Biomolecular electrostatics and solvation: a computational perspective

The Streptococcus phage protein paratox is an intrinsically disordered protein

The bacteriophage protein paratox (Prx) blocks quorum sensing in its streptococcal host by directly binding the signal receptor and transcription factor ComR. This reduces the ability of Streptococcus to uptake environmental DNA and protects phage DNA from damage by recombination. Past work characterizing the Prx:ComR molecular interaction revealed that paratox adopts a well-ordered globular fold when bound to ComR. However, solution-state biophysical measurements suggested that Prx may be conformationally dynamic. To address this discrepancy, we investigated the stability and dynamic properties of Prx in solution using circular dichroism, nuclear magnetic resonance, and several fluorescence-based protein folding assays. Our work shows that under dilute buffer conditions Prx is intrinsically disordered. We also show that the addition of kosmotropic salts or protein stabilizing osmolytes induces Prx folding. However, the solute stabilized fold is different from the conformation Prx adopts when it is bound to ComR. Furthermore, we have characterized Prx folding thermodynamics and folding kinetics through steady-state fluorescence and stopped flow kinetic measurements. Our results show that Prx is a highly dynamic protein in dilute solution, folding and refolding within the 10 ms timescale. Overall, our results demonstrate that the streptococcal phage protein Prx is an intrinsically disordered protein in a two-state equilibrium with a solute-stabilized folded form. Furthermore, the solute-stabilized fold is likely the predominant form of Prx in a solute-crowded bacterial cell. Finally, our work suggests that Prx binds and inhibits ComR, and thus quorum sensing in Streptococcus, by a combination of conformational selection and induced-fit binding mechanisms.

Structural and mechanistic insights into Quinolone Synthase to address its functional promiscuity

Quinolone synthase from Aegle marmelos (AmQNS) is a type III polyketide synthase that yields therapeutically effective quinolone and acridone compounds. Addressing the structural and molecular underpinnings of AmQNS and its substrate interaction in terms of its high selectivity and specificity can aid in the development of numerous novel compounds. This paper presents a high-resolution AmQNS crystal structure and explains its mechanistic role in synthetic selectivity. Additionally, we provide a model framework to comprehend structural constraints on ketide insertion and postulate that AmQNS's steric and electrostatic selectivity plays a role in its ability to bind to various core substrates, resulting in its synthetic diversity. AmQNS prefers quinolone synthesis and can accommodate large substrates because of its wide active site entrance. However, our research suggests that acridone is exclusively synthesized in the presence of high malonyl-CoA concentrations. Potential implications of functionally relevant residue mutations were also investigated, which will assist in harnessing the benefits of mutations for targeted polyketide production. The pharmaceutical industry stands to gain from these findings as they expand the pool of potential drug candidates, and these methodologies can also be applied to additional promising enzymes.

Polarizability Plays a Decisive Role in Modulating Association between Molecular Cations and Anions

Electrostatic interactions involving proteins depend on not only the ionic charges involved but also their chemical identities. Here we examine the origins of incompletely understood differences in the strength of association of different pairs of monovalent molecular ions that are relevant to protein-protein and protein-ligand interactions. Cationic analogues of the basic amino acid side chains are simulated, along with oxyanionic analogues of cation-exchange ligands and acidic amino acids. Experimentally observed association trends with respect to the cations, but not anions, are captured by a nonpolarizable model. An effective continuum correction to account for electronic polarizability can capture both trends better but at the expense of fidelity to the underlying free energy landscape for ion-pair association. A polarizable model proves decisive in capturing experimentally suggested trends with respect to both cations and anions; critically, the free energy landscape for ion-pair association is itself altered, thus altering configurational sampling.

Streamlined Efficient Synthesis and Antioxidant Activity of γ-[Glutamyl](n≥1)-tryptophan Peptides by Glutaminase from Bacillus amyloliquefaciens

As a group of naturally occurring peptides in various foods, γ-glutamyl peptides possess a unique Kokumi taste and health benefits. However, few studies have focused on the functionality of γ-glutamyl peptides. In this study, the γ-[glutamyl] _{(n=1, 2, 3)}-tryptophan peptides were synthesized from a solution of glutamine (Gln) and tryptophan (Trp) employing L-glutaminase from Bacillus amyloliquefaciens. Four different γ-glutamyl peptides were identified from the reaction mixture by UPLC-Q-TOF-MS/MS. Under optimal conditions of pH 10, 37 °C, 3 h, 0.1 mol/L Gln: 0.1 mol/L Trp = 1:3, and glutaminase at 0.1% (m/v), the yields of γ-l-glutamyl-l-tryptophan (γ-EW), γ-l-glutamyl-γ-l-glutamyl-l-tryptophan (γ-EEW) and γ-l-glutamyl-γ-l-glutamyl-γ-l-glutamyl-l-tryptophan (γ-EEEW) were 51.02%, 26.12% and 1.91% respectively. The antioxidant properties of the reaction mixture and the two peptides (γ-EW, γ-EEW) identified from the reaction media were further compared. Results showed that γ-EW exhibited the highest DPPH^•, ABTS^•+ and O₂^•--scavenging activity (EC₅₀ = 0.2999 mg/mL, 67.6597 μg/mL and 5.99 mg/mL, respectively) and reducing power (EC₅₀ = 4.61 mg/mL), while γ-EEW demonstrated the highest iron-chelating activity (76.22%). Thus, the synthesized mixture may be used as a potential source of antioxidant peptides for food and nutraceutical applications.

