Surveying implicit solvent models for estimating small molecule absolute hydration free energies

Implicit solvent models are powerful tools in accounting for the aqueous environment at a fraction of the computational expense of explicit solvent representations. Here, we compare the ability of common implicit solvent models (TC, OBC, OBC2, GBMV, GBMV2, GBSW, GBSW/MS, GBSW/MS2 and FACTS) to reproduce experimental absolute hydration free energies for a series of 499 small neutral molecules that are modeled using AMBER/GAFF parameters and AM1‐BCC charges. Given optimized surface tension coefficients for scaling the surface area term in the nonpolar contribution, most implicit solvent models demonstrate reasonable agreement with extensive explicit solvent simulations (average difference 1.0–1.7 kcal/mol and R 2 = 0.81–0.91) and with experimental hydration free energies (average unsigned errors = 1.1–1.4 kcal/mol and R 2 = 0.66–0.81). Chemical classes of compounds are identified that need further optimization of their ligand force field parameters and others that require improvement in the physical parameters of the implicit solvent models themselves. More sophisticated nonpolar models are also likely necessary to more effectively represent the underlying physics of solvation and take the quality of hydration free energies estimated from implicit solvent models to the next level. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87097/1/21876_ftp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/87097/2/JCC_21876_sm_suppinfo.pd

Atomic radius and charge parameter uncertainty in biomolecular solvation energy calculations

Atomic radii and charges are two major parameters used in implicit solvent electrostatics and energy calculations. The optimization problem for charges and radii is under-determined, leading to uncertainty in the values of these parameters and in the results of solvation energy calculations using these parameters. This paper presents a new method for quantifying this uncertainty in implicit solvation calculations of small molecules using surrogate models based on generalized polynomial chaos (gPC) expansions. There are relatively few atom types used to specify radii parameters in implicit solvation calculations; therefore, surrogate models for these low-dimensional spaces could be constructed using least-squares fitting. However, there are many more types of atomic charges; therefore, construction of surrogate models for the charge parameter space requires compressed sensing combined with an iterative rotation method to enhance problem sparsity. We demonstrate the application of the method by presenting results for the uncertainties in small molecule solvation energies based on these approaches. The method presented in this paper is a promising approach for efficiently quantifying uncertainty in a wide range of force field parameterization problems, including those beyond continuum solvation calculations.The intent of this study is to provide a way for developers of implicit solvent model parameter sets to understand the sensitivity of their target properties (solvation energy) on underlying choices for solute radius and charge parameters

Accuracy comparison of several common implicit solvent models and their implementations in the context of protein-ligand binding.

In this study several commonly used implicit solvent models are compared with respect to their accuracy of estimating solvation energies of small molecules and proteins, as well as desolvation penalty in protein-ligand binding. The test set consists of 19 small proteins, 104 small molecules, and 15 protein-ligand complexes. We compared predicted hydration energies of small molecules with their experimental values; the results of the solvation and desolvation energy calculations for small molecules, proteins and protein-ligand complexes in water were also compared with Thermodynamic Integration calculations based on TIP3P water model and Amber12 force field. The following implicit solvent (water) models considered here are: PCM (Polarized Continuum Model implemented in DISOLV and MCBHSOLV programs), GB (Generalized Born method implemented in DISOLV program, S-GB, and GBNSR6 stand-alone version), COSMO (COnductor-like Screening Model implemented in the DISOLV program and the MOPAC package) and the Poisson-Boltzmann model (implemented in the APBS program). Different parameterizations of the molecules were examined: we compared MMFF94 force field, Amber12 force field and the quantum-chemical semi-empirical PM7 method implemented in the MOPAC package. For small molecules, all of the implicit solvent models tested here yield high correlation coefficients (0.87-0.93) between the calculated solvation energies and the experimental values of hydration energies. For small molecules high correlation (0.82-0.97) with the explicit solvent energies is seen as well. On the other hand, estimated protein solvation energies and protein-ligand binding desolvation energies show substantial discrepancy (up to 10kcal/mol) with the explicit solvent reference. The correlation of polar protein solvation energies and protein-ligand desolvation energies with the corresponding explicit solvent results is 0.65-0.99 and 0.76-0.96 respectively, though this difference in correlations is caused more by different parameterization and less by methods and indicates the need for further improvement of implicit solvent models parameterization. Within the same parameterization, various implicit methods give practically the same correlation with results obtained in explicit solvent model for ligands and proteins: e.g. correlation values of polar ligand solvation energies and the corresponding energies in the frame of explicit solvent were 0.953-0.966 for the APBS program, the GBNSR6 program and all models used in the DISOLV program. The DISOLV program proved to be on a par with the other used programs in the case of proteins and ligands solvation energy calculation. However, the solution of the Poisson-Boltzmann equation (APBS program) and Generalized Born method (implemented in the GBNSR6 program) proved to be the most accurate in calculating the desolvation energies of complexes

