Substance P analogs devoid of key residues fail to activate human mast cells via MRGPRX2.

Mast cells play an important role in disease pathogenesis by secreting immunomodulatory molecules. Mast cells are primarily activated by the crosslinking of their high affinity IgE receptors (FcεRI) by antigen bound immunoglobulin (Ig)E antibody complexes. However, mast cells can also be activated by the mas related G protein-coupled receptor X2 (MRGPRX2), in response to a range of cationic secretagogues, such as substance P (SP), which is associated with pseudo-allergic reactions. We have previously reported that the in vitro activation of mouse mast cells by basic secretagogues is mediated by the mouse orthologue of the human MRGPRX2, MRGPRB2. To further elucidate the mechanism of MRGPRX2 activation, we studied the time-dependent internalization of MRGPRX2 by human mast cells (LAD2) upon stimulation with the neuropeptide SP. In addition, we performed computational studies to identify the intermolecular forces that facilitate ligand-MRGPRX2 interaction using SP. The computational predictions were tested experimentally by activating LAD2 with SP analogs, which were missing key amino acid residues. Our data suggest that mast cell activation by SP causes internalization of MRGPRX2 within 1 min of stimulation. Hydrogen bonds (h-bonds) and salt bridges govern the biding of SP to MRGPRX2. Arg1 and Lys3 in SP are key residues that are involved in both h-bonding and salt bridge formations with Glu164 and Asp184 of MRGPRX2, respectively. In accordance, SP analogs devoid of key residues (SP1 and SP2) failed to activate MRGPRX2 degranulation. However, both SP1 and SP2 caused a comparable release of chemokine CCL2. Further, SP analogs SP1, SP2 and SP4 did not activate tumor necrosis factor (TNF) production. We further show that SP1 and SP2 limit the activity of SP on mast cells. The results provide important mechanistic insight into the events that result in mast cell activation through MRGPRX2 and highlight the important physiochemical characteristics of a peptide ligand that facilitates ligand-MRGPRX2 interactions. The results are important in understanding activation through MRGPRX2, and the intermolecular forces that govern ligand-MRGPRX2 interaction. The elucidation of important physiochemical properties within a ligand that are needed for receptor interaction will aid in designing novel therapeutics and antagonists for MRGPRX2.

Understanding the Liquid States of Cyclic Hydrocarbons Containing N, O, and S Atoms via the 3D-RISM-KH Molecular Solvation Theory.

The 3D-reference interaction site model (3D-RISM) molecular solvation theory in combination with the Kovalenko-Hirata (KH) closure is extended to seven heterocyclic liquids to understand their liquid states and to test the performance of the theory in solvation free energy (SFE) calculations of solutes in select solvents. The computed solvent site distribution profiles were compared with the all-atom molecular dynamics (MD) simulations, showing comparable performances. The computational results were compared against the structural parameters for liquids, whenever available, as well as against the experimental SFEs. The liquids are found to have local ordered structures held together via weak interactions in both the RISM and MD simulations. The 3D-RISM-KH computed SFEs are in good agreement with the benchmark values for the tetrahydrothiophene-S,S-dioxide, and showed comparatively larger deviations in the case of the SFEs in the tetrahydrofuran continuum.

Computational Investigation of the Metal and Ligand Substitution Effects on the Structure and Electronic States of the Phosphoranimide Tetramer Complexes of Cu(I), Ni(I), Co(I), and Fe(I).