Interactions between Rigid Polyelectrolytes Mediated by Ordering and Orientation of Multivalent Nonspherical Ions in Salt Solutions

Multivalent ions in solutions with polyelectrolytes (PEs) induce electrostatic correlations that can drastically change ion distributions around the PEs and their mutual interactions. Using coarse-grained molecular dynamics simulations, we show how in addition to valency, ion shape and concentration can be harnessed as tools to control rigid like-charged PE-PE interactions. We demonstrate a correlation between the orientational ordering of aspherical ions and how they mediate the effective PE-PE attraction induced by multivalency. The interaction type, strength, and range can thus be externally controlled in ionic solutions. Our results can be used as generic guidelines to tune the self-assembly of like-charged polyelectrolytes by variation of the characteristics of the ions.

Conformations and Solvation of Synthetic Polymers in Water by Generalized Born Implicit-Solvent Molecular Dynamics Simulations: Stereoisomers of Poly(acrylic acid) and Poly(methacrylic acid)

We present the GB-OBC model as an approach for implicit-solvent MD simulations of a synthetic macromolecule in water. The model is tested and found to be successful in reproducing the chain dimensions and predicting the free energy of solvation of carboxylic acid vinyl polymers. The influence of stereochemistry and the hydrophobic nature of the polymer was investigated as a function of chain length (20 < N < 600) for poly(acrylic acid) (PAA) and poly(methacrylic acid) (PMA). The dimensionless parameters of the GB-OBC model were parameterized to be applicable to PAA and PMA. Scaling relations for chain dimensions obtained using implicit-solvent MD simulations in this study are in good agreement with those from experiments, theory of solvated chains in good solvents, and all-atom MD simulations in explicit water. Results show that ⟨R_g²⟩/NL² is greater for the atactic chain as compared to the isotactic chain, for PAA as well as PMA. ⟨R_g²⟩/NL² values of chains attain constancy in water for N = 200, with the values being greater for PMA. The PMA chain is conformationally more perturbed than the PAA chain, for both isotactic and atactic stereochemistry. The solvation free energy ΔG_hyd of PAA and PMA in water is negative for all chain lengths (N = 20-600) and becomes more favorable with an increase in molecular weight. The ΔG_hyd values for isotactic and atactic chains are identical at lower values of N but differ slightly for N > 300. Irrespective of the hydrophobic nature of the polymer, the atactic chain is thermodynamically more soluble in water as compared to the isotactic chain. The isotactic chain is less hydrophilic as compared to the atactic chain due to the closer proximity of the COOH groups along the backbone. This implicit solvent method is an effective way to accurately simulate the configurational properties and solvation of synthetic polymers in water.

Treating Polarization Effects in Charged and Polar Bio-Molecules Through Variable Electrostatic Parameters

Polarization plays important roles in charged and hydrogen bonding containing systems. Much effort ranging from the construction of physics-based models to quantum mechanism (QM)-based and machine learning (ML)-assisted models have been devoted to incorporating the polarization effect into the conventional force fields at different levels, such as atomic and coarse grained (CG). The application of polarizable force fields or polarization models was limited by two aspects, namely, computational cost and transferability. Different from physics-based models, no predetermining parameters were required in the QM-based approaches. Taking advantage of both the accuracy of QM calculations and efficiency of molecular mechanism (MM) and ML, polarization effects could be treated more efficiently while maintaining the QM accuracy. The computational cost could be reduced with variable electrostatic parameters, such as the charge, dipole, and electronic dielectric constant with the help of linear scaling fragmentation-based QM calculations and ML models. Polarization and entropy effects on the prediction of partition coefficient of druglike molecules are demonstrated by using both explicit or implicit all-atom molecular dynamics simulations and machine learning-assisted models. Directions and challenges for future development are also envisioned.

Non-Ewald methods for evaluating the electrostatic interactions of charge systems: similarity and difference

In molecular simulations, it is essential to properly calculate the electrostatic interactions of particles in the physical system of interest. Here we consider a method called the non-Ewald method, which does not rely on the standard Ewald method with periodic boundary conditions, but instead relies on the cutoff-based techniques. We focus on the physicochemical and mathematical conceptual aspects of the method in order to gain a deeper understanding of the simulation methodology. In particular, we take into account the reaction field (RF) method, the isotropic periodic sum (IPS) method, and the zero-multipole summation method (ZMM). These cutoff-based methods are based on different physical ideas and are completely distinguishable in their underlying concepts. The RF and IPS methods are "additive" methods that incorporate information outside the cutoff region, via dielectric medium and isotropic boundary condition, respectively. In contrast, the ZMM is a "subtraction" method that tries to remove the artificial effects, generated near the boundary, from the cutoff sphere. Nonetheless, we find physical and/or mathematical similarities between these methods. In particular, the modified RF method can be derived by the principle of neutralization utilized in the ZMM, and we also found a direct relationship between IPS and ZMM.

Modeling of the Electrostatic Interaction and Catalytic Activity of [NiFe] Hydrogenases on a Planar Electrode

Hydrogenases are a group of enzymes that have caught the interest of researchers in renewable energies, due to their ability to catalyze the redox reaction of hydrogen. The exploitation of hydrogenases in electrochemical devices requires their immobilization on the surface of suitable electrodes, such as graphite. The orientation of the enzyme on the electrode is important to ensure a good flux of electrons to the catalytic center, through an array of iron-sulfur clusters. Here we present a computational approach to determine the possible orientations of a [NiFe] hydrogenase (PDB 1e3d) on a planar electrode, as a function of pH, salinity, and electrode potential. The calculations are based on the solution of the linearized Poisson-Boltzmann equation, using the PyGBe software. The results reveal that electrostatic interactions do not truly immobilize the enzyme on the surface of the electrode, but there is instead a dynamic equilibrium between different orientations. Nonetheless, after averaging over all thermally accessible orientations, we find significant differences related to the solution's salinity and pH, while the effect of the electrode potential is relatively weak. We also combine models for the protein adsoption-desorption equilibria and for the electron transfer between the proteins and the electrode to arrive at a prediction of the electrode's activity as a function of the enzyme concentration.