ATOMISTIC SIMULATIONS OF INTRINSICALLY DISORDERED PROTEIN FOLDING AND DYNAMICS

Intrinsically disordered proteins (IDPs) are crucial in biology and human diseases, necessitating a comprehensive understanding of their structure, dynamics, and interactions. Atomistic simulations have emerged as a key tool for unraveling the molecular intricacies and establishing mechanistic insights into how these proteins facilitate diverse biological functions. However, achieving accurate simulations requires both an appropriate protein force field capable of describing the energy landscape of functionally relevant IDP conformations and sufficient conformational sampling to capture the free energy landscape of IDP dynamics. These factors are fundamental in comprehending potential IDP structures, dynamics, and interactions. I first conducted explicit solvent simulations to assess the performance of two state-of-the-art protein force fields, namely CHARMM36m and a99SB-disp, in capturing the stability of small protein-protein interactions. To evaluate their accuracy, I selected a set of 46 amino acid backbone and side chain pairs with representative configurations and computed the free energy profiles of their interactions. The results demonstrated that CHARMM36m consistently predicted stronger protein-protein interactions compared to a99SB-disp. Notably, the most significant overestimation in CHARMM36m occurred in charged pairs involving Arg and Glu side chains, with an overestimation of up to 2.9 kcal/mol. Through free energy decomposition analysis, I determined that these overestimations were primarily driven by protein-water electrostatic interactions rather than van der Waals (vdW) interactions. Consequently, these findings suggest that careful rebalancing of electrostatic interactions should be considered in the further optimization of protein force fields. In order to enhance the conformational sampling of IDPs, I developed an integrated approach that combines an improved implicit solvent model called Generalized Born with molecular volume and solvent accessible surface area (GBMV2/SA) with a multiscale enhanced sampling (MSES) technique. To make this approach more efficient, I implemented it as a standalone OpenMM plugin on Graphics Processing Units (GPUs). The results demonstrated that the GPU-GBMV2/SA model achieved numerical equivalence to the original CPU-GBMV2/SA models, while providing a remarkable ~60x speedup on a single NVIDIA TITAN X (Pascal) graphics card for molecular dynamic simulations of both folded and unstructured proteins. This significant acceleration greatly facilitated the application of the approach in biomolecular simulations. In addition, I conducted an evaluation of the reliability of GBMV2/SA models in simulating both folded and unfolded proteins. The results revealed that the GBMV2/SA model accurately describes small proteins, but its applicability is limited when it comes to larger proteins such as KID and p53-TAD proteins. This limitation can be attributed to the absence of long-range solute-solvent dispersion interactions in the model. To address this issue, I introduced a comprehensive treatment of nonpolar solvation free energy called GBMV2/NP model. Unfortunately, the GBMV2/NP model exhibited a destabilizing effect on well-folded proteins, particularly larger ones, due to an inaccurate representation of the repulsive solvent accessible surface area (SASA) model caused by the utilization of unphysical van der Waals volume. This observation highlights the need for further improvements in accurately describing the nonpolar term in the model

Implicit Solvation Methods for Catalysis at Electrified Interfaces

Implicit solvation is an effective, highly coarse-grained approach in atomic-scale simulations to account for a surrounding liquid electrolyte on the level of a continuous polarizable medium. Originating in molecular chemistry with finite solutes, implicit solvation techniques are now increasingly used in the context of first-principles modeling of electrochemistry and electrocatalysis at extended (often metallic) electrodes. The prevalent ansatz to model the latter electrodes and the reactive surface chemistry at them through slabs in periodic boundary condition supercells brings its specific challenges. Foremost this concerns the difficulty of describing the entire double layer forming at the electrified solid–liquid interface (SLI) within supercell sizes tractable by commonly employed density functional theory (DFT). We review liquid solvation methodology from this specific application angle, highlighting in particular its use in the widespread ab initio thermodynamics approach to surface catalysis. Notably, implicit solvation can be employed to mimic a polarization of the electrode’s electronic density under the applied potential and the concomitant capacitive charging of the entire double layer beyond the limitations of the employed DFT supercell. Most critical for continuing advances of this effective methodology for the SLI context is the lack of pertinent (experimental or high-level theoretical) reference data needed for parametrization

Theoretical and Experimental Investigations of the Interaction of Proteins and Nanoparticles with Biological Membranes

Biomolecular processes related to the interaction of proteins, AuNPs and biological membranes are studied in this thesis. In particular, computational methods were developed, implemented and validated. To characterize the influence of antimicrobial peptides and AuNPs on a membrane, black lipid bilayer experiments were performed. To understand interactions of certain AuNPs with the hERG ion channel, complex formation between these two was studied using atomistic simulations

Development of Methods for the Investigation of RNA-Ligand Interactions.

