Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field

Hydration free energy (HFE) calculations are often used to assess the performance of biomolecular force fields and the quality of assigned parameters. The AMOEBA polarizable force field moves beyond traditional pairwise additive models of electrostatics and may be expected to improve upon predictions of thermodynamic quantities such as HFEs over and above fixed point charge models. The recent SAMPL4 challenge evaluated the AMOEBA polarizable force field in this regard, but showed substantially worse results than those using the fixed point charge GAFF model. Starting with a set of automatically generated AMOEBA parameters for the SAMPL4 dataset, we evaluate the cumulative effects of a series of incremental improvements in parameterization protocol, including both solute and solvent model changes. Ultimately the optimized AMOEBA parameters give a set of results that are not statistically significantly different from those of GAFF in terms of signed and unsigned error metrics. This allows us to propose a number of guidelines for new molecule parameter derivation with AMOEBA, which we expect to have benefits for a range of biomolecular simulation applications such as protein ligand binding studie

Development and analysis of Tinker-OpenMM as a GPU-based free energy perturbation engine

The utilization of computational technologies for the lead optimization process is one of the biggest challenges in the computational chemistry field. In this dissertation, I describe the addition of GPU-based absolute and relative free energy calculation methods using polarizable force field AMOEBA to Tinker-OpenMM. I then proceed to test the capabilities of this platform by studying the binding free energy and binding structures of derivatives of the MELK inhibitor IN17. Also, I present the implementation of virial-based pressure control to the Tinker-OpenMM platform that is needed for performing isobaric simulations.Cellular and Molecular Biolog

Modeling RNA, protein, and synthetic molecules using coarse-grained and all-atom representations

The aim of computational chemistry is to depict and understand the dynamics and interactions of molecular systems. In addition to increased comprehension in the physical and life sciences, this insight yields important applications to therapeutic design and materials science. In computational chemistry, molecules can be modeled in a number of representations depending on the molecular system and phenomena of interest. In this work, both simplified, coarse-grained representations and all-atom representations are used to model the interactions of RNA, cucurbituril host-guest chemistry, and cadmium selenide quantum dot binding to the Src homology 3 domain. For RNA, a coarse-grained model was developed termed RACER (RnA CoarsE-gRained) to accurately predict RNA structure and folding free energy. After optimization to statistical potentials, RACER accurately predicted the structures of 14 RNAs with an average 4.15Å root mean square deviation (RMSD) to the experimental structure. Further, RACER captured the sequence-specific variation in folding free energy for a set of 6 RNA hairpins and 5 RNA duplexes, with a R² correlation of 0.96 to experiment. The binding free energies of a cucurbituril host with 14 guests were computed using a polarizable force field and the free energy techniques of Bennett acceptance ratio and the orthogonal space random walk. The polarizable force field captured binding accurately, yet unexpectedly, the orthogonal space random walk method converged slowly, albeit at still reduced computational expense to the Bennett acceptance ratio. Lastly, the nanotoxicity effects of trioctylphosphine oxide coated cadmium selenide quantum dots are investigated with the model Src homology 3 protein domain in complex with its native proline rich motif ligand. With increasing quantum dot concentration, there is an increasing preference for the quantum dots to bind to the proline rich motif active site, inhibiting Src homology 3 function.Biomedical Engineerin

Development of an Advanced Force Field for Water using Variational Energy Decomposition Analysis

Given the piecewise approach to modeling intermolecular interactions for force fields, they can be difficult to parameterize since they are fit to data like total energies that only indirectly connect to their separable functional forms. Furthermore, by neglecting certain types of molecular interactions such as charge penetration and charge transfer, most classical force fields must rely on, but do not always demonstrate, how cancellation of errors occurs among the remaining molecular interactions accounted for such as exchange repulsion, electrostatics, and polarization. In this work we present the first generation of the (many-body) MB-UCB force field that explicitly accounts for the decomposed molecular interactions commensurate with a variational energy decomposition analysis, including charge transfer, with force field design choices that reduce the computational expense of the MB-UCB potential while remaining accurate. We optimize parameters using only single water molecule and water cluster data up through pentamers, with no fitting to condensed phase data, and we demonstrate that high accuracy is maintained when the force field is subsequently validated against conformational energies of larger water cluster data sets, radial distribution functions of the liquid phase, and the temperature dependence of thermodynamic and transport water properties. We conclude that MB-UCB is comparable in performance to MB-Pol, but is less expensive and more transferable by eliminating the need to represent short-ranged interactions through large parameter fits to high order polynomials