Predicting accurate ab initio DNA electron densities with equivariant neural networks

One of the fundamental limitations of accurately modeling biomolecules like DNA is the inability to perform quantum chemistry calculations on large molecular structures. We present a machine learning model based on an equivariant Euclidean neural network framework to obtain accurate ab initio electron densities for arbitrary DNA structures that are much too large for conventional quantum methods. The model is trained on representative B-DNA basepair steps that capture both base pairing and base stacking interactions. The model produces accurate electron densities for arbitrary B-DNA structures with typical errors of less than 1%. Crucially, the error does not increase with system size, which suggests that the model can extrapolate to large DNA structures with negligible loss of accuracy. The model also generalizes reasonably to other DNA structural motifs such as the A- and Z-DNA forms, despite being trained on only B-DNA configurations. The model is used to calculate electron densities of several large-scale DNA structures, and we show that the computational scaling for this model is essentially linear. We also show that this machine learning electron density model can be used to calculate accurate electrostatic potentials for DNA. These electrostatic potentials produce more accurate results compared with classical force fields and do not show the usual deficiencies at short range.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

A Polarizable Cationic Dummy Metal Ion Model

A novel locally polarizable multisite model based on the original cation dummy atom (CDA) model is described for molecular dynamics simulations of ions in condensed phases. Polarization effects are introduced by the electronegativity equalization model (EEM) method where charges on the metal ion and its dummy atoms can fluctuate to respond to the environment. This model includes explicit polarization and ion-induced interactions and can be coupled with nonpolarizable or polarizable water models, making it more transferable to simpler force fields. This approach allows us to enhance the original fixed charge CDA model where the charge distribution cannot adapt to the local solvent structure. To illustrate the new CDApol model, we examined properties of the Zn2+, Al3+, and Zr4+ ions in aqueous solution. The polarizable model and Lennard-Jones parameters were refined for octahedrally coordinated Zn2+, Al3+, and Zr4+ CDAs to reproduce thermodynamic and geometrical properties. Using this locally polarizable model, we were able to obtain the experimental hydration free energy, ion-oxygen distance, and coordination number coupled with the standard 12-6 Lennard-Jones model. This model can be used in myriad additional applications where local polarization and charge transfer is important.

Modified Poisson–Boltzmann theory for polyelectrolytes in monovalent salt solutions with finite-size ions

We present a soft-potential-enhanced Poisson–Boltzmann (SPB) theory to efficiently capture ion distributions and electrostatic potential around rodlike charged macromolecules. The SPB model is calibrated with a coarse-grained particle-based model for polyelectrolytes (PEs) in monovalent salt solutions as well as compared to a full atomistic molecular dynamics simulation with the explicit solvent. We demonstrate that our modification enables the SPB theory to accurately predict monovalent ion distributions around a rodlike PE in a wide range of ion and charge distribution conditions in the weak-coupling regime. These include excess salt concentrations up to 1M and ion sizes ranging from small ions, such as Na+ or Cl−, to softer and larger ions with a size comparable to the PE diameter. The work provides a simple way to implement an enhancement that effectively captures the influence of ion size and species into the PB theory in the context of PEs in aqueous salt solutions.

Electrical impulse characterization along actin filaments in pathological conditions

We present an interactive Mathematica notebook that characterizes the electrical impulses along actin filaments in both muscle and non-muscle cells for a wide range of physiological and pathological conditions. The simplicity of the theoretical formulation, and high performance of the Mathematica software, enable the analysis of multiple conditions without computational restrictions. The program is based on a multi-scale (atomic → monomer → filament) approach capable of accounting for the atomistic details of a protein molecular structure, its biological environment, and their impact on the travel distance, velocity, and attenuation of monovalent ionic wave packets propagating along microfilaments. The interactive component allows investigators to choose the experimental conditions (intracellular Vs in vitro), nucleotide state (ATP Vs ADP), actin isoform (alpha, gamma, beta, and muscle or non-muscle cell), as well as a conformation model that covers a variety of mutants and wild-type (the control) actin filament. We used the computational tool to analyze environmental changes such as temperature effects and pH changes of the surrounding solutions, as well as structural changes to an actin monomer due to radius changes. Additionally, we investigated for the first time the electrostatic consequences of actin mutations from different disease conditions. These studies may provide an unprecedented molecular understanding of why and how age, inheritance, and disease conditions induce dysfunctions in the biophysical mechanisms underlying the propagation of electrical signals along actin filaments.

RNA-ligand molecular docking: advances and challenges

With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.

Quantum Vibrational Spectroscopy of Explicitly Solvated Thymidine in Semiclassical Approximation

In this paper, we demonstrate the possibility to perform spectroscopy simulations of solvated biological species taking into consideration quantum effects and explicit solvation. We achieve this goal by interfacing our recently developed divide-and-conquer approach for semiclassical initial value representation molecular dynamics with the polarizable AMOEBABIO18 force field. The method is applied to the study of solvation of the thymidine nucleoside in two different polar solvents, water and N,N-dimethylformamide. Such systems are made of up to 2476 atoms. Experimental evidence concerning the different behavior of thymidine in the two solvents is well reproduced by our study, even though quantitative estimates are hampered by the limited accuracy of the classical force field employed. Overall, this study shows that semiclassically approximate quantum dynamical studies of explicitly solvated biological systems are both computationally affordable and insightful.