Three critical features of RNA make it a unique challenge for drug discovery: a) it is highly negatively charged, increasing non-specific binding, b) it can be highly dynamic, adopting different conformations upon binding varying ligands, and c) it has solvent exposed shallow binding pockets. All these properties represent distinct problems in the advancement of RNA-drug discovery. To address this first problem, MATCH was developed to rapidly, accurately, and universally parameterize small molecules for docking. MATCH accomplishes this by deconstructing a force field into a set of fundamental rules which best replicates existing parameters and permits extension to new molecules. MATCH is not only necessary to study RNA-ligand interactions en masse but will also contribute to understanding the charge-charge consequences of ligand binding. To address RNA flexibility, a method to combine NMR chemical shifts and Molecular Dynamics (MD) was developed to generate dynamic ensembles. To benchmark this technique, a set of 26 RNA structures with experimentally determined chemical shift was selected. An ensemble of structures was optimized to match the chemical shifts of each system. These ensembles were also shown to be consistent with of NMR NOE and RDCs constraints. To further demonstrate the utility of this method a large pool of structures (~350,000) was used to generate an ensemble for a prominent RNA target – the ribosomal decoding site. The conformations within this ensemble were found on favorable areas of the free energy landscape, independently indicating the validity of these structures. Finally to address the solvent exposed binding pocket of RNA and its flexible ligands, a new docking approach for RNA was developed, which performs an enhanced sampling technique by fragmenting the ligand and independently optimizing the conformation of each fragment. To properly benchmark this novel algorithm, a large set of 230 nucleic acid-ligand complexes was compiled. Utilizing this large set of this enhanced sampling technique was compared to ICM – a leading docking program. ICM produced native-like conformations 45% of the time, while our approach yields native-like conformations 55% of the time. Demonstrating the effectiveness of this novel sampling procedure.PHDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102297/1/jyesselm_1.pd

The Design and Application of Enzyme Inter-residue Interaction Networks Towards Quantum Mechanical Modeling

The Design and Application of Enzyme Inter-residue Interaction Networks Towards Quantum Mechanical Modelin

Towards Improving The Accuracy of Implicit Solvent Models and Understanding Electrostatic Catalysis in Complex Solvent Environment

This thesis develops improved protocols for studying reactions in solution and uses them to explore the possibility of harnessing complex non-standard solvent environments to catalyse chemical reactions. The thesis covers different but related topics: Improving the accuracy of implicit solvent models. Implicit solvent models are simple cost-effective strategies for modelling solvent as a polarizable continuum. However, the accuracy of this approach can be quite variable. Herein, we examine approaches to improving their accuracy through cavity scaling, the choice of theoretical level and the inclusion of explicit solvent molecules. For SMD, we show that the best performance is achieved when cavity scaling is not employed, while for PCM we present a series of electrostatic scale factors that are radii, solvent and ion type dependent. For both families of method, we also highlight the importance choosing an appropriate level of theory, and identify when explicit solvent molecules are required.. Modelling electrostatic catalysis in complex solvent environment. Recent nanoscale experiments have shown that electric fields are capable of catalysing and controlling chemical reactions, but experimental platforms for scaling these effects remain elusive. Herein, two different approaches to addressing this challenge are explored. The first is using the internal electric field of ordered solvents and ionic liquids, the second is using the electric fields that form naturally at the gas-water interface. A multi-scale modelling approach was developed using polarizable force field based molecular dynamic simulation, post-HF, DFT and semi-empirical quantum chemical calculations. We showed that after exposure to an external electric field, ensembles of solvent or ionic liquid molecules become ordered and this ordering can generate an internal electric field, which persists even after the external potential is removed. Experimental collaborators subsequently detected this field as an open-circuit potential that is strong and long-lived. Computationally we showed that this field is enough to lower reaction barriers by as much as 20 kcal mol-1, and we also developed a predictive structure-reactivity model to choose ionic liquids that optimize this field. In the second approach, we harnessed the electric fields of the gas-water interface. A collaborator showed that in the presence of static, inert gas bubbles, the oxidation potential of HO anion/HO radical was dramatically lowered (by more than 0.5V), much more than any subtle concentration effects predicted by the Nernst equation. Further experiments showed that a high unbalanced concentration of HO- ions (as much as 5M) accumulate at the interface. Our multi-scale modelling calculations showed that this reduction in potential was due to the mutual repulsion of the HO- ions and as little as 1M unbalanced excess was enough to explain the experimental results. The work raises opportunities in reducing the cost of electrochemical processes, and points to electrostatic effects contributing to the well-known catalytic effects of "on water" reactions. Works in this thesis are expected to be useful in the future studies of solution-phase pKa, redox potential, electrostatic catalysis and ionic liquids-based electrochemical devices