The structurally unique saddle-shaped paramagnetic tetrametallic clusters of Co(I) and Ni(I) with phosphoranimide ligands have been synthesized and proposed as catalyst precursors. The analogous Cu(I) nanocluster is planar and diamagnetic. These notable variations in geometry and ground electronic states indicate that the effect of metal and ligand substituents on the structure and electronic properties of these complexes requires investigation. We present a computational study of a series of these novel homoleptic complexes containing Co(I), Ni(I), and Cu(I) as well as Fe(I) coordinated to phosphoranimides with electron-donating and withdrawing substituents, conducted at the relativistic density functional theory level using ZORA-PBE/TZP. The optimized structures of the saddle-shaped Co(I) and Ni(I) and planar Cu(I) tetramers with linear N-M-N coordination are validated with respect to X-ray diffraction determinations. The ground-state analysis indicates that Cu(I) complexes are diamagnetic, whereas Ni(I) and Co(I) complexes are in high-spin states, in agreement with magnetic susceptibility measurements. The computational results show that Fe(I) complexes are saddle shaped and high spin. The Co(I) complex is stabilized by a metal macrocycle distortion from square to diamond, as elucidated from its Walsh diagram. The effects of metals and ligand substituents on the ground electronic state, metal center coordination environment, and energy of the complexes are investigated. The bulky tertiary butyl substituent causes the largest saddle-shape distortion of the tetramer marcocycle, which partially offsets its electron-donating effect. Macrocycle distortions with N-M-N site angles ranging from obtuse to alternating obtuse reflex are correlated with the increasing number of unpaired electrons. The phenyl-substituted complexes are expected to have the highest reactivity toward electrophiles. Understanding the interplay between structural and electronic parameters is intended to guide the development of synthetic cooperative systems for multielectron redox reactions, models of biological systems, and molecular magnets.

Response to Comment on "Density Functional Theory and 3D-RISM-KH molecular theory of solvation studies of CO2 reduction on Cu-, Cu2O-, Fe-, and Fe3O4-based nanocatalysts".

In response to the Comment on "Density Functional Theory and 3D-RISM-KH molecular theory of solvation studies of CO2 reduction on Cu-, Cu2O-, Fe-, and Fe3O4-based nanocatalysts" (Gusarov J Mol Model 27:344-344, 1), the behavior of a CO* molecule on a Cu21 nanocatalyst slab without a solution considered in the Comment is considerably different from our case of this system in 1.0 Mol KH2PO4 ambient aqueous solution. Moreover, our calculations for CO* on Cu21 without a solution that we presented in our article are similar to those shown in the Comment. The Comment and its conclusions are controversial and should be treated with much caution.

Biomolecular Simulations with the Three-Dimensional Reference Interaction Site Model with the Kovalenko-Hirata Closure Molecular Solvation Theory.

The statistical mechanics-based 3-dimensional reference interaction site model with the Kovalenko-Hirata closure (3D-RISM-KH) molecular solvation theory has proven to be an essential part of a multiscale modeling framework, covering a vast region of molecular simulation techniques. The successful application ranges from the small molecule solvation energy to the bulk phase behavior of polymers, macromolecules, etc. The 3D-RISM-KH successfully predicts and explains the molecular mechanisms of self-assembly and aggregation of proteins and peptides related to neurodegeneration, protein-ligand binding, and structure-function related solvation properties. Upon coupling the 3D-RISM-KH theory with a novel multiple time-step molecular dynamic (MD) of the solute biomolecule stabilized by the optimized isokinetic Nosé-Hoover chain thermostat driven by effective solvation forces obtained from 3D-RISM-KH and extrapolated forward by generalized solvation force extrapolation (GSFE), gigantic outer time-steps up to picoseconds to accurately calculate equilibrium properties were obtained in this new quasidynamics protocol. The multiscale OIN/GSFE/3D-RISM-KH algorithm was implemented in the Amber package and well documented for fully flexible model of alanine dipeptide, miniprotein 1L2Y, and protein G in aqueous solution, with a solvent sampling rate ~150 times faster than a standard MD simulation in explicit water. Further acceleration in computation can be achieved by modifying the extent of solvation layers considered in the calculation, as well as by modifying existing closure relations. This enhanced simulation technique has proven applications in protein-ligand binding energy calculations, ligand/solvent binding site prediction, molecular solvation energy calculations, etc. Applications of the RISM-KH theory in molecular simulation are discussed in this work.

Cloning and high-level expression of monomeric human superoxide dismutase 1 (SOD1) and its interaction with pyrimidine analogs.