Electrical Propagation of Condensed and Diffuse Ions Along Actin Filaments

In this article, we elucidate the roles of divalent ion condensation and highly polarized immobile water molecules on the propagation of ionic calcium waves along actin filaments. We introduced a novel electrical triple layer model and used a non-linear Debye-Huckel theory with a non-linear, dissipative, electrical transmission line model to characterize the physicochemical properties of each monomer in the filament. This characterization is carried out in terms of an electric circuit model containing monomeric flow resistances and ionic capacitances in both the condensed and diffuse layers. We considered resting and excited states of a neuron using representative mono and divalent electrolyte mixtures. Additionally, we used 0.05V and 0.15V voltage inputs to study ionic waves along actin filaments in voltage clamp experiments. Our results reveal that the physicochemical properties characterizing the condensed and diffuse layers lead to different electrical conductive mediums depending on the ionic species and the neuron state. This region specific propagation mechanism provides a more realistic avenue of delivery by way of cytoskeleton filaments for larger charged cationic species. A new direct path for transporting divalent ions might be crucial for many electrical processes found in localized neuron elements such as at mitochondria and dendritic spines.

Dendritic polyelectrolytes with monovalent and divalent counterions: the charge regulation effect and counterion release

The charge regulation and the release of counterions are extremely important and substantial in determining the charge state of polyelectrolytes and the interaction between polyelectrolytes and proteins. Going beyond monovalent to multivalent cations, it is well-known that the effects of ions are qualitatively different. Therefore, the well-accepted descriptions of the charge regulation and the counterion release based on monovalent ions do not immediately apply to systems with multivalent ions. Here, we study the key structural and electrostatic features of charged dendrimers at hand of the pharmaceutically important dendritic polyglycerol sulfate (dPGS) macromolecule equilibrated with monovalent and divalent salts by molecular dynamics (MD) simulations. Following a simple but accurate scheme to determine its effective radius, the counterion condensed layer of the dPGS is determined with high accuracy and we observe the sequential replacement of condensed monovalent cations (MCs) to divalent cations (DCs) rendering a smaller dPGS effective charge versus the DC concentration. We resolve and track the release of counterions on the dPGS along its binding pathway with the plasma protein Human Serum Albumin (HSA). We find that the release of MCs remains favorable for the complexation leading to a considerable amount of release entropy as the driving force for complexation. The release of DCs only occurs above a certain DC concentration with a comparably smaller number of released ions than MCs. Its contribution to the binding free energy is small indicating a subtle cancellation between the entropy gain in releasing DCs and the enthalpy penalty from dissociating DCs from the dendrimer.

Machine learning implicit solvation for molecular dynamics

Accurate modeling of the solvent environment for biological molecules is crucial for computational biology and drug design. A popular approach to achieve long simulation time scales for large system sizes is to incorporate the effect of the solvent in a mean-field fashion with implicit solvent models. However, a challenge with existing implicit solvent models is that they often lack accuracy or certain physical properties compared to explicit solvent models as the many-body effects of the neglected solvent molecules are difficult to model as a mean field. Here, we leverage machine learning (ML) and multi-scale coarse graining (CG) in order to learn implicit solvent models that can approximate the energetic and thermodynamic properties of a given explicit solvent model with arbitrary accuracy, given enough training data. Following the previous ML-CG models CGnet and CGSchnet, we introduce ISSNet, a graph neural network, to model the implicit solvent potential of mean force. ISSNet can learn from explicit solvent simulation data and be readily applied to molecular dynamics simulations. We compare the solute conformational distributions under different solvation treatments for two peptide systems. The results indicate that ISSNet models can outperform widely used generalized Born and surface area models in reproducing the thermodynamics of small protein systems with respect to explicit solvent. The success of this novel method demonstrates the potential benefit of applying machine learning methods in accurate modeling of solvent effects for in silico research and biomedical applications.

A Quantum Chemical Topology Picture of Intermolecular Electrostatic Interactions and Charge Penetration Energy

Based on the Interacting Quantum Atoms approach, we present herein a conceptual and theoretical framework of short-range electrostatic interactions, whose accurate description is still a challenging problem in molecular modeling. For all the noncovalent complexes in the S66 database, the fragment-based and atomic decomposition of the electrostatic binding energies is performed using both the charge density of the dimers and the unrelaxed densities of the monomers. This energy decomposition together with dispersion corrections gives rise to a pairwise approximation to the total binding energy. It also provides energetic descriptors at varying distance that directly address the atomic and molecular electrostatic interactions as described by point-charge or multipole-based potentials. Additionally, we propose a consistent definition of the charge penetration energy within quantum chemical topology, which is mainly characterized in terms of the intramolecular electrostatic energy. Finally, we discuss some practical implications of our results for the design and validation of electrostatic potentials.

Inversely polarized thermo-electrochemical power generation via the reaction of an organic redox couple on a TiO2/Ti mesh electrode

We demonstrate thermo-electrochemical (TEC) conversion using a biocompatible redox couple of lactic acid and pyruvic acid on earth-abundant TiO₂. The TEC cell exhibited a positive Seebeck coefficient of 1.40 mV K^-1. DFT calculations figured out that the adsorption of intermediate species and protons on TiO₂ controls both the redox reaction and current polarity.

