Kink turn sRNA folding upon L7Ae binding using molecular dynamics simulations

Molecular dynamics simulations at high temperatures of the Aeropyrum pernix L7Ae thermostable protein: Insight into the unfolding pathway

Most life forms on earth live at temperatures below 50 °C. Within these organisms are proteins that form the three-dimensional structures essential to their biological activity and function. However, some thermophilic life forms can resist higher temperatures and have corresponding adaptations to preserve protein function at these high temperatures. Among the structural factors responsible for this resistance of thermophilic proteins to high temperatures is the presence of additional hydrogen bonds in the thermophilic proteins, which means that the structure of the protein is more resistant to unfolding. Similarly, thermostable proteins are rich in structure-stabilizing salt bridges and/or disulfide bridges. In this context, we perform multiple replica molecular dynamics simulations at different temperatures on the Aeropyrum pernix (L7Ae) protein (from the crenarchaeal species A. pernix), known for its high melting temperature, and this in the aim to elucidate the structural factors responsible for its high thermostability. The results reveal that between the most sensitive regions of the protein to the increase of temperature are the loops L1, and L5, which surround the hydrophobic core region of the protein, besides the loop L9, and the C-terminal α5 region. This latter is the longer alpha helix of the protein secondary structure motifs and it is the first to be denaturated at 450 K, while the rest of the protein secondary structure motifs at this temperature were intact. The mechanism of unfolding that follows this protein at 550 K is similar to other thermophile proteins found in literature, with the opening of the loops that surround the hydrophobic core of the protein. So, the latter is completely exposed to the solvent, and partially denatured. The total denaturation process of the protein takes an average time of 40 ns to be achieved. Our investigation also shows that all the calculated salt bridges, with distances less than or equal to 6 A°, are on the periphery part of the protein, exposed to the solvent. However, the hydrophobic core of the protein is not involved in the formation of salt bridges, but rather with formation of some important hydrogen bondings that still persist even at 450 K. So, optimizing hydrogen bonding, near or within the core region, at high temperatures is a strategy that follows this thermostable protein to protect its hydrophobic core from denaturation, and ensure the thermal stability of the protein.

Simulation Study of the Plasticity of k-Turn Motif in Different Environments

The k-turn is a widespread and important motif in RNA. According to the internal hydrogen bond network, it has two stable states, called N1 and N3. The relative stability between the states changes with the environment. It is able to accept different conformations in different environments. This is called the "plasticity" of a molecule. In this work, we study the plasticity of k-turn by the mixing REMD method in explicit solvent. The results are concluded as follows. First, N1 and N3 are almost equally stable when k-turn is in the solvent alone. The molecule is quite flexible as a hinge. However, after binding to different proteins, such as the proteins L7Ae and L24e, k-turn falls into one global minimum. The preferred state could be either N1 or N3. On the contrary, the other nonpreferred state becomes unstable with a weaker binding affinity to the protein. It reveals that RNA-binding protein is able to modulate the representative state of k-turn at equilibrium. This is in agreement with the findings in experiments. Moreover, free energy calculations show that the free energy barrier between the N1 and N3 states of k-turn increases in the complexes. The state-to-state transition is greatly impeded. We also give a deep discussion on the mechanism of the high plasticity of k-turn in different environments.

Order-disorder transition of intrinsically disordered kinase inducible transactivation domain of CREB

Transcription factor cyclic Adenosine monophosphate response-element binding protein plays a critical role in the cyclic AMP response pathway via its intrinsically disordered kinase inducible transactivation domain (KID). KID is one of the most studied intrinsically disordered proteins (IDPs), although most previous studies focus on characterizing its disordered state structures. An interesting question that remains to be answered is how the order-disorder transition occurs at experimental conditions. Thanks to the newly developed IDP-specific force field ff14IDPSFF, the quality of conformer sampling for IDPs has been dramatically improved. In this study, molecular dynamics (MD) simulations were used to study the order-to-disorder transition kinetics of KID based on the good agreement with the experiment on its disordered-state properties. Specifically, we tested four force fields, ff99SBildn, ff99IDPs, ff14IDPSFF, and ff14IDPs in the simulations of KID and found that ff14IDPSFF can generate more diversified disordered conformers and also reproduce more accurate experimental secondary chemical shifts. Kinetics analysis of MD simulations demonstrates that the order-disorder transition of KID obeys the first-order kinetics, and the transition nucleus is I127/L128/L141. The possible transition pathways from the nucleus to the last folded residues were identified as I127-R125-L138-L141-S143-A145 and L128-R125-L138-L141-S143-A145 based on a residue-level dynamical network analysis. These computational studies not only provide testable prediction/hypothesis on the order-disorder transition of KID but also confirm that the ff14IDPSFF force field can be used to explore the correlation between the structure and function of IDPs.

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field.

