Ab Initio Molecular Dynamics Simulations of Amino Acids in Aqueous Solutions: Estimating pKa Values from Metadynamics Sampling

Advantages of Multidimensional Biasing in Accelerated Dynamics: Application to the Calculation of the Acid pKa for Acetic Acid

The use of accelerated sampling methods such as metadynamics has shown a significant advantage in calculations that involve infrequent events, which would otherwise require sampling a prohibitive number of configurations to determine the difference in free energies between two or more chemically distinct states such as in the calculation of acid dissociation constants Ka. In this case, the most common method is to bias the system via a single collective variable (CV) representing the coordination number of the proton donor group, which yields results in reasonable agreement with experiments. Here we study the deprotonation of acetic acid using the reactive force field ReaxFF and observe a significant dependence of Ka on the simulation box size when biasing only the coordination number CV, which is due to incomplete sampling of the deprotonated state for small simulation systems and inefficient sampling for larger ones. Incorporating a second CV representing the distance between the H3O+ cation and the acetate anion results in substantially more efficient sampling, both accelerating the dynamics and virtually eliminating the computational box size dependence.

pH- and Facet-Dependent Surface Chemistry of TiO2 in Aqueous Environment from First Principles

TiO₂ is a relevant catalytic material, and its chemistry in aqueous environment is a challenging aspect to address. Also, the morphology of TiO₂ particles at the nanoscale is often complex, spanning from faceted to spherical. In this work, we study the pH- and facet-dependent surface chemistry of TiO₂/water interfaces by performing ab initio molecular dynamics simulations with the grand canonical formulation of species in solution. We first determined the acid-base equilibrium constants at the interface, which allows us to estimate the pH at the point of zero charge, an important experimental observable. Then, based on simulated equilibrium constants, we predict the amount of H⁺, OH^-, and adsorbed H₂O species present on the surfaces as a function of the pH, a relevant aspect for water splitting semi-reactions. We approximated the complex morphology of TiO₂ particles by considering the rutile (110) and (011), and anatase (101), (001), and (100) surfaces.

Predicting Solvent Effects on SN2 Reaction Rates: Comparison of QM/MM, Implicit, and MM Explicit Solvent Models

Solvents are one of the key variables in the optimization of a synthesis yield or properties of a synthesis product. In this paper, contemporary solvent models are applied to predict the rates of S_N2 reactions in a range of aqueous and non-aqueous solvents. High-level CCSD(T)/CBS//M06-2X/6-31+G(d) gas phase energies were combined with solvation free energies from SMD, SM12, and ADF-COSMO-RS continuum solvent models, as well as molecular mechanics (MM) explicit solvent models with different atomic charge schemes to predict the rate constants of three S_N2 reactions in eight protic and aprotic solvents. It is revealed that the prediction of rate constants in organic solvents is not necessarily less challenging than in water and popular solvent models struggle to predict their rate constants to within 3 log units of experimental values. Among the continuum solvent models, the ADF-COSMO-RS model performed the best in predicting absolute rate contants while the SM12 model was best at predicting relative rate constants with an average accuracy of about 1.5 and 0.8 log units, respectively. The use of computationally more demanding MM explicit solvent models did not translate to improvements in absolute rate constants but was quite effective at predicting relative rate constants due to systematic error cancellation. Free energy barriers obtained from umbrella sampling with explicit solvent QM/MM simulations led to excellent agreement with experimental values, provided that a validated level of theory is used to treat the QM region.