Thermodynamics of ion binding and occupancy in potassium channels

Potassium channels modulate various cellular functions through efficient and selective conduction of K⁺ ions. The mechanism of ion conduction in potassium channels has recently emerged as a topic of debate. Crystal structures of potassium channels show four K⁺ ions bound to adjacent binding sites in the selectivity filter, while chemical intuition and molecular modeling suggest that the direct ion contacts are unstable. Molecular dynamics (MD) simulations have been instrumental in the study of conduction and gating mechanisms of ion channels. Based on MD simulations, two hypotheses have been proposed, in which the four-ion configuration is an artifact due to either averaged structures or low temperature in crystallographic experiments. The two hypotheses have been supported or challenged by different experiments. Here, MD simulations with polarizable force fields validated by ab initio calculations were used to investigate the ion binding thermodynamics. Contrary to previous beliefs, the four-ion configuration was predicted to be thermodynamically stable after accounting for the complex electrostatic interactions and dielectric screening. Polarization plays a critical role in the thermodynamic stabilities. As a result, the ion conduction likely operates through a simple single-vacancy and water-free mechanism. The simulations explained crystal structures, ion binding experiments and recent controversial mutagenesis experiments. This work provides a clear view of the mechanism underlying the efficient ion conduction and demonstrates the importance of polarization in ion channel simulations.

Polarization shapes the energy landscape of ion conduction in potassium channels.

Testing the Limitations of MD-Based Local Electric Fields Using the Vibrational Stark Effect in Solution: Penicillin G as a Test Case

Noncovalent interactions underlie nearly all molecular processes in the condensed phase from solvation to catalysis. Their quantification within a physically consistent framework remains challenging. Experimental vibrational Stark effect (VSE)-based solvatochromism can be combined with molecular dynamics (MD) simulations to quantify the electrostatic forces in solute-solvent interactions for small rigid molecules and, by extension, when these solutes bind in enzyme active sites. While generalizing this approach toward more complex (bio)molecules, such as the conformationally flexible and charged penicillin G (PenG), we were surprised to observe inconsistencies in MD-based electric fields. Combining synthesis, VSE spectroscopy, and computational methods, we provide an intimate view on the origins of these discrepancies. We observe that the electric fields are correlated to conformation-dependent effects of the flexible PenG side chain, including both the local solvation structure and solute conformational sampling in MD. Additionally, we identified that MD-based electric fields are consistently overestimated in three-point water models in the vicinity of charged groups; this cannot be entirely ameliorated using polarizable force fields (AMOEBA) or advanced water models. This work demonstrates the value of the VSE as a direct method for experiment-guided refinements of MD force fields and establishes a general reductionist approach to calibrating vibrational probes for complex (bio)molecules.

Java application for cytoskeleton filament characterization (JACFC)

JACFC is a Java web application (http://neuronanobiophysics.utsa.edu/) that provides both experts and non-experts in the field suitable tools for elucidating the molecular mechanisms modulating the electrical signal propagation, stability, and bundle formation of microtubules and actin filaments under different molecular (wild type, isoforms, mutants) and environmental (physiological and pathological) conditions. This acknowledgment might reveal the potential role of cytoskeleton filaments in neuronal activities, including molecular-level processing of information and neural regeneration. Molecular understanding of the polyelectrolyte properties of bionanowires, is also crucial for development of reliability, highly functioning small devices with biotechnological applications such as bionanosensors and computing bionanoprocessors.

Implicit Solvents for the Polarizable Atomic Multipole AMOEBA Force Field

Computational protein design, ab initio protein/RNA folding, and protein-ligand screening can be too computationally demanding for explicit treatment of solvent. For these applications, implicit solvent offers a compelling alternative, which we describe here for the polarizable atomic multipole AMOEBA force field based on three treatments of continuum electrostatics: numerical solutions to the nonlinear and linearized versions of the Poisson-Boltzmann equation (PBE), the domain-decomposition conductor-like screening model (ddCOSMO) approximation to the PBE, and the analytic generalized Kirkwood (GK) approximation. The continuum electrostatics models are combined with a nonpolar estimator based on novel cavitation and dispersion terms. Electrostatic model parameters are numerically optimized using a least-squares style target function based on a library of 103 small-molecule solvation free energy differences. Mean signed errors for the adaptive Poisson-Boltzmann solver (APBS), ddCOSMO, and GK models are 0.05, 0.00, and 0.00 kcal/mol, respectively, while the mean unsigned errors are 0.70, 0.63, and 0.58 kcal/mol, respectively. Validation of the electrostatic response of the resulting implicit solvents, which are available in the Tinker (or Tinker-HP), OpenMM, and Force Field X software packages, is based on comparisons to explicit solvent simulations for a series of proteins and nucleic acids. Overall, the emergence of performative implicit solvent models for polarizable force fields opens the door to their use for folding and design applications.

Solvation of nonionic poly(ethylene oxide) surfactant Brij 35 in organic and aqueous-organic solvents

HYPOTHESIS

By combining the experimental small- and wide-angle x-ray scattering (SWAXS) method with molecular dynamics simulations and the theoretical 'complemented-system approach' it is possible to obtain detailed information about the intra- and inter-molecular structure and dynamics of the solvation and hydration of the surfactant in organic and mixed solvents, e.g., of the nonionic surfactant Brij 35 (C₁₂E₂₃) in alcohols and aqueous alcohol-rich ternary systems. This first application of the complemented-system approach to the surfactant system will promote the use of this powerful methodology that is based on experimental and calculated SWAXS data in studies of colloidal systems. By applying high-performance computing systems, such an approach is readily available for studies in the colloidal domain.

