Explicit-solvent Simulations Research Articles

Determinants of Alanine Dipeptide Conformational Equilibria on Graphene and Hydroxylated Derivatives.

Understanding the interaction of carbon nanomaterials with proteins is essential for determining the potential effects of these materials on health and in the design of biotechnology based on them. Here we leverage explicit-solvent molecular simulation and multidimensional free-energy calculations to investigate how adsorption to carbon nanomaterial surfaces affects the conformational equilibrium of alanine dipeptide, a widely used model of protein backbone structure. We find that the two most favorable structures of alanine dipeptide on graphene (or large carbon nanotubes) correspond to the two amide linkages lying in the same plane, flat against the surface, rather than the nonplanar α-helix-like and β-sheet-like conformations that predominate in aqueous solution. On graphenic surfaces, the latter conformations are metastable and most often correspond to amide-π stacking of the N-terminal amide. The calculations highlight the key role of amide-π interactions in determining the conformational equilibrium. Lesser but significant contributions from hydrogen bonding to the high density interfacial water layer or to the hydroxy groups of hydroxylated graphene also define the most favorable conformations. This work should yield insight on the influence of carbon nanotubes, graphene, and their functionalized derivatives on protein structure.

Accelerated flexible protein-ligand docking using Hamiltonian replica exchange with a repulsive biasing potential.

A molecular dynamics replica exchange based method has been developed that allows rapid identification of putative ligand binding sites on the surface of biomolecules. The approach employs a set of ambiguity restraints in replica simulations between receptor and ligand that allow close contacts in the reference replica but promotes transient dissociation in higher replicas. This avoids long-lived trapping of the ligand or partner proteins at nonspecific, sticky, sites on the receptor molecule and results in accelerated exploration of the possible binding regions. In contrast to common docking methods that require knowledge of the binding site, exclude solvent and often keep parts of receptor and ligand rigid the approach allows for full flexibility of binding partners. Application to peptide-protein, protein-protein and a drug-receptor system indicate rapid sampling of near-native binding regions even in case of starting far away from the native binding site outperforming continuous MD simulations. An application on a DNA minor groove binding ligand in complex with DNA demonstrates that it can also be used in explicit solvent simulations.

Molecular dynamics simulation on the inhibition mechanism of peptide-based inhibitor of islet amyloid polypeptide (IAPP) to islet amyloid polypeptide (IAPP22-28 ) oligomers.

Aggregation of islet amyloid polypeptide (IAPP) is implicated in the development of type 2 diabetes. The modified NFGAIL with double N-methylated at Gly24 and Ile26 has the property of soluble, non-amyloidogenic, non-cytotoxic, and the ability of inhibiting amyloid formation and cytotoxicity of IAPP. To discover the inhibition mechanism of this peptide inhibitor and provide useful information to design more potential peptide inhibitors, molecular dynamics simulations in explicit solvent were performed. The simulation results reveal that Gly24 and Ile26 are of importance in IAPP aggregation, and N-methylation at these two key residues will disrupt the stability of formed oligomer and prevent the conformation transition of free monomer near the oligomer template. The origin of the N-methylated peptide inhibitor inhibiting IAPP aggregation is that it can keep good binding with IAPP template by stable hydrogen bonding interaction. Furthermore, it cannot induce the conformational transition of free monomer by preventing the hydrogen bond interaction between free monomer and boundary peptide. The structural environment can largely affect the stacking of free monomers to the template. Our study sheds light on the inhibition mechanism of peptide inhibitor at molecular level and may provide guidance for the future design and discovery of new peptide inhibitors.

Is Protein Single Molecule Dynamics under Force Described by Two or More States?

Single molecule force spectroscopy is a useful technique for investigating mechanically induced protein unfolding and refolding under reduced forces by monitoring the end-to-end distance of the protein. The data is often interpreted via a “two-state” model based on the assumption that the end-to-end distance alone is a good reaction coordinate and the thermodynamic behavior is then ascribed to the free energy as a function of this one reaction coordinate. In this paper, we determined the free energy surface (PMF) of GB1 protein from atomistic simulations in explicit solvent under different applied forces as a function of two collective variables (the end-to-end-distance, and the fraction of native contacts ρ). The calculated 2-d free energy surfaces exhibited several distinct states, or basins, mostly visible along the ρ coordinate. Brownian dynamics (BD) simulations on the smoothed free energy surface show that the protein visits a metastable molten globule state and is thus a three state folder, not the two state folder inferred using the end-to-end distance as the sole reaction coordinate. These BD simulations reproduce the unfolding and collapse-refolding patterns observed in the force-clamp experiments. This study lends support to recent experiments that suggest that GB1 is not a two-state folder.