Superoxide dismutase 1 (SOD1) is known to be involved in the pathogenesis of Amyotrophic Lateral Sclerosis (ALS) and is therefore considered to be an important ALS drug target. Identifying potential drug leads that bind to SOD1 and characterizing their interactions by nuclear magnetic resonance (NMR) spectroscopy is complicated by the fact that SOD1 is a homodimer. Creating a monomeric version of SOD1 could alleviate these issues. A specially designed monomeric form of human superoxide dismutase (T2M4SOD1) was cloned into E. coli and its expression significantly enhanced using a number of novel DNA sequence, leader peptide and growth condition optimizations. Uniformly 15N-labeled T2M4SOD1 was prepared from minimal media using 15NH4Cl as the 15N source. The T2M4SOD1 monomer (both 15N labeled and unlabeled) was correctly folded as confirmed by 1H-NMR spectroscopy and active as confirmed by an in-gel enzymatic assay. To demonstrate the utility of this new SOD1 expression system for NMR-based drug screening, eight pyrimidine compounds were tested for binding to T2M4SOD1 by monitoring changes in their 1H NMR and/or 19F-NMR spectra. Weak binding to 5-fluorouridine (FUrd) was observed via line broadening, but very minimal spectral changes were seen with uridine, 5-bromouridine or trifluridine. On the other hand, 1H-NMR spectra of T2M4SOD1 with uracil or three halogenated derivatives of uracil changed dramatically suggesting that the pyrimidine moiety is the crucial binding component of FUrd. Interestingly, no change in tryptophan 32 (Trp32), the putative receptor for FUrd, was detected in the 15N-NMR spectra of 15N-T2M4SOD1 when mixed with these uracil analogs. Molecular docking and molecular dynamic (MD) studies indicate that interaction with Trp32 of SOD1 is predicted to be weak and that there was hydrogen bonding with the nearby aspartate (Asp96), potentiating the Trp32-uracil interaction. These studies demonstrate that monomeric T2M4SOD1 can be readily used to explore small molecule interactions via NMR.

Predicting PAMPA permeability using the 3D-RISM-KH theory: are we there yet?

The parallel artificial membrane permeability assay (PAMPA), a non-cellular lab-based assay, is extensively used to measure the permeability of pharmaceutical compounds. PAMPA experiments provide a working mimic of a molecule passing through cells and PAMPA values are widely used to estimate drug absorption parameters. There is an increased interest in developing computational methods to predict PAMPA permeability values. We developed an in silico model to predict the permeability of compounds based on the PAMPA assay. We used the three-dimensional reference interaction site model (3D-RISM) theory with the Kovalenko-Hirata (KH) closure to calculate the excess chemical potentials of a large set of compounds and predicted their apparent permeability with good accuracy (mean absolute error or MAE = 0.69 units) when compared to a published experimental data set. Furthermore, our in silico PAMPA protocol performed very well in the binary prediction of 288 compounds as being permeable or impermeable (precision = 94%, accuracy = 93%). This suggests that our in silico protocol can mimic the PAMPA assay and could aid in the rapid discovery or screening of potentially therapeutic drug leads that can be delivered to a desired tissue.

Density functional theory and 3D-RISM-KH molecular theory of solvation studies of CO2 reduction on Cu-, Cu2O-, Fe-, and Fe3O4-based nanocatalysts.

Using OpenMX quantum chemistry software for self-consistent field calculations of electronic structure with geometry optimization and 3D-RISM-KH molecular theory of solvation for 3D site distribution functions and solvation free energy, we modeled the reduction of CO2+H2 in ambient aqueous electrolyte solution of 1.0-M KH2PO4 into (i) formic acid HCOOH and (ii) CO H2O on the surfaces of Cu-, Fe-, Cu2O-, and Fe3O4-based nanocatalysts. It is applicable to its further reduction to hydrocarbons. The optimized geometries and free energies were obtained for the pathways of adsorption of the reactants from the solution, successive reduction on the surfaces of the nanocatalysts, and then release back to the solution bulk.

Application of the Approximate 3D-Reference Interaction Site Model (RISM) Molecular Solvation Theory to Acetonitrile as Solvent.

The integral equation formalism based reference interaction site model (RISM) molecular solvation theory is applied to pure liquid acetonitrile and water-acetonitrile binary mixtures of different compositions. Solvate formation of d- and f-block ions by ACN is also calculated to check applicability of the RISM theory. The generalized Amber force field (GAFF) and the universal force field (UFF) parameters were found to be suitable for applications with the RISM theory for acetonitrile solvent. The presence of local microsolvated clusters for water-acetonitrile mixtures is validated in the RISM calculations.

Molecular interactions between monoclonal oligomer-specific antibody 5E3 and its amyloid beta cognates.