EXPERIMENTS

SWAXS experiments and MD simulations were performed for binary Brij 35/alcohol and ternary Brij 35/water/alcohol systems with ethanol, n-butanol and n-hexanol as the organic solvent component at 25 °C.

FINDINGS

We confirmed the presence of solvated Brij 35 monomers in the studied organic media, revealed their preferential hydration and discussed their structural and dynamic features at the intra- and inter-molecular levels. Anisotropic effective surfactant molecular conformations were found. The influence of the hydrophobicity of the organic solvent on the hydration phenomena of surfactant molecules was explained.

Physical stability enhancement and antimicrobial properties of a sodium ionic cocrystal with theophylline

In the present study, we have described the synthesis and characterisation of the theophylline hydrate (Theo hydrate), cocrystal (Theo–Phen·2H₂O) and hydrated sodium co-crystal of theophylline (Na–(Theo)₂ClO·2H₂O), where Theo = theophylline and Phen = 1,10-phenathroline.

Enriched gradient recovery for interface solutions of the Poisson-Boltzmann equation

Accurate calculation of electrostatic potential and gradient on the molecular surface is highly desirable for the continuum and hybrid modeling of large scale deformation of biomolecules in solvent. In this article a new numerical method is proposed to calculate these quantities on the dielectric interface from the numerical solutions of the Poisson-Boltzmann equation. Our method reconstructs a potential field locally in the least square sense on the polynomial basis enriched with Green's functions, the latter characterize the Coulomb potential induced by charges near the position of reconstruction. This enrichment resembles the decomposition of electrostatic potential into singular Coulomb component and the regular reaction field in the Generalized Born methods. Numerical experiments demonstrate that the enrichment recovery produces drastically more accurate and stable potential gradients on molecular surfaces compared to classical recovery techniques.

Novel Highly Potent and Selective Sigma1 Receptor Antagonists Effectively Block the Binge Eating Episode in Female Rats

In this paper, the benzo-cracking approach was applied to the potent sigma1 (σ₁) receptor antagonist 1 to afford the less conformationally constrained 1,3-dioxane derivatives 2 and 3. To evaluate the effect of the increase in the distance between the two hydrophobic structural elements that flank the basic function, the cis and trans diastereomers of 4 and 5 were also prepared and studied. Compounds 2 and 3 showed affinity values at the σ₁ receptor significantly higher than that of the lead compound 1. In particular, 3 displayed unprecedented selectivity over the σ₂ receptor, the phencyclidine site of the NMDA receptor, and opioid receptor subtypes, as well as over the dopamine transporter. Docking results supported the structure–activity relationship studies. Due to its interesting biological profile, derivative 3, selected for an in vivo study in a validated preclinical model of binge eating, was able to counteract the overeating of palatable food only in binging rats, without affecting palatable food intake in the control group and anxiety-like and depression-related behaviors in female rats. This result strengthened the involvement of the σ₁ receptor in the compulsive-like eating behavior and supported the σ₁ receptor as a promising target for the management of eating disorders.

Comparison of Free-Energy Methods to Calculate the Barriers for the Nucleophilic Substitution of Alkyl Halides by Hydroxide

Calculating the free-energy barriers of liquid-phase chemical reactions with explicit solvent is a considerable challenge. Most studies use the energy and entropy of minimized single-point geometries of the reactants and transition state in implicit solvent using normal mode analysis (NMA). Explicit-solvent methods instead make use of the potential of mean force (PMF). Here, we propose a new energy-entropy (EE) method to calculate the Gibbs free energy of reactants and transition states in explicit solvent by combining quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulations with multiscale cell correlation (MCC). We apply it to six nucleophilic substitution reactions of the hydroxide transfer to methyl and ethyl halides in water, where the halides are F, Cl, and Br. We compare EE-MCC Gibbs free energy barriers using two Hamiltonians, self-consistent charge density functional based tight-binding (SCC-DFTB) and B3LYP/6-31+G* density functional theory (DFT) with respective PMF values, EE-NMA values using B3LYP/6-31+G* and M06/6-31+G* DFT in implicit solvent and experimental values derived via transition state theory. The barriers using SCC-DFTB are found to agree well with the PMF and experiment and previous computational studies, being slightly higher but improving on the lower values obtained for the implicit solvent. Achieving convergence over many degrees of freedom remains a challenge for EE-MCC in explicit-solvent QM/MM systems, particularly for the more expensive B3LYP/6-31+G* and M06/6-31+G* DFT methods, but the insightful decomposition of entropy over all degrees of freedom should make EE-MCC a valuable tool for deepening the understanding of chemical reactions.

On interactions of P-glycoprotein with various anti-tumor drugs by binding free energy calculations

P-glycoprotein (P-gp, MDR1), one of ATP-binding cassette transporters, may confer tumor cells cross-resistance to chemotherapeutics. A large amount of P-gp inhibitors were designed to inhibit the multidrug resistance (MDR) feature of P-gp. However, no sufficient researches were reported to explore the correlation between binding capacity and drug property by experiment. Without particular drug property found to inhibit the MDR feature of P-gp, the orientation of drug design is indefinite. In this work, 10 representative cancer drugs with various properties are used to bind with P-gp by molecular dynamics simulation. Binding free energy between P-gp and 10 drugs ranges -139 to -253 kJ/mol. It reveals that the promiscuity nature of P-gp is in light of the similar binding free energy in separate P-gp-ligand binding systems. The binding effect of P-gp and drugs correlates well with the size of drugs and has no apparent correlation with the polarity of each drug. The key reason is that van der Waal's interaction occupies most of the total binding free energy, and it is led by the number of atoms in the drugs. Two transmembrane segments (TM6 and TM12) and three types of amino acids (PHE, MET, and GLN) are vital in binding drugs with van der Waal's energy, which evident the influence between binding stability and size of drugs. This work provides the cause and theoretical basis for the promiscuity nature of P-gp.Communicated by Ramaswamy H. Sarma.