Nucleosome Opening Kinetics and the Influence of Histone Modifications Studied by Single Molecule FRET

Nucleosomes compact the genome and regulate access by wrapping and unwrapping DNA. Here we characterized NaCl-induced opening of mononucleosomes by single-molecule FRET, using donor/acceptor labels on the DNA and various histones. By species-selective fluorescence lifetime and photon distribution analysis we identified new nucleosome opening intermediates and developed a kinetic model [1]. Opening proceeds through a weakening of the H2A-H2B dimer/(H3-H4)2 tetramer interface on a 0.1 ms time scale, then by a slower two-step release of the dimers coupled to DNA unwrapping, extending from several ms to minutes. Nucleosome opening and detachment of histone dimers proceed asymmetrically depending on the DNA sequence.Mutations at the H2A/H3 interface (H2A R81A, R88A, R81A/R88A, R81E/R88E) facilitate the initial opening, confirming the importance of the dimer:tetramer interface for nucleosome stability. This is also supported by molecular dynamics (MD) simulations that show enhanced DNA fluctuations for the mutants. Partially opened states such as described here might be a convenient nucleation point for DNA-recognizing proteins.For characterizing the role of histone tails in nucleosome interaction and stability, we performed MD simulations in explicit and implicit solvent on the H4 tail interacting with the acidic patch of a neighboring nucleosome. The simulations show that H4K16 acetylation decreases the interactions between H4 tail and the acidic patch of the neighboring nucleosome, a process that is central to the regulation of chromatin compaction [2,3].

Computational analysis of Amsacrine resistance in human topoisomerase II alpha mutants (R487K and E571K) using homology modeling, docking and all-atom molecular dynamics simulation in explicit solvent

Amsacrine is an effective topoisomerase II enzyme inhibitor in acute lymphatic leukemia. Previous experimental studies have successfully identified two important mutations (R487K and E571K) conferring 100 and 25 fold resistance to Amsacrine respectively. Although the reduction of the cleavage ligand-DNA-protein ternary complex has been well thought as the major cause of drug resistance, the detailed energetic, structural and dynamic mechanisms remain to be elusive. In this study, we constructed human topoisomerase II alpha (hTop2α) homology model docked with Amsacrine based on crystal structure of human Top2β in complex with etoposide. This wild type complex was used to build the ternary complex with R487K and E571K mutants. Three 500ns molecular dynamics simulations were performed on complex systems of wild type and two mutants. The detailed energetic, structural and dynamic analysis were performed on the simulation data. Our binding data indicated a significant impairment of Amsacrine binding energy in the two mutants compared with the wild type. The order of weakening (R487K>E571K) was in agreement with the order of experimental drug resistance fold (R489K>E571K). Our binding energy decomposition further indicated that weakening of the ligand-protein interaction rather than the ligand-DNA interaction was the major contributor of the binding energy difference between R487K and E571K. In addition, key residues contributing to the binding energy (ΔG) or the decrease of the binding energy (ΔΔG) were identified through the energy decomposition analysis. The change in ligand binding pose, dynamics of protein, DNA and ligand upon the mutations were thoroughly analyzed and discussed. Deciphering the molecular basis of drug resistance is crucial to overcome drug resistance using rational drug design.

Protocols for Molecular Dynamics Simulations of RNA Nanostructures.

Molecular dynamics (MD) simulations have been used as one of the main research tools to study a wide range of biological systems and bridge the gap between X-ray crystallography or NMR structures and biological mechanism. In the field of RNA nanostructures, MD simulations have been used to fix steric clashes in computationally designed RNA nanostructures, characterize the dynamics, and investigate the interaction between RNA and other biomolecules such as delivery agents and membranes.In this chapter we present examples of computational protocols for molecular dynamics simulations in explicit and implicit solvent using the Amber Molecular Dynamics Package. We also show examples of post-simulation analysis steps and briefly mention selected tools beyond the Amber package. Limitations of the methods, tools, and protocols are also discussed. Most of the examples are illustrated for a small RNA duplex (helix), but the protocols are applicable to any nucleic acid structure, subject only to the computational speed and memory limitations of the hardware available to the user.

A computational study of self-assembled hexapeptide inhibitors against amyloid-β (Aβ) aggregation.