Oligomeric amyloid ß (Aß) is currently considered the most neurotoxic form of the Aß peptide implicated in Alzheimer's disease (AD). The molecular structures of the oligomers have remained mostly unknown due to their transient nature. As a result, the molecular mechanisms of interactions between conformation-specific antibodies and their Aß oligomer (AßO) cognates are not well understood. A monoclonal conformation-specific antibody, m5E3, was raised against a structural epitope of Aß oligomers. m5E3 binds to AßOs with high affinity, but not to Aß monomers or fibrils. In this study, a computational model of the variable fragment (Fv) of the m5E3 antibody (Fv5E3) is introduced. We further employ docking and molecular dynamics simulations to determine the molecular details of the antibody-oligomer interactions, and to classify the AßOs as Fv5E3-positives and negatives, and to provide a rationale for the low affinity of Fv5E3 for fibrils. This information will help us to perform site-directed mutagenesis on the m5E3 antibody to improve its specificity and affinity toward oligomeric Aß species. We also provide evidence for the possible capability of the m5E3 antibody to disaggregate AßOs and to fragment protofilaments.

Enhanced solvation force extrapolation for speeding up molecular dynamics simulations of complex biochemical liquids.

We propose an enhanced approach to the extrapolation of mean potential forces acting on atoms of solute macromolecules due to their interactions with solvent atoms in complex biochemical liquids. It improves and extends our previous extrapolation schemes by additionally including new techniques such as an exponential scaling transformation of coordinate space with weights complemented by an automatically adjusted balancing between the least square minimization of force deviations and the norm of expansion coefficients in the approximation. The expensive mean potential forces are treated in terms of the three-dimensional reference interaction site model with Kovalenko-Hirata closure molecular theory of solvation. During the dynamics, they are calculated only after every long (outer) time interval, i.e., quite rarely to reduce the computational costs. At much shorter (inner) time steps, these forces are extrapolated on the basis of their outer values. The equations of motion are then solved using a multiple time step integration within an optimized isokinetic Nosé-Hoover chain thermostat. The new approach is applied to molecular dynamics simulations of various systems consisting of solvated organic and biomolecules of different complexity. For example, we consider hydrated alanine dipeptide, asphaltene in toluene solvent, miniprotein 1L2Y, and protein G in aqueous solution. It is shown that in all these cases, the enhanced extrapolation provides much better accuracy of the solvation force approximation than the existing approaches. As a result, it can be used with much larger outer time steps, leading to a significant speedup of the simulations.

Prediction of P-glycoprotein inhibitors with machine learning classification models and 3D-RISM-KH theory based solvation energy descriptors.

Development of novel in silico methods for questing novel PgP inhibitors is crucial for the reversal of multi-drug resistance in cancer therapy. Here, we report machine learning based binary classification schemes to identify the PgP inhibitors from non-inhibitors using molecular solvation theory with excellent accuracy and precision. The excess chemical potential and partial molar volume in various solvents are calculated for PgP± (PgP inhibitors and non-inhibitors) compounds with the statistical-mechanical based three-dimensional reference interaction site model with the Kovalenko-Hirata closure approximation (3D-RISM-KH molecular theory of solvation). The statistical importance analysis of descriptors identified the 3D-RISM-KH based descriptors as top molecular descriptors for classification. Among the constructed classification models, the support vector machine predicted the test set of Pgp± compounds with highest accuracy and precision of ~ 97% for test set. The validation of models confirms the robustness of state-of-the-art molecular solvation theory based descriptors in identification of the Pgp± compounds.

The role of hydration effects in 5-fluorouridine binding to SOD1: insight from a new 3D-RISM-KH based protocol for including structural water in docking simulations.