Additive Modulation of DNA-DNA Interactions by Interstitial Ions

Quantitative understanding of biomolecular electrostatics, particularly involving multivalent ions and highly charged surfaces, remains lacking. Ion-modulated interactions between nucleic acids provide a model system in which electrostatics plays a dominant role. Using ordered DNA arrays neutralized by spherical cobalt³⁺ hexammine and Mg²⁺ ions, we investigate how the interstitial ions modulate DNA-DNA interactions. Using methods of ion counting, osmotic stress, and x-ray diffraction, we systematically determine thermodynamic quantities, including ion chemical potentials, ion partition, DNA osmotic pressure and force, and DNA-DNA spacing. Analyses of the multidimensional data provide quantitative insights into their interdependencies. The key finding of this study is that DNA-DNA forces are observed to linearly depend on the partition of interstitial ions, suggesting the dominant role of ion-DNA coupling. Further implications are discussed in light of physical theories of electrostatic interactions and like-charge attraction.

Developing an effective polarizable bond method for small molecules with application to optimized molecular docking

Electrostatic interaction plays an essential role in protein-ligand binding. Due to the polarization effect, electrostatic interactions are largely impacted by their local environments. However, traditional force fields use fixed point charge-charge interactions to describe electrostatic interactions but is unable to include the polarization effect. The lack of the polarization effect in the force field representation can result in substantial error in biomolecular studies, such as molecular dynamics and molecular docking. Docking programs usually employ traditional force fields to estimate the binding energy between a ligand and a protein for pose selection or scoring. The intermolecular interaction energy mainly consists of van der Waals and electrostatic interaction in the force field representation. In the current study, we developed an Effective Polarizable Bond (EPB) method for small organic molecules and applied this EPB method to optimize protein-ligand docking in computational tests for a variety of protein-ligand systems. We tested the method on a set of 38 cocrystallized structures taken from the Protein Data Bank (PDB) and found that the maximum error was reduced from 7.98 Å to 2.03 Å when using EPB Dock, providing strong evidence that the use of EPB charges is important. We found that our optimized docking approach with EPB charges could improve the docking performance, sometimes dramatically, and the maximum error was reduced from 12.88 Å to 1.57 Å in Optimized Docking (in the case of 1fqx). The average RMSD decreased from 2.83 Å to 1.85 Å. Further investigations showed that the use of the EBP method could enhance intermolecular hydrogen bonding, which is a major contributing factor to improved docking performance. Developed tools for the calculation of the polarized ligand charge from a protein-ligand complex structure with the EPB method are freely available on GitHub (https://github.com/Xundrug/EPB).

The Self-Adaptation Ability of Zinc Oxide Nanoparticles Enables Reliable Cancer Treatments

Optimal procedures for reliable anti-cancer treatments involve the systematic delivery of zinc oxide nanoparticles, which spread through the circulatory system. The success of these procedures may largely depend on the NPs' ability of self-adapting their physicochemical properties to overcome the different challenges facing at each stage on its way to the interior of a cancerous cell. In this article, we combine a multiscale approach, a unique nanoparticle model, and available experimental data to characterize the behavior of zinc oxide nanoparticles under different vessels rheology, pH levels, and biological environments. We investigate their ability to prevent aggregation, allow prolonged circulation time in the bloodstream, avoid clearance, conduct themselves through the capillarity system to reach damaged tissues, and selectively approach to target cancerous cells. Our results show that non-functionalized spherical zinc oxide nanoparticles with surface density N = 5.89 × 10-6 mol/m2, protonation and deprotonation rates pKa = 10.9 and pKb = -5.5, and NP size in the range of 20-50 nm are the most effective, smart anti-cancer agents for biomedical treatments.

Molecular Dynamics Study of the Hybridization between RNA and Modified Oligonucleotides

MicroRNAs (miRNAs) are attractive drug candidates for many diseases as they can modulate the expression of gene networks. Recently, we discovered that DNAs targeting microRNA-22-3p (miR-22-3p) hold the potential for treating obesity and related metabolic disorders (type 2 diabetes mellitus, hyperlipidemia, and nonalcoholic fatty liver disease (NAFLD)) by turning fat-storing white adipocytes into fat-burning adipocytes. In this work, we explored the effects of chemical modifications, including phosphorothioate (PS), locked nucleic acid (LNA), and peptide nucleic acid (PNA), on the structure and energy of DNA analogs by using molecular dynamics (MD) simulations. To achieve a reliable prediction of the hybridization free energy, the AMOEBA polarizable force field and the free energy perturbation technique were employed. The calculated hybridization free energies are generally compatible with previous experiments. For LNA and PNA, the enhanced duplex stability can be explained by the preorganization mechanism, i.e., the single strands adopt stable helical structures similar to those in the duplex. For PS, the S and R isomers (Sp and Rp) have preferences for C2'-endo and C3'-endo sugar puckering conformations, respectively, and therefore Sp is less stable than Rp in DNA/RNA hybrids. In addition, the solvation penalty of Rp accounts for its destabilization effect. PS-LNA is similar to LNA as the sugar puckering is dominated by the locked sugar ring. This work demonstrated that MD simulations with polarizable force fields are useful for the understanding and design of modified nucleic acids.