The fibrillation and deposition of amyloid-β (Aβ) peptides in human brains are pathologically linked to Alzheimer's disease (AD). Development of different inhibitors (peptides, organic molecules, and nanoparticles) to prevent Aβ aggregation becomes a promising therapeutic strategy for AD treatment. We recently propose a "like-interacts-like" design principle to computationally design/screen and experimentally validate a new set of hexapeptide inhibitors with completely different sequences from the Aβ sequence. These hexapeptide inhibitors inhibit Aβ aggregation and reduce Aβ-induced cytotoxicity. However, inhibitory mechanisms of these hexapeptides and the underlying interactions between hexapeptides and Aβ remain unclear. Herein we apply multi-scale computational methods (quantum-chemical calculations, molecular docking and explicit-solvent molecular dynamic simulation) to explore the structure, dynamics, and interaction between 3 identified hexapeptides (CTLWWG, GTVWWG, and CTIYWG) and different Aβ-derived fragments and an Aβ17-42 pentamer. When interacting with 6 Aβ-derived fragments, 3 hexapeptide inhibitors show stronger interactions with two lysine-included fragments (16KLVFFA21 and 27NKGAII33) than other fragments, indicating different sequence-specific interactions with Aβ. When interacting with the Aβ17-42 pentamer, the 3 peptides show similar binding modes and interaction mechanisms by preferentially binding to the edge of the Aβ17-42 pentamer to potentially block the Aβ elongation pathway. This work provides structural-based binding information on further modification and optimization of these peptide inhibitors to experimentally enhance their inhibitory abilities against Aβ aggregation.

Performance of MDockPP in CAPRI rounds 28-29 and 31-35 including the prediction of water-mediated interactions.

Protein-protein interactions are either through direct contacts between two binding partners or mediated by structural waters. Both direct contacts and water-mediated interactions are crucial to the formation of a protein-protein complex. During the recent CAPRI rounds, a novel parallel searching strategy for predicting water-mediated interactions is introduced into our protein-protein docking method, MDockPP. Briefly, a FFT-based docking algorithm is employed in generating putative binding modes, and an iteratively derived statistical potential-based scoring function, ITScorePP, in conjunction with biological information is used to assess and rank the binding modes. Up to 10 binding modes are selected as the initial protein-protein complex structures for MD simulations in explicit solvent. Water molecules near the interface are clustered based on the snapshots extracted from independent equilibrated trajectories. Then, protein-ligand docking is employed for a parallel search for water molecules near the protein-protein interface. The water molecules generated by ligand docking and the clustered water molecules generated by MD simulations are merged, referred to as the predicted structural water molecules. Here, we report the performance of this protocol for CAPRI rounds 28-29 and 31-35 containing 20 valid docking targets and 11 scoring targets. In the docking experiments, we predicted correct binding modes for nine targets, including one high-accuracy, two medium-accuracy, and six acceptable predictions. Regarding the two targets for the prediction of water-mediated interactions, we achieved models ranked as "excellent" in accordance with the CAPRI evaluation criteria; one of these two targets is considered as a difficult target for structural water prediction. Proteins 2017; 85:424-434. © 2016 Wiley Periodicals, Inc.

Hot spot mapping of protein surfaces with TEMPOL: Bovine pancreatic RNase A as a model system

TEMPOL spin-label has been used to identify surface exposure of protein nuclei from NMR analysis of the induced paramagnetic relaxation enhancements (PRE). The absence of linear dependence between atom depths and observed PRE reveals that specific mechanisms drive the approach of the paramagnet to the protein surface. RNase A represents a unique protein system to explore the fine details of the information offered by TEMPOL induced PRE, due to the abundance of previous results, obtained in solution and in the crystal, dealing with surface dynamics behavior of this protein. MD simulations in explicit solvent have been performed, also in the presence of TEMPOL, in order to delineate the role of intermolecular hydrogen bonds (HB) on PRE extents. Comparison of our results with the ones obtained from multiple solvent crystal structure (MSCS) studies yields information on the specificities that these two techniques have for characterizing protein-ligand interactions, a fundamental step in the development of reliable surface druggability predictors.