GAFF/IPolQ-Mod+LJ-Fit: Optimized force field parameters for solvation free energy predictions

Rational drug design featuring explicit solubility considerations can greatly benefit from molecular dynamics simulations, as they allow for the prediction of the Gibbs free energy of solvation and thus relative solubilities. In our previous work (A. Mecklenfeld, G. Raabe. J. Chem. Theory Comput. 13 no. 12 (2017) 6266–6274), we have compared solvation free energy results obtained with the General Amber Force Field (GAFF) and its default restrained electrostatic potential (RESP) partial charges to those obtained by modified implicitly polarized charges (IPolQ-Mod) for an implicit representation of impactful polarization effects. In this work, we have adapted Lennard-Jones parameters for GAFF atom types in combination with IPolQ-Mod to further improve the accuracies of solvation free energy and liquid density predictions. We thereby focus on prominent atom types in common drugs. For the refitting, 357 respectively 384 systems were considered for free energies and densities and validation was performed for 142 free energies and 100 densities of binary mixtures. By the in-depth comparison of simulation results for default GAFF, GAFF with IPolQ-Mod and our new set of parameters, which we label GAFF/IPolQ-Mod+LJ-Fit, we can clearly highlight the improvements of our new model for the description of both relative solubilities and fluid phase behaviour.</p

Computational studies of protein-ligand recognition

Molecular recognition between biomolecules and ligands is very specific in living cells. The functions of all biochemical processes and cell mechanisms are dependent upon complex but specific non-covalent intermolecular interactions. As essential building blocks in protein and nucleic acid, phosphate groups are commonly found in nucleic acids, proteins, and lipids. Nearly half of known proteins have been shown to interact with ligands containing a phosphate group. Binding of a phosphoryl group is fundamental to a range of biological processes including metabolism, biosynthesis, gene regulation, signal transduction, muscle contraction, and antibiotic resistance. Phosphorylation is one of the most common forms of reversible posttranslational modification of protein and, nearly 30% of all proteins are phosphorylated on at least one residue in cells. However, phosphate binding sites are less well defined and fundamental principles of why and how proteins recognize phosphate groups are not yet fully understood. Molecular modeling is a common tool for studying biomolecular structure, dynamics, interaction and function. Due to the complex electrostatics, high concentration of ions and intricate interactions with environment, however, the modeling and designing of highly charged drug-like molecules and nucleic acid derivatives are extremely difficult. This thesis will focus on the highly charged phosphate, including its different protonation states, and energetic and thermodynamic driving forces behind protein-phosphate recognition. This thesis work will also discuss the development of more sophisticated computational models, AMOEBA+, that are necessary for a better understanding and prediction of the physical properties of small organic molecules. Four projects will be discussed in this dissertation: two projects on force field development, and two on applying molecular dynamic simulations to understand biological processes. These projects have led to new insights into understanding of physical and chemical principles and mechanisms underlying highly protein-phosphate binding and nucleic acid stability. In addition, this thesis work will enhance the capability to develop and apply computational and theoretical frameworks to model, predict and design proteins, therapeutics, and diagnostic strategies targeting phosphates, phosphate-containing biomoleculesBiomedical Engineerin

Semiclassical Vibrational Spectroscopy of Biological Molecules Using Force Fields

Semiclassical spectroscopy is a practical way to get an accurately approximate quantum description of spectral features starting from ab initio molecular dynamics simulations. The computational bottleneck for the method is represented by the cost of ab initio potential, gradient, and Hessian matrix estimates. This drawback is particularly severe for biological systems due to their unique complexity and large dimensionality. The main goal of this manuscript is to demonstrate that quantum dynamics and spectroscopy, at the level of semiclassical approximation, are doable even for sizable biological systems. To this end, we investigate the possibility of performing semiclassical spectroscopy simulations when ab initio calculations are replaced by computationally cheaper force field evaluations. Both polarizable (AMOEBABIO18) and nonpolarizable (AMBER14SB) force fields are tested. Calculations of some particular vibrational frequencies of four nucleosides, i.e., uridine, thymidine, deoxyguanosine, and adenosine, show that ab initio simulations are accurate and widely applicable. Conversely, simulations based on AMBER14SB are limited to harmonic approximations, but those relying on AMOEBABIO18 yield acceptable semiclassical values if the investigated conformation has been included in the force field parametrization. The main conclusion is that AMOEBABIO18 may provide a viable route to assist semiclassical spectroscopy in the study of large biological molecules for which an ab initio approach is not computationally affordable