Advanced Theory and Simulation to Guide the Development of CO2 Capture Solvents

Increasing atmospheric concentrations of greenhouse gases due to industrial activity have led to concerning levels of global warming. Reducing carbon dioxide (CO₂) emissions, one of the main contributors to the greenhouse effect, is key to mitigating further warming and its negative effects on the planet. CO₂ capture solvent systems are currently the only available technology deployable at scales commensurate with industrial processes. Nonetheless, designing these solvents for a given application is a daunting task requiring the optimization of both thermodynamic and transport properties. Here, we discuss the use of atomic scale modeling for computing reaction energetics and transport properties of these chemically complex solvents. Theoretical studies have shown that in many cases, one is dealing with a rich ensemble of chemical species in a coupled equilibrium that is often difficult to characterize and quantify by experiment alone. As a result, solvent design is a balancing act between multiple parameters which have optimal zones of effectiveness depending on the operating conditions of the application. Simulation of reaction mechanisms has shown that CO₂ binding and proton transfer reactions create chemical equilibrium between multiple species and that the agglomeration of resulting ions and zwitterions can have profound effects on bulk solvent properties such as viscosity. This is balanced against the solvent systems needing to perform different functions (e.g., CO₂ uptake and release) depending on the thermodynamic conditions (e.g., temperature and pressure swings). The latter constraint imposes a "Goldilocks" range of effective parameters, such as binding enthalpy and pK _a, which need to be tuned at the molecular level. The resulting picture is that solvent development requires an integrated approach where theory and simulation can provide the necessary ingredients to balance competing factors.

Quantum Chemistry Calculations for Metabolomics

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

Nuclear quantum effects on autoionization of water isotopologs studied by ab initio path integral molecular dynamics

In this study, we investigate the nuclear quantum effects (NQEs) on the acidity constant (pK_A) of liquid water isotopologs under the ambient condition by path integral molecular dynamics (PIMD) simulations. We compared simulations using a fully explicit solvent model with a classical polarizable force field, density functional tight binding, and ab initio density functional theory, which correspond to empirical, semiempirical, and ab initio PIMD simulations, respectively. The centroid variable with respect to the proton coordination number of a water molecule was restrained to compute the gradient of the free energy, which measures the reversible work of the proton abstraction for the quantum mechanical system. The free energy curve obtained by thermodynamic integration was used to compute the pK_A value based on probabilistic determination. This technique not only reproduces the pK_A value of liquid D₂O experimentally measured (14.86) but also allows for a theoretical prediction of the pK_A values of liquid T₂O and aqueous HDO and HTO, which are unknown due to their scarcity. It is also shown that the NQEs on the free energy curve can result in a downshift of 4.5 ± 0.9 pK_A units in the case of liquid water, which indicates that the NQEs plays an indispensable role in the absolute determination of pK_A. The results of this study can help inform further extensions into the calculation of the acidity constants of isotope substituted species with high accuracy.

Cracking a cancer code histone deacetylation in epigenetic: the implication from molecular dynamics simulations on efficacy assessment of histone deacetylase inhibitors

Epigenetic changes, histone acetylation and deacetylation in chromatin have been intensively studied due to their significance in regulating the gene expression. According to the type of tumor, the levels of histone deacetylases (HDAC) are varied. HDAC inhibitors are a new promising class of compounds that inhibit the proliferation of tumor cells. In this study, the inhibitory efficacy of some HDAC inhibitors such as vorinostat, panobinostat, abexinostat, belinostat, resminostat, dacinostat and pracinostat was studied using molecular dynamics simulation. The inhibitory efficacy was estimated by computing the enzyme's stability, positional stability of the individual amino acids and interaction energies of HDLP-inhibitor complexes. It is hoped that this investigation may improve our understanding of the atomic-level description of the inhibitor binding site and how the HDAC inhibitors change the environment of the enzyme's active site. The results obtained from the root-mean-square deviation, the radius of gyration, solvent-accessible surface area, root-mean-square fluctuation, stride server and Ramachandran plot have revealed that the stability of HDLP enzyme with vorinostat, panobinostat and abexinostat is higher than the other studied complexes. According to the calculated values for MM-PBSA, LIE, semi-LIE binding free energies and interaction energies, the stability of the HDLP enzyme varies as panobinostat > abexinostat > vorinostat where resminostat complex showed relatively low stability. The ligandability and drugability values also give the same trend as above. The findings revealed that the panobinostat and abexinostat are potential lead compounds as reference inhibitor vorinostat. Therefore, it is possible to use these drugs as HDAC inhibitors in clinical practices. Also, the outcomes of this study could be utilized to identify new inhibitors for clinical research.Communicated by Ramaswamy H. Sarma.