Implicit Solvent Model for Million-Atom Atomistic Simulations: Insights into the Organization of 30-nm Chromatin Fiber

Molecular dynamics (MD) simulations based on the implicit solvent generalized Born (GB) models can provide significant computational advantages over the traditional explicit solvent simulations. However, the standard GB becomes prohibitively expensive for all-atom simulations of large structures; the model scales poorly, ∼n2, with the number of solute atoms. Here we combine our recently developed optimal point charge approximation (OPCA) with the hierarchical charge partitioning (HCP) approximation to present an ∼n log n multiscale, yet fully atomistic, GB model (GB-HCPO). The HCP approximation exploits the natural organization of biomolecules (atoms, groups, chains, and complexes) to partition the structure into multiple hierarchical levels of components. OPCA approximates the charge distribution for each of these components by a small number of point charges so that the low order multipole moments of these components are optimally reproduced. The approximate charges are then used for computing electrostatic interactions with distant components, while the full set of atomic charges are used for nearby components. We show that GB-HCPO can deliver up to 2 orders of magnitude speedup compared to the standard GB, with minimal impact on its accuracy. For large structures, GB-HCPO can approach the same nominal speed, as in nanoseconds per day, as the highly optimized explicit-solvent simulation based on particle mesh Ewald (PME). The increase in the nominal simulation speed, relative to the standard GB, coupled with substantially faster sampling of conformational space, relative to the explicit solvent, makes GB-HCPO a suitable candidate for MD simulation of large atomistic systems in implicit solvent. As a practical demonstration, we use GB-HCPO simulation to refine a ∼1.16 million atom structure of 30 nm chromatin fiber (40 nucleosomes). The refined structure suggests important details about spatial organization of the linker DNA and the histone tails in the fiber: (1) the linker DNA fills the core region, allowing the H3 histone tails to interact with the linker DNA, which is consistent with experiment; (2) H3 and H4 tails are found mostly in the core of the structure, closer to the helical axis of the fiber, while H2A and H2B are mostly solvent exposed. Potential functional consequences of these findings are discussed. GB-HCPO is implemented in the open source MD software NAB in Amber 2016.

A protocol for the refinement of NMR structures using simultaneously pseudocontact shift restraints from multiple lanthanide ions.

The binding of paramagnetic metal ions to proteins produces a number of different effects on the NMR spectra of the system. In particular, when the magnetic susceptibility of the metal ion is anisotropic, pseudocontact shifts (PCSs) arise and can be easily measured. They constitute very useful restraints for the solution structure determination of metal-binding proteins. In this context, there has been great interest in the use of lanthanide(III) ions to induce PCSs in diamagnetic proteins, e.g. through the replacement native calcium(II) ions. By preparing multiple samples in each of which a different ion of the lanthanide series is introduced, it is possible to obtain multiple independent PCS datasets that can be used synergistically to generate protein structure ensembles (typically called bundles). For typical NMR-based determination of protein structure, it is necessary to perform an energetic refinement of such initial bundles to obtain final structures whose geometric quality is suitable for deposition in the PDB. This can be conveniently done by using restrained molecular dynamics simulations (rMD) in explicit solvent. However, there are no available protocols for rMD using multiple PCS datasets as part of the restraints. In this work, we extended the PCS module of the AMBER MD package to handle multiple datasets and tuned a previously developed protocol for NMR structure refinement to achieve consistent convergence with PCS restraints. Test calculations with real experimental data show that this new implementation delivers the expected improvement of protein geometry, resulting in final structures that are of suitable quality for deposition. Furthermore, we observe that also initial structures generated only with traditional restraints can be successfully refined using traditional and PCS restraints simultaneously.

Structure of the SLC4 transporter Bor1p in an inward-facing conformation.

Bor1p is a secondary transporter in yeast that is responsible for boron transport. Bor1p belongs to the SLC4 family which controls bicarbonate exchange and pH regulation in animals as well as borate uptake in plants. The SLC4 family is more distantly related to members of the Amino acid-Polyamine-organoCation (APC) superfamily, which includes well studied transporters such as LeuT, Mhp1, AdiC, vSGLT, UraA, SLC26Dg. Their mechanism generally involves relative movements of two domains: a core domain that binds substrate and a gate domain that in many cases mediates dimerization. To shed light on conformational changes governing transport by the SLC4 family, we grew helical membrane crystals of Bor1p from Saccharomyces mikatae and determined a structure at ∼6 Å resolution using cryo-electron microscopy. To evaluate the conformation of Bor1p in these crystals, a homology model was built based on the related anion exchanger from red blood cells (AE1). This homology model was fitted to the cryo-EM density map using the Molecular Dynamics (MD) Flexible Fitting method and then relaxed by all-atom MD simulation in explicit solvent and membrane. Mapping of water accessibility indicates that the resulting structure represents an inward-facing conformation. Comparisons of the resulting Bor1p model with the X-ray structure of AE1 in an outward-facing conformation, together with MD simulations of inward-facing and outward-facing Bor1p models, suggest rigid body movements of the core domain relative to the gate domain. These movements are consistent with the rocking-bundle transport mechanism described for other members of the APC superfamily.