Misfolded Cu/Zn superoxide dismutase enzyme (SOD1) shows prion-like propagation in neuronal cells leading to neurotoxic aggregates that are implicated in amyotrophic lateral sclerosis (ALS). Tryptophan-32 (W32) in SOD1 is part of a potential site for templated conversion of wild type SOD1. This W32 binding site is located on a convex, solvent exposed surface of the SOD1 suggesting that hydration effects can play an important role in ligand recognition and binding. A recent X-ray crystal structure has revealed that 5-Fluorouridine (5-FUrd) binds at the W32 binding site and can act as a pharmacophore scaffold for the development of anti-ALS drugs. In this study, a new protocol is developed to account for structural (non-displaceable) water molecules in docking simulations and successfully applied to predict the correct docked conformation binding modes of 5-FUrd at the W32 binding site. The docked configuration is within 0.58 Å (RMSD) of the observed configuration. The docking protocol involved calculating a hydration structure around SOD1 using molecular theory of solvation (3D-RISM-KH, 3D-Reference Interaction Site Model-Kovalenko-Hirata) whereby, non-displaceable water molecules are identified for docking simulations. This protocol was also used to analyze the hydrated structure of the W32 binding site and to explain the role of solvation in ligand recognition and binding to SOD1. Structural water molecules mediate hydrogen bonds between 5-FUrd and the receptor, and create an environment favoring optimal placement of 5-FUrd in the W32 binding site.

Application of the 3D-RISM-KH molecular solvation theory for DMSO as solvent.

The molecular solvation theory in the form of the Three-Dimensional Reference Interaction Site Model (3D-RISM) with Kovalenko-Hirata (KH) closure relation is benchmarked for use with dimethyl sulfoxide (DMSO) as solvent for (bio)-chemical simulation within the framework of integral equation formalism. Several force field parameters have been tested to correctly reproduce solvation free energy in DMSO, ion solvation in DMSO, and DMSO coordination prediction. Our findings establish a united atom (UA) type parameterization as the best model of DMSO for use in 3D-RISM-KH theory based calculations.

To Pass or Not To Pass: Predicting the Blood-Brain Barrier Permeability with the 3D-RISM-KH Molecular Solvation Theory.

Predicting the ability of chemical species to cross the blood-brain barrier (BBB) is an active field of research for development and mechanistic understanding in the pharmaceutical industry. Here, we report the BBB permeability of a large data set of compounds by incorporating molecular solvation energy descriptors computed by the 3D-RISM-KH molecular solvation theory. We have been able to show, for the first time, that the computed excess chemical potential in different solvents can be successfully used to predict permeability of compounds in a binary manner (yes/no) via a minimum-descriptor-based model. Our findings successfully combine the molecular solvation theory with the machine learning approach to address one of the most daunting challenges in predictive structure-activity relationship modeling. The workflow presented in this work is simple enough to be used by nonexperts with ease.

A molecular reconstruction approach to site-based 3D-RISM and comparison to GIST hydration thermodynamic maps in an enzyme active site.

Computed, high-resolution, spatial distributions of solvation energy and entropy can provide detailed information about the role of water in molecular recognition. While grid inhomogeneous solvation theory (GIST) provides rigorous, detailed thermodynamic information from explicit solvent molecular dynamics simulations, recent developments in the 3D reference interaction site model (3D-RISM) theory allow many of the same quantities to be calculated in a fraction of the time. However, 3D-RISM produces atomic-site, rather than molecular, density distributions, which are difficult to extract physical meaning from. To overcome this difficulty, we introduce a method to reconstruct molecular density distributions from atomic-site density distributions. Furthermore, we assess the quality of the resulting solvation thermodynamics density distributions by analyzing the binding site of coagulation Factor Xa with both GIST and 3D-RISM. We find good qualitative agreement between the methods for oxygen and hydrogen densities as well as direct solute-solvent energetic interactions. However, 3D-RISM predicts lower energetic and entropic penalties for moving water from the bulk to the binding site.

Predicting skin permeability using the 3D-RISM-KH theory based solvation energy descriptors for a diverse class of compounds.

The state-of-the-art molecular solvation theory is used to predict skin permeability of a large set of compounds with available experimental skin permeability coefficient (logKP). Encouraging results are obtained pointing to applicability of a novel quantitative structure activity model that uses statistical physics based 3D-RISM-KH theory for solvation free energy calculations as a primary descriptor for the prediction of logKP with relative mean square error of 0.77 units.

Performance of 3D-RISM-KH in Predicting Hydration Free Energy: Effect of Solute Parameters.