Predicting Ion Effects in an RNA Conformational Equilibrium

We develop a partial charge-based tightly bound ion (PCTBI) model for the ion effects in RNA folding. On the basis of the Monte Carlo tightly bound ion (MCTBI) approach, the model can account for ion fluctuation and correlation effects, and can predict the ion distribution around the RNA. Furthermore, unlike the previous coarse-grained RNA charge models, where negative charges are placed on the phosphates only, the current new model considers the detailed all-atom partial charge distribution on the RNA. Thus, the model not only keeps the advantage of the MCTBI model, but also has the potential to provide important detailed information unattainable by the previous MCTBI models. For example, the model predicts the reduction in ion binding upon protein binding and ion-induced conformational switches. For hepatitis C virus genomic RNA, the model predicts a Mg²⁺-induced stabilization of a kissing motif for a cis-acting regulatory element in the genomic RNA. Extensive theory-experiment comparisons support the reliability of the theoretical predictions. Therefore, the model may serve as a robust starting point for further development of an accurate method for ion effects in an RNA conformational equilibrium and RNA-cofactor interactions.

Allosteric mechanism of cyclopropylindolobenzazepine inhibitors for HCV NS5B RdRp via dynamic correlation network analysis

HCV RNA dependent RNA polymerase (RdRp) nonstructural protein 5B (NS5B) is a major target against hepatitis C virus (HCV) for antiviral therapy. Recently discovered cyclopropylindolobenzazepine derivatives have been considered as the most potent for their ability to bind the thumb site 1 domain and allosterically inhibit HCV NS5B RdRp activity. However, the allosteric mechanism for these derivatives has not been clarified at the molecular level. In this study, fluctuation correlation networks were constructed based on all-atom molecular dynamics simulations to elucidate the allosteric mechanism. The fluctuation correlation networks between free and M2 bound NS5B are significantly different. Information can better transfer from the allosteric site to the catalytic site for bound NS5B than for free NS5B. Thus, the hypothesis of "binding induced allosteric regulation" is proposed to link the enzyme activation and inhibitor binding and then confirmed by the mutant network. Finally, one possible allosteric pathway was identified with the shortest path and evaluated by the perturbation of the network. These methods will be helpful to identify the allosteric pathway of other proteins and to design new drugs targeting the pathway.

Allosteric Autoinhibition Pathway in Transcription Factor ERG: Dynamics Network and Mutant Experimental Evaluations

Allosteric autoinhibition exists in many transcription factors. The ERG proteins exhibit autoinhibition on DNA binding by the C-terminal and N-terminal inhibitory domains (CID and NID). However, the autoinhibition mechanism and allosteric pathway of ERG are unknown. In this study we intend to elucidate the residue-level allosteric mechanism and pathway via a combined approach of computational and experimental analyses. Specifically computational residue-level fluctuation correlation data was analyzed to reveal detailed dynamics signatures in the allosteric autoinhibition process. A hypothesis of "NID/CID binding induced allostery" is proposed to link similar structures and different protein functions, which is subsequently validated by perturbation and mutation analyses in both computation and experiment. Two possible allosteric autoinhibition pathways of L286-L382-A379-G377-I360-Y355-R353 and L286-L382-A379-G377-I360-Y355- A351-K347-R350 were identified computationally and were confirmed by the computational and experimental mutations. Specifically we identified two mutation sites on the allosteric inhibition pathways, L286P/Q383P (NID/CID binding site) and I360G (pathway junction), which completely restore the wild type DNA binding affinity. These results suggest that the putative protein structure-function relationship may be augmented with a general relationship of protein "structure/fluctuation-correlation/function" for more thorough analyses of protein functions.

Calculating protein-ligand binding affinities with MMPBSA: Method and error analysis

Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) methods have become widely adopted in estimating protein-ligand binding affinities due to their efficiency and high correlation with experiment. Here different computational alternatives were investigated to assess their impact to the agreement of MMPBSA calculations with experiment. Seven receptor families with both high-quality crystal structures and binding affinities were selected. First the performance of nonpolar solvation models was studied and it was found that the modern approach that separately models hydrophobic and dispersion interactions dramatically reduces RMSD's of computed relative binding affinities. The numerical setup of the Poisson-Boltzmann methods was analyzed next. The data shows that the impact of grid spacing to the quality of MMPBSA calculations is small: the numerical error at the grid spacing of 0.5 Å is already small enough to be negligible. The impact of different atomic radius sets and different molecular surface definitions was further analyzed and weak influences were found on the agreement with experiment. The influence of solute dielectric constant was also analyzed: a higher dielectric constant generally improves the overall agreement with experiment, especially for highly charged binding pockets. The data also showed that the converged simulations caused slight reduction in the agreement with experiment. Finally the direction of estimating absolute binding free energies was briefly explored. Upon correction of the binding-induced rearrangement free energy and the binding entropy lost, the errors in absolute binding affinities were also reduced dramatically when the modern nonpolar solvent model was used, although further developments were apparently necessary to further improve the MMPBSA methods. © 2016 Wiley Periodicals, Inc.