Evaluation of Generalized Born Models for Large Scale Affinity Prediction of Cyclodextrin Host-Guest Complexes.

Binding affinity prediction with implicit solvent models remains a challenge in virtual screening for drug discovery. In order to assess the predictive power of implicit solvent models in docking techniques with Amber scoring, three generalized Born models (GBHCT, GBOBCI, and GBOBCII) available in Dock 6.7 were utilized, for determining the binding affinity of a large set of β-cyclodextrin complexes with 75 neutral guest molecules. The results were compared to potential of mean force (PMF) free energy calculations with four GB models (GBStill, GBHCT, GBOBCI, and GBOBCII) and to experimental data. Docking results yield similar accuracy to the computationally demanding PMF method with umbrella sampling. Neither docking nor PMF calculations reproduce the experimental binding affinities, however, as indicated by a small Spearman rank order coefficient (∼0.5). The binding energies obtained from GB models were decomposed further into individual contributions of the binding partners and solvent environments and compared to explicit solvent simulations for five complexes allowing for rationalizing the difference between explicit and implicit solvent models. An important observation is that the explicit solvent screens the interaction between host and guest much stronger than GB models. In contrast, the screening in GB models is too strong in solutes, leading to overestimation of short-range interactions and too strong binding. It is difficult to envision a way of overcoming these two opposite effects.

Blinded predictions of distribution coefficients in the SAMPL5 challenge.

In the context of the SAMPL5 challenge water-cyclohexane distribution coefficients for 53 drug-like molecules were predicted. Four different models based on molecular dynamics free energy calculations were tested. All models initially assumed only one chemical state present in aqueous or organic phases. Model A is based on results from an alchemical annihilation scheme; model B adds a long range correction for the Lennard Jones potentials to model A; model C adds charging free energy corrections; model D applies the charging correction from model C to ionizable species only. Model A and B perform better in terms of mean-unsigned error (hbox {MUE}=6.79<6.87<6.95 log D units − 95 % confidence interval) and determination coefficient (hbox {R}^2 = 0.26< 0.27< 0.28), while charging corrections lead to poorer results with model D (hbox {MUE}=12.8<12.63<12.98 and hbox {R}^2 = 0.16<0.17<0.18). Because overall errors were large, a retrospective analysis that allowed co-existence of ionisable and neutral species of a molecule in aqueous phase was investigated. This considerably reduced systematic errors (hbox {MUE}=1.87<1.97<2.07 and hbox {R}^2 = 0.35<0.40<0.45). Overall accurate log D predictions for drug-like molecules that may adopt multiple tautomers and charge states proved difficult, indicating a need for methodological advances to enable satisfactory treatment by explicit-solvent molecular simulations.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-016-9969-1) contains supplementary material, which is available to authorized users.

Computationally Designed Armadillo Repeat Proteins for Modular Peptide Recognition

Armadillo repeat proteins (ArmRPs) recognize their target peptide in extended conformation and bind, in a first approximation, two residues per repeat. Thus, they may form the basis for building a modular system, in which each repeat is complementary to a piece of the target peptide. Accordingly, preselected repeats could be assembled into specific binding proteins on demand and thereby avoid the traditional generation of every new binding molecule by an independent selection from a library. Stacked armadillo repeats, each consisting of 42 aa arranged in three α-helices, build an elongated superhelical structure. Here, we analyzed the curvature variations in natural ArmRPs and identified a repeat pair from yeast importin-α as having the optimal curvature geometry that is complementary to a peptide over its whole length. We employed a symmetric in silico design to obtain a uniform sequence for a stackable repeat while maintaining the desired curvature geometry. Computationally designed ArmRPs (dArmRPs) had to be stabilized by mutations to remove regions of higher flexibility, which were identified by molecular dynamics simulations in explicit solvent. Using an N-capping repeat from the consensus-design approach, two different crystal structures of dArmRP were determined. Although the experimental structures of dArmRP deviated from the designed curvature, the insertion of the most conserved binding pockets of natural ArmRPs onto the surface of dArmRPs resulted in binders against the expected peptide with low nanomolar affinities, similar to the binders from the consensus-design series.