Fully Atomistic Multiscale Approach for pKa Prediction

The ionization state of titratable amino acids strongly affects proteins structure and functioning in a large number of biological processes. It is therefore essential to be able to characterize the pK_a of ionizable groups inside proteins and to understand its microscopic determinants in order to gain insights into many functional properties of proteins. A big effort has been devoted to the development of theoretical approaches for the prediction of deprotonation free energies, yet the accurate theoretical/computational calculation of pK_a values is recognized as a current challenge. A methodology based on a hybrid quantum/classical approach is here proposed for the computation of deprotonation free energies. The method is applied to calculate the pK_a of formic acid, methylammonium, and methanethiol, providing results in good agreement with the corresponding experimental estimates. The pK_a is also calculated for aspartic acid and lysine as single residues in solution and for three aspartic/glutamic acids inside a well-characterized protein: hen egg white lysozyme. While for small molecules the method is able to deal with multiple protonation states of all titratable groups, this becomes computationally very expensive for proteins. The calculated pK_a values for the single amino acids (except for the zwitterionic aspartic acid) and inside the protein display a systematic shift with respect to the experimental values that suggests that the fine balance between hydrophobic and polar interactions might be not accurately reproduced by the usual classical force-fields, thus affecting the computation of deprotonation free energies. The calculated pK_a shifts inside the protein are in good agreement with the corresponding experimental ones (within 1 pK_a unit), well reproducing the pK_a changes due to the protein environment even in the case of large pK_a shifts.

Hydration, Solvation, and Isomerization of Methylglyoxal at the Air/Water Interface: New Mechanistic Pathways

Aqueous-phase processing of methylglyoxal (MG) has been suggested to play a key role in the formation of secondary organic aerosols and catalyze particle growth in the atmosphere. However, the details of these processes remain speculative owing to the lack of a complete description of the physicochemical behavior of MG on atmospheric aerosols. Here, the solvation and hydrolysis of MG at the air/liquid water interface is studied via classical and first-principles molecular dynamics simulations combined with free-energy methods. Our results reveal that the polarity of the water solvent catalyzed the trans-to-cis isomerization of MG at the air/liquid water interface relative to the gas phase. Despite the presence of a hydrophobic group, MG often solvates with both the ketone and methyl groups parallel to the water interface. Analysis of the instantaneous water surface reveals that when MG is in the trans state, the methyl group repels interfacial water to maintain the planarity of the molecule, indicating that lateral and temporal inhomogeneities of interfacial environments are important for fully characterizing the solvation of MG. The counterintuitive behavior of the hydrophobic group is ascribed to a tendency to maximize the number of hydrogen bonds between MG and interfacial water while minimizing the torsional free energy. This drives MG hydration, and our simulations indicate that the formation of MG diol is catalyzed at the air/liquid water interface compared to the gas phase and occurs through nucleophilic attack of water on the carbonyl carbon.

Rapid and Accurate Prediction of pKa Values of C-H Acids Using Graph Convolutional Neural Networks

The ability to estimate the acidity of C-H groups within organic molecules in non-aqueous solvents is important in synthetic planning to correctly predict which protons will be abstracted in reactions such as alkylations, Michael additions, or aldol condensations. This Article describes the use of the so-called graph convolutional neural networks (GCNNs) to perform such predictions on the time scales of milliseconds and with accuracy comparing favorably with state-of-the-art solutions, including commercial ones. The crux of the method is to train GCNNs using descriptors that reflect not only topological but also chemical properties of atomic environments. The model is validated against adversarial controls, supplemented by the discussion of realistic synthetic problems (on which it correctly predicts the most acidic protons in >90% of cases), and accompanied by a Web application intended to aid the community in everyday synthetic planning.