Monte Carlo Tightly Bound Ion Model: Predicting Ion-Binding Properties of RNA with Ion Correlations and Fluctuations

Experiments have suggested that ion correlation and fluctuation effects can be potentially important for multivalent ions in RNA folding. However, most existing computational methods for the ion electrostatics in RNA folding tend to ignore these effects. The previously reported tightly bound ion (TBI) model can treat ion correlation and fluctuation but its applicability to biologically important RNAs is severely limited by the low computational efficiency. Here, on the basis of Monte Carlo sampling for the many-body ion distribution, we develop a new computational model, the Monte Carlo tightly bound ion (MCTBI) model, for ion-binding properties around an RNA. Because of an enhanced sampling algorithm for ion distribution, the model leads to a significant improvement in computational efficiency. For example, for a 160-nt RNA, the model causes a more than 10-fold increase in the computational efficiency, and the improvement in computational efficiency is more pronounced for larger systems. Furthermore, unlike the earlier model that describes ion distribution using the number of bound ions around each nucleotide, the current MCTBI model is based on the three-dimensional coordinates of the ions. The higher efficiency of the model allows us to treat the ion effects for medium to large RNA molecules, RNA-ligand complexes, and RNA-protein complexes. This new model together with proper RNA conformational sampling and the energetics model may serve as a starting point for further development for the ion effects in RNA folding and conformational changes and for large nucleic acid systems.

Synergistic Modification Induced Specific Recognition between Histone and TRIM24 via Fluctuation Correlation Network Analysis

Histone modification plays a key role in gene regulation and gene expression. TRIM24 as a histone reader can recognize histone modification. However the specific recognition mechanism between TRIM24 and histone modification is unsolved. Here, systems biology method of dynamics correlation network based on molecular dynamics simulation was used to answer the question. Our network analysis shows that the dynamics correlation network of H3K23ac is distinctly different from that of wild type and other modifications. A hypothesis of “synergistic modification induced recognition” is then proposed to link histone modification and TRIM24 binding. These observations were further confirmed from community analysis of networks with mutation and network perturbation. Finally, a possible recognition pathway is also identified based on the shortest path search for H3K23ac. Significant difference of recognition pathway was found among different systems due to methylation and acetylation modifications. The analysis presented here and other studies show that the dynamic network-based analysis might be a useful general strategy to study the biology of protein post-translational modification and associated recognition.

Can We Execute Stable Microsecond-Scale Atomistic Simulations of Protein-RNA Complexes?

We report over 30 μs of unrestrained molecular dynamics simulations of six protein-RNA complexes in explicit solvent. We utilize the AMBER ff99bsc0χ(OL3) RNA force field combined with the ff99SB protein force field and its more recent ff12SB version with reparametrized side-chain dihedrals. The simulations show variable behavior, ranging from systems that are essentially stable to systems with progressive deviations from the experimental structure, which we could not stabilize anywhere close to the starting experimental structure. For some systems, microsecond-scale simulations are necessary to achieve stabilization after initial sizable structural perturbations. The results show that simulations of protein-RNA complexes are challenging and every system should be treated individually. The simulations are affected by numerous factors, including properties of the starting structures (the initially high force field potential energy, resolution limits, conformational averaging, crystal packing, etc.), force field imbalances, and real flexibility of the studied systems. These factors, and thus the simulation behavior, differ from system to system. The structural stability of simulated systems does not correlate with the size of buried interaction surface or experimentally determined binding affinities but reflects the type of protein-RNA recognition. Protein-RNA interfaces involving shape-specific recognition of RNA are more stable than those relying on sequence-specific RNA recognition. The differences between the protein force fields are considerably smaller than the uncertainties caused by sampling and starting structures. The ff12SB improves description of the tyrosine side-chain group, which eliminates some problems associated with tyrosine dynamics.

Molecular dynamic simulations of protein/RNA complexes: CRISPR/Csy4 endoribonuclease

BACKGROUND

Many prokaryotic genomes comprise Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) offering defense against foreign nucleic acids. These immune systems are conditioned by the production of small CRISPR-derived RNAs matured from long RNA precursors. This often requires a Csy4 endoribonuclease cleaving the RNA 3'-end.

METHODS

We report extended explicit solvent molecular dynamic (MD) simulations of Csy4/RNA complex in precursor and product states, based on X-ray structures of product and inactivated precursor (55 simulations; ~3.7μs in total).

RESULTS

The simulations identify double-protonated His29 and deprotonated terminal phosphate as the likely dominant protonation states consistent with the product structure. We revealed potential substates consistent with Ser148 and His29 acting as the general base and acid, respectively. The Ser148 could be straightforwardly deprotonated through solvent and could without further structural rearrangements deprotonate the nucleophile, contrasting similar studies investigating the general base role of nucleobases in ribozymes. We could not locate geometries consistent with His29 acting as general base. However, we caution that the X-ray structures do not always capture the catalytically active geometries and then the reactive structures may be unreachable by the simulation technique.

CONCLUSIONS

We identified potential catalytic arrangement of the Csy4/RNA complex but we also report limitations of the simulation technique. Even for the dominant protonation state we could not achieve full agreement between the simulations and the structural data.

GENERAL SIGNIFICANCE

Potential catalytic arrangement of the Csy4/RNA complex is found. Further, we provide unique insights into limitations of simulations of protein/RNA complexes, namely, the influence of the starting experimental structures and force field limitations. This article is part of a Special Issue entitled Recent developments of molecular dynamics.