Flexible binding simulation by a novel and improved version of virtual-system coupled adaptive umbrella sampling

Virtual-system coupled adaptive umbrella sampling (VAUS) enhances sampling along a reaction coordinate by using a virtual degree of freedom. However, VAUS and regular adaptive umbrella sampling (AUS) methods are yet computationally expensive. To decrease the computational burden further, improvements of VAUS for all-atom explicit solvent simulation are presented here. The improvements include probability distribution calculation by a Markov approximation; parameterization of biasing forces by iterative polynomial fitting; and force scaling. These when applied to study Ala-pentapeptide dimerization in explicit solvent showed advantage over regular AUS. By using improved VAUS larger biological systems are amenable.

The SAMPL5 host-guest challenge: computing binding free energies andenthalpies from explicit solvent simulations by the attach-pull-release (APR) method.

The absolute binding free energies and binding enthalpies of twelve host-guest systems in the SAMPL5 blind challenge were computed using our attach-pull-release (APR) approach. This method has previously shown good correlations between experimental and calculated binding data in retrospective studies of cucurbit[7]uril (CB7) and β-cyclodextrin (βCD) systems. In the present work, the computed binding free energies for host octa acid (OA or OAH) and tetra-endo-methyl octa-acid (TEMOA or OAMe) with guests are in good agreement with prospective experimental data, with a coefficient of determination (R2) of 0.8 and root-mean-squared error of 1.7kcal/mol using the TIP3P water model. The binding enthalpy calculations achieve moderate correlations, with R2 of 0.5 and RMSE of 2.5kcal/mol, for TIP3P water. Calculations using the newly developed OPC water model also show good performance. Furthermore, the present calculations semi-quantitatively capture the experimental trend of enthalpy-entropy compensation observed, and successfully predict guests with the strongest and weakest binding affinity. The most populated binding poses of all twelve systems, based on clustering analysis of 750ns molecular dynamics (MD) trajectories, were extracted and analyzed. Computational methods using MD simulations and explicit solvent models in a rigorous statistical thermodynamic framework, like APR, can generate reasonable predictions of binding thermodynamics. Especially with continuing improvement in simulation force fields, such methods hold the promise of making substantial contributions to hit identification and lead optimization in the drug discovery process.

Mechanism of pyrophosphate ion release in T7 RNA polymerase revealed by free energy simulations

In this article, we explored pyrophosphate ion (PPi) release mechanism in T7 RNAP using all-atom free energy simulations in explicit solvent. Steered Molecular Dynamics (SMD) and Umbrella Sampling (US) methods were combined to estimate the potential of mean force (PMF) for the PPi release in T7 RNAP. PPi is a small group with negative charges. After binding a Mg2+ ion, the formed (Mg-PPi)2− complex is able to bind with both positively and negatively charged groups. Our simulation results show there is a large free energy barrier in the PMF profile for PPi release. Non-bonded interaction analysis suggests that the PPi is firmly trapped in the pore region of the secondary channel, which is composed of many polar, negatively and positively charged groups. Strong electric interactions from the surrounding groups contribute mostly to the high energy barrier for PPi release. Hydrogen bond map clearly exhibits part of the interaction network that the PPi has been trapped in. Therefore, we conclude that PPi is less likely to be released spontaneously. Additional energy input is needed to facilitate this process. It is deduced that NTP addition reaction may serve as an energy provider to drive PPi release. Then the whole NAC mechanism can be well explained. It is expected that our study provides a clear picture on the essential step of RNA transcription at atom-level resolution.

Calculating distribution coefficients based on multi-scale free energy simulations: an evaluation of MM and QM/MM explicit solvent simulations of water-cyclohexane transfer in the SAMPL5 challenge.

One of the central aspects of biomolecular recognition is the hydrophobic effect, which is experimentally evaluated by measuring the distribution coefficients of compounds between polar and apolar phases. We use our predictions of the distribution coefficients between water and cyclohexane from the SAMPL5 challenge to estimate the hydrophobicity of different explicit solvent simulation techniques. Based on molecular dynamics trajectories with the CHARMM General Force Field, we compare pure molecular mechanics (MM) with quantum-mechanical (QM) calculations based on QM/MM schemes that treat the solvent at the MM level. We perform QM/MM with both density functional theory (BLYP) and semi-empirical methods (OM1, OM2, OM3, PM3). The calculations also serve to test the sensitivity of partition coefficients to solute polarizability as well as the interplay of the quantum-mechanical region with the fixed-charge molecular mechanics environment. Our results indicate that QM/MM with both BLYP and OM2 outperforms pure MM. However, this observation is limited to a subset of cases where convergence of the free energy can be achieved.