ACCELERATED COMPUTING FOR MOLECULAR DYNAMICS SIMULATION

Molecular dynamics (MD) simulation serves as a computational microscope into the behavior of the biological and chemical macromolecules. At its core, MD models the interactions between atoms at various levels – force fields model the higher quantum level interactions using simpler physics-based models of interaction energies, while periodic boundary conditions model the bulk phase using lattice-based periodic copies of the simulation box. One limitation of the finite size of the simulation box seen during the simulation of membrane bilayers is the artifact of a chemical disequilibrium between the two layers as a drug molecule enters into the bilayer. We have tried to solve this problem by using a periodic boundary condition which has a half screw symmetry. Our results show that the method scales similar to the best-known method for the normal periodic boundary conditions. We have migrated CHARMM to an efficient implementation on the GPUs. These architectures provide thousands of cores on the same chip but require different programming model in order to use the underlying architecture. Our results show that the new CHARMM CUDA engine is efficient in time and accurate in precision. We have also participated in blind prediction challenges organized by SAMPL community to have a fair assessment of the computational chemistry tools. We developed a hybrid QM and MM technique to predict the pKa of drug-like molecules. It avoids the implicit solvent model used by quantum mechanical models and uses explicit solvent molecules. Since modeling explicit solvent molecules is difficult at QM level, they are modeled at the MM level instead. Thermodynamic cycle couples the aqueous Gibbs free energy of deprotonation to simpler components which can be modeled with higher accuracy. We also built a deep learning model to predict the logP of a set of drug-like molecules in a blind fashion. The generated model is robust over a large number of molecules, not just the ones that it was tested for in the SAMPL competition. We expect the method to be interesting for the drug design industry since lipophilicity of a molecule is important to be known even before it has been synthesized

Polarizable Force Field Development, and Applications to Conformational Sampling and Free Energy Calculation

The parameters of monovalent ions for the AMOEBA force field were revised. High level quantum mechanics results, relative solvation free energies of monovalent ions, lattice energies and lattice constants of salt crystals were used to calibrate the parameters. The revised parameters were validated against the quantum optimized structures and energies of ion-water dimers and ion-water clusters, and against thermodynamic properties of salt solutions at different concentrations measured in experiments, e.g. mean ionic activity coefficients, self-diffusion coefficients of water. In the simulations the sodium ion is found to qualitatively differ from larger cations in aqueous solution. Direct ionic interactions are predominant for potassium and larger cations, while sodium salt solutions at similar concentrations are dominated by ion-water interactions. A novel stochastic isokinetic integrator proposed by Tuckerman, et al. was extended and generalized in three respects. First, the Nos-Hoover chain algorithm was implemented in the original integrator. Next, the functional form of the isokinetic constraint was generalized so that it was no longer restricted to multiples of kBT. Finally, the isokinetic constraint was extended to be able to constrain the kinetic energies of multi-dimensional velocities, instead of only one degree of freedom as in its original form. An application of conformational sampling with molecular dynamics method, predictions of the binding free energies of cucurbit[8]uril and ligands in the SAMPL6 challenge, is presented. A great improvement in the prediction accuracy was made by more accurate torsional parameters of cucurbit[8]uril and by revised protocols annihilating the intra-molecular van der Waals and key torsions in the ligands. Corresponding methods for all portions of this work have been implemented in the Tinker software package, some of which are also available in the Tinker-OpenMM library

Hydration free energies in the FreeSolv database calculated with polarized iterative Hirshfeld charges

Computer simulations of biomolecular systems often use force fields, which are combinations of simple empirical atom-based functions to describe the molecular interactions. Even though polarizable force fields give a more detailed description of intermolecular interactions, nonpolarizable force fields, developed several decades ago, are often still preferred because of their reduced computation cost. Electrostatic interactions play a major role in biomolecular systems and are therein described by atomic point charges. In this work, we address the performance of different atomic charges to reproduce experimental hydration free energies in the FreeSolv database in combination with the GAFF force field. Atomic charges were calculated by two atoms-in-molecules approaches, Hirshfeld-I and Minimal Basis Iterative Stockholder (MBIS). To account for polarization effects, the charges were derived from the solute’s electron density computed with an implicit solvent model, and the energy required to polarize the solute was added to the free energy cycle. The calculated hydration free energies were analyzed with an error model, revealing systematic errors associated with specific functional groups or chemical elements. The best agreement with the experimental data is observed for the AM1-BCC and the MBIS atomic charge methods. The latter includes the solvent polarization and presents a root-mean-square error of 2.0 kcal mol–1 for the 613 organic molecules studied. The largest deviation was observed for phosphorus-containing molecules and the molecules with amide, ester and amine functional groups