By neutralizing a sodium-binding acidic patch at the α1β interface, we reduced ENaC activation of αW493R by more than 2-fold. By combining homology modeling, molecular dynamics, cysteine cross-linking, and voltage clamp experiments, we propose a dynamics-driven model for the gain-of-function in ENaC by αW493R. Our integrated computational and experimental approach advances our understanding of structure, dynamics, and function of ENaC in its disease-causing state.

Applications of Discrete Molecular Dynamics in biology and medicine

Discrete Molecular Dynamics (DMD) is a physics-based simulation method using discrete energetic potentials rather than traditional continuous potentials, allowing microsecond time scale simulations of biomolecular systems to be performed on personal computers rather than supercomputers or specialized hardware. With the ongoing explosion in processing power even in personal computers, applications of DMD have similarly multiplied. In the past two years, researchers have used DMD to model structures of disease-implicated protein folding intermediates, study assembly of protein complexes, predict protein-protein binding conformations, engineer rescue mutations in disease-causative protein mutants, design a protein conformational switch to control cell signaling, and describe the behavior of polymeric dispersants for environmental cleanup of oil spills, among other innovative applications.

ApoE4-specific Misfolded Intermediate Identified by Molecular Dynamics Simulations

The increased risk of developing Alzheimer's disease (AD) is associated with the APOE gene, which encodes for three variants of Apolipoprotein E, namely E2, E3, E4, differing only by two amino acids at positions 112 and 158. ApoE4 is known to be the strongest risk factor for AD onset, while ApoE3 and ApoE2 are considered to be the AD-neutral and AD-protective isoforms, respectively. It has been hypothesized that the ApoE isoforms may contribute to the development of AD by modifying the homeostasis of ApoE physiological partners and AD-related proteins in an isoform-specific fashion. Here we find that, despite the high sequence similarity among the three ApoE variants, only ApoE4 exhibits a misfolded intermediate state characterized by isoform-specific domain-domain interactions in molecular dynamics simulations. The existence of an ApoE4-specific intermediate state can contribute to the onset of AD by altering multiple cellular pathways involved in ApoE-dependent lipid transport efficiency or in AD-related protein aggregation and clearance. We present what we believe to be the first structural model of an ApoE4 misfolded intermediate state, which may serve to elucidate the molecular mechanism underlying the role of ApoE4 in AD pathogenesis. The knowledge of the structure for the ApoE4 folding intermediate provides a new platform for the rational design of alternative therapeutic strategies to fight AD.

Ab initio RNA folding

RNA molecules are essential cellular machines performing a wide variety of functions for which a specific three-dimensional structure is required. Over the last several years, the experimental determination of RNA structures through x-ray crystallography and NMR seems to have reached a plateau in the number of structures resolved each year, but as more and more RNA sequences are being discovered, the need for structure prediction tools to complement experimental data is strong. Theoretical approaches to RNA folding have been developed since the late nineties, when the first algorithms for secondary structure prediction appeared. Over the last 10 years a number of prediction methods for 3D structures have been developed, first based on bioinformatics and data-mining, and more recently based on a coarse-grained physical representation of the systems. In this review we are going to present the challenges of RNA structure prediction and the main ideas behind bioinformatic approaches and physics-based approaches. We will focus on the description of the more recent physics-based phenomenological models and on how they are built to include the specificity of the interactions of RNA bases, whose role is critical in folding. Through examples from different models, we will point out the strengths of physics-based approaches, which are able not only to predict equilibrium structures, but also to investigate dynamical and thermodynamical behavior, and the open challenges to include more key interactions ruling RNA folding.

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5-3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson-Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/.

A theoretical view of protein dynamics

Proteins are fascinating supramolecular structures, which are able to recognize ligands transforming binding information into chemical signals. They can transfer information across the cell, can catalyse complex chemical reactions, and are able to transform energy into work with much more efficiency than any human engine. The unique abilities of proteins are tightly coupled with their dynamic properties, which are coded in a complex way in the sequence and carefully refined by evolution. Despite its importance, our experimental knowledge of protein dynamics is still rather limited, and mostly derived from theoretical calculations. I will review here, in a systematic way, the current state-of-the-art theoretical approaches to the study of protein dynamics, emphasizing the most recent advances, examples of use and the expected lines of development in the near future.