The multiple dissociation constants of glutathione disulfide: interpreting experimental pH-titration curves with ab initio MD simulations

The hexapeptide glutathione disulfide (GSSG) has six ionizable groups with six associated dissociation constants. The experimentally measured pH-titration curve, however, does not exhibit the six corresponding equivalence points and bears little resemblance to standard textbook examples of acid-base pH-titration curves. The curve highlights the difficulties in determining multiple pKa values of polyprotic acids - typically proteins and peptides - from experiment. The six pKa values of GSSG can, however, be estimated using Car-Parrinello molecular dynamics (CPMD) simulations in conjunction with metadynamics sampling of the underlying free energy landscape of the dissociation reactions. Ab initio MD simulations were performed on a GSSG molecule solvated by 200 water molecules. Using the estimated pKa values the theoretical titration curve was calculated and found to be in good agreement with experiment. The results clearly highlight how dissociation constants estimated from ab initio MD simulations can facilitate the interpretation of the pH-titration curves of complex chemical and biological systems.

Predicting Mole-Fraction-Dependent Dissociation for Weak Acids

We demonstrate for formic and acetic acid dissolved in water as examples that the binary quantum cluster equilibrium (bQCE) approach can predict acid strengths over the whole range of acid concentrations. The acid strength increases in a complex rather than a simple way with increasing mole fraction of the acid from 0 to 0.7, reflecting the complex interplay between the dissociated ions or conjugate bases available as compared to the acid and water molecules. Furthermore, our calculated ion concentrations meet the experimental maximum of the conductivity with excellent agreement for acetic acid and satisfactorily for the formic acid/water mixture. As only a limited number of simple quantum-chemical calculations are required for the prediction, bQCE is clearly a valuable approach to access these quantities also in non-aqueous solutions. It is a highly valuable asset for predicting ionization processes in highly concentrated solutions, which are relevant for biological and chemical systems, as well as technological processes.

Development of constant-pH simulation methods in implicit solvent and applications in biomolecular systems

pH is a critical parameter for biological and technological systems directly related with electrical charges. It can give rise to peculiar electrostatic phenomena, which also makes them more challenging. Due to the quantum nature of the process, involving the forming and breaking of chemical bonds, quantum methods should ideally by employed. Nevertheless, due to the very large number of ionizable sites, different macromolecular conformations, salt conditions, and all other charged species, the CPU time cost simply becomes prohibitive for computer simulations, making this a quite complex problem. Simplified methods based on Monte Carlo sampling have been devised and will be reviewed here, highlighting the updated state-of-the-art of this field, advantages, and limitations of different theoretical protocols for biomolecular systems (proteins and nucleic acids). Following a historical perspective, the discussion will be associated with the applications to protein interactions with other proteins, polyelectrolytes, and nanoparticles.

Effect of protonation on the solvation structure of solute N-butylamine in an aprotic ionic liquid

Significant change in the solvation structure by protonation reaction in the ionic liquid [C₂mIm][TFSA].

Ab initio molecular dynamics investigation of structural, dynamic and spectroscopic aspects of Se(vi) species in the aqueous environment

Microscopic investigation of selenic acid in aqueous environment is carried out. Hydrogen bonding and spectroscopic signatures of HSeO₄⁻and SeO₄²⁻species are discussed.

Ab Initio MD Simulations of the Brønsted Acidity of Glutathione in Aqueous Solutions: Predicting pKa Shifts of the Cysteine Residue

The tripeptide glutathione (GSH) is one of the most abundant peptides and the major repository for nonprotein sulfur in both animal and plant cells. It plays a critical role in intracellular oxidative stress management by the reversible formation of glutathione disulfide with the thiol-disulfide pair acting as a redox buffer. The state of charge of the ionizable groups of GSH can influence the redox couple, and hence the pKa value of the cysteine residue of GSH is critical to its functioning. Here we report ab initio Car-Parrinello molecular dynamics simulations of glutathione solvated by 200 water molecules, all of which are considered in the simulation. We show that the free-energy landscape for the protonation-deprotonation reaction of the cysteine residue of GSH computed using metadynamics sampling provides accurate estimates of the pKa and correctly predicts the shift in the dissociation constant values as compared with the isolated cysteine amino acid.