The three-dimensional reference interaction site model molecular solvation theory with the Kovalenko-Hirata closure relation has been shown to produce excellent solvation characteristics for a large class of (bio)chemical systems in solution. Correct calculation of hydration free energy is central to successful application of any solvation model. In order to find out the best possible force-field parameters to be used for hydration free energy calculation with the aforementioned theory, we have developed an extended database containing a large number of experimental solvation free energies available in the current literature and used a plethora of theoretical models for assessment. The general Amber force field was found to perform satisfactorily, whereas special care should be taken in solute charge assignment with the universal force field.

Solvent Composition Effects on the Structural Properties of the Aß42 Monomer from the 3D-RISM-KH Molecular Theory of Solvation.

Structural characterization of amyloid (A)ß peptides implicated in Alzheimer's disease is a challenging problem due to their intrinsically disordered nature and their high propensity for aggregation. Only limited information is currently available from experiments on conformational properties and aggregation pathways of the peptides in cellular environments. In silico modeling complements experimental information, providing atomistic insight into structure and dynamics of different Aß species. All-atom explicit solvent molecular dynamics (MD) simulations with a properly selected force field can deliver reliable structural and dynamic information. In the case of intrinsically disordered Aß peptides, enhanced sampling simulations beyond the nanosecond time scale are required to obtain statistically meaningful results even for simple solvent conditions. To overcome the challenges of conformational sampling in crowded cellular environments, alternative approaches have to be used, including postprocessing of MD data. In this study, we employ the statistical-mechanical, three-dimensional reference interaction site model with the Kovalenko-Hirata closure integral equation molecular theory of solvation to describe solvent composition effects on the conformational equilibrium in a structural ensemble of the Aß42 (covering residues 1-42) monomer based on a statistical reweighting technique. The methodology enables a computationally efficient prediction on how different factors in the cellular environment, such as solvent composition, nonpolar solvation, and macromolecular crowding, affect the structural properties of the monomer. Similarities have been identified between changes in the structural ensemble caused by nonpolar solvation and crowded environments modeled by ionic solution with large negative ions. In particular, both solvent conditions reduce the random coil content and enhance the helical structure content of the monomer. In contrast to the previous studies, which reported increased α-helical content of peptides in crowded environments, this work attributes these structural features to the difference in solvent exposure of hydrophilic residues of the monomer for different secondary structure elements, rather than to (entropic) excluded volume effects.

Tryptophan 32 mediates SOD1 toxicity in a in vivo motor neuron model of ALS and is a promising target for small molecule therapeutics.

SOD1 misfolding, toxic gain of function, and spread are proposed as a pathological basis of amyotrophic lateral sclerosis (ALS), but the nature of SOD1 toxicity has been difficult to elucidate. Uniquely in SOD1 proteins from humans and other primates, and rarely in other species, a tryptophan residue at position 32 (W32) is predicted to be solvent exposed and to participate in SOD1 misfolding. We hypothesized that W32 is influential in SOD1 acquiring toxicity, as it is known to be important in template-directed misfolding. We tested if W32 contributes to SOD1 cytotoxicity and if it is an appropriate drug target to ameliorate ALS-like neuromuscular deficits in a zebrafish model of motor neuron axon morphology and function (swimming). Embryos injected with human SOD1 variant with W32 substituted for a serine (SOD1W32S) had reduced motor neuron axonopathy and motor deficits compared to those injected with wildtype or disease-associated SOD1. A library of FDA-approved small molecules was ranked with virtual screening based on predicted binding to W32, and subsequently filtered for analogues using a pharmacophore model based on molecular features of the uracil moiety of a small molecule previously predicted to interact with W32 (5'-fluorouridine or 5'-FUrd). Along with testing 5'-FUrd and uridine, a lead candidate from this list was selected based on its lower toxicity and improved blood brain barrier penetrance; telbivudine significantly rescued SOD1 toxicity in a dose-dependent manner. The mechanisms whereby the small molecules ameliorated motor neuron phenotypes were specifically mediated through human SOD1 and its residue W32, because these therapeutics had no measurable impact on the effects of UBQLN4D90A, EtOH, or tryptophan-deficient human SOD1W32S. By substituting W32 for a more evolutionarily conserved residue (serine), we confirmed the significant influence of W32 on human SOD1 toxicity to motor neuron morphology and function; further, we performed pharmaceutical targeting of the W32 residue for rescuing SOD1 toxicity. This unique residue offers future novel insights into SOD1 stability and toxic gain of function, and therefore poses an potential target for drug therapy.