The maturation of HIV-1 protease precursor studied by discrete molecular dynamics

The equilibrium properties of a HIV-1-protease precursor are studied by means of an efficient molecular dynamics scheme, which allows for the simulation of the folding of the protein monomers and their dimerization into an active form and compare them with those of the mature protein. The results of the model provide, with atomic detail, an overall account of several experimental findings, including the NMR conformation of the mature dimer, the calorimetric properties of the system, the effects of the precursor tail on the dimerization constant, the secondary chemical shifts of the monomer, and the paramagnetic relaxation enhancement data associated with the conformations of the precursor. It is found that although the mature protein can dimerize in a unique, single way, the precursor populates several dimeric conformations in which monomers are always native-like, but their binding can be non-native.

Exploration of conformational transition pathways from coarse-grained simulations

MOTIVATION

A new algorithm to trace conformational transitions in proteins is presented. The method uses discrete molecular dynamics as engine to sample protein conformational space. A multiple minima Go-like potential energy function is used in combination with several enhancing sampling strategies, such as metadynamics, Maxwell Demon molecular dynamics and essential dynamics. The method, which shows an unprecedented computational efficiency, is able to trace a wide range of known experimental transitions. Contrary to simpler methods our strategy does not introduce distortions in the chemical structure of the protein and is able to reproduce well complex non-linear conformational transitions. The method, called GOdMD, can easily introduce additional restraints to the transition (presence of ligand, known intermediate, known maintained contacts, …) and is freely distributed to the community through the Spanish National Bioinformatics Institute (http://mmb.irbbarcelona.org/GOdMD).

AVAILABILITY

Freely available on the web at http://mmb.irbbarcelona.org/GOdMD.

Multiscale simulation of microbe structure and dynamics

A multiscale mathematical and computational approach is developed that captures the hierarchical organization of a microbe. It is found that a natural perspective for understanding a microbe is in terms of a hierarchy of variables at various levels of resolution. This hierarchy starts with the N -atom description and terminates with order parameters characterizing a whole microbe. This conceptual framework is used to guide the analysis of the Liouville equation for the probability density of the positions and momenta of the N atoms constituting the microbe and its environment. Using multiscale mathematical techniques, we derive equations for the co-evolution of the order parameters and the probability density of the N-atom state. This approach yields a rigorous way to transfer information between variables on different space-time scales. It elucidates the interplay between equilibrium and far-from-equilibrium processes underlying microbial behavior. It also provides framework for using coarse-grained nanocharacterization data to guide microbial simulation. It enables a methodical search for free-energy minimizing structures, many of which are typically supported by the set of macromolecules and membranes constituting a given microbe. This suite of capabilities provides a natural framework for arriving at a fundamental understanding of microbial behavior, the analysis of nanocharacterization data, and the computer-aided design of nanostructures for biotechnical and medical purposes. Selected features of the methodology are demonstrated using our multiscale bionanosystem simulator DeductiveMultiscaleSimulator. Systems used to demonstrate the approach are structural transitions in the cowpea chlorotic mosaic virus, RNA of satellite tobacco mosaic virus, virus-like particles related to human papillomavirus, and iron-binding protein lactoferrin.

Parallel Discrete Molecular Dynamics Simulation With Speculation and In-Order Commitment

Discrete molecular dynamics simulation (DMD) uses simplified and discretized models enabling simulations to advance by event rather than by timestep. DMD is an instance of discrete event simulation and so is difficult to scale: even in this multi-core era, all reported DMD codes are serial. In this paper we discuss the inherent difficulties of scaling DMD and present our method of parallelizing DMD through event-based decomposition. Our method is microarchitecture inspired: speculative processing of events exposes parallelism, while in-order commitment ensures correctness. We analyze the potential of this parallelization method for shared-memory multiprocessors. Achieving scalability required extensive experimentation with scheduling and synchronization methods to mitigate serialization. The speed-up achieved for a variety of system sizes and complexities is nearly 6× on an 8-core and over 9× on a 12-core processor. We present and verify analytical models that account for the achieved performance as a function of available concurrency and architectural limitations.