A force consistent method for electrostatic energy calculation in fluctuating charge model

A practical approach to include the polarization effect in a molecular force field is the fluctuating charge method in which atomic charges vary as the configuration of the molecular system changes. However, the use of the Coulomb formula to evaluate energy in a fluctuating charge method is theoretically inconsistent with the forces given by the fluctuating method. In this work, we propose a force-consistent method to correctly calculate electrostatic energies of molecular systems using a fluctuating charge model (Effective Polarizable Bond or EPB). In this protocol, the electrostatic energy is obtained by numerical interaction of the atomic forces along the MD trajectory, rather than using the default Coulomb formula in the EPB model. Test study on the benchmark Barnase-Barstar protein-protein interaction system demonstrates that although the total electrostatic energy of the system shows little deviation due to the averaging effect, specific residue-residue electrostatic interaction energy is affected and the level of the effect depends on the charges of the interacting residues with charged residues showing pronounced differences in calculated energies between using the current protocol and the standard Coulomb formula. It is recommended that the proposed numerical interaction method should be preferred in the calculation of electrostatic energy in fluctuating charge models used in molecular dynamics simulations.

Drude Polarizable Force Field Parametrization of Carboxylate and N-Acetyl Amine Carbohydrate Derivatives

In this work, we report the development of Drude polarizable force field parameters for the carboxylate and N-acetyl amine derivatives, extending the functionality of the existing Drude polarizable carbohydrate force field. The force field parameters have been developed in a hierarchical manner, reproducing the quantum mechanical gas-phase properties of small model compounds representing the key functional group in the carbohydrate derivatives, including optimization of the electrostatic and bonded parameters. The optimized parameters were then used to generate the models for carboxylate and N-acetyl amine carbohydrate derivatives. The transferred parameters were further tested and optimized to reproduce crystal geometries and J-coupling data from nuclear magnetic resonance experiments. The parameter development resulted in the incorporation of d-glucuronate, l-iduronate, N-acetyl-d-glucosamine (GlcNAc), and N-acetyl-d-galactosamine (GalNAc) sugars into the Drude polarizable force field. The parameters developed in this study were then applied to study the conformational properties of glycosaminoglycan polymer hyaluronan, composed of d-glucuronate and N-acetyl-d-glucosamine, in aqueous solution. Upon comparing the results from the additive and polarizable simulations, it was found that the inclusion of polarization improved the description of the electrostatic interactions observed in hyaluronan, resulting in enhanced conformational flexibility. The developed Drude polarizable force field parameters in conjunction with the remainder of the Drude polarizable force field parameters can be used for future studies involving carbohydrates and their conjugates in complex, heterogeneous systems.

A Java Application to Characterize Biomolecules and Nanomaterials in Electrolyte Aqueous Solutions

The electrostatic, entropic and surface interactions between a macroion (nanoparticle or biomolecule), surrounding ions and water molecules play a fundamental role in the behavior and function of colloidal systems. However, the molecular mechanisms governing these phenomena are still poorly understood. One of the major limitations in procuring this understanding is the lack of appropriate computational tools. Additionally, only experts in the field with an extensive background in programming, who are trained in statistical mechanics, and have access to supercomputers are able to study these systems. To overcome these limitations, in this article, we present a free, multiplatform, portable Java software, which provides experts and non-experts in the field an easy and efficient way to obtain an accurate molecular characterization of electrical and structural properties of aqueous electrolyte mixture solutions around both cylindrical- and spherical-like rigid macroions under multiple conditions. These properties include the normalized ions and water density profile distributions, the mean electrostatic potential, the integrated charge, the zeta potential, the electrostatic potential energy, the particle crowding entropy energy, the ion-ion electrostatic direct correlation energy, and the ionic potential of mean force. The Java software does not require outstanding skills and comes with detailed user-guide documentation. The application is based on the so-called Classical Density Functional Theory Solver (CSDFTS), which was successfully applied to a variety of rod-like biopolymers, rigid-like globular proteins, nanoparticles, and nano-rods. CSDFTS implements several electrolyte and macroion models, uses different levels of approximation and takes advantage of high performance Fortran90 routines and optimized libraries. These features enable the software to run on single processor computers at low-to-moderate computational cost depending on the computer performance, the grid resolution, and the characterization of the macroion and the electrolyte solution, among other factors. As a unique feature, the software comes with a graphical user interface (GUI) that allows users to take advantage of the visually guided setup of the required input data to properly characterize the system and configure the solver. Several examples on nanomaterials and biomolecules are provided to illustrate the use of the GUI and the solver performance.

Structural Insights into Hearing Loss Genetics from Polarizable Protein Repacking

Hearing loss is associated with ∼8100 mutations in 152 genes, and within the coding regions of these genes are over 60,000 missense variants. The majority of these variants are classified as "variants of uncertain significance" to reflect our inability to ascribe a phenotypic effect to the observed amino acid change. A promising source of pathogenicity information is biophysical simulation, although input protein structures often contain defects because of limitations in experimental data and/or only distant homology to a template. Here, we combine the polarizable atomic multipole optimized energetics for biomolecular applications force field, many-body optimization theory, and graphical processing unit acceleration to repack all deafness-associated proteins and thereby improve average structure MolProbity score from 2.2 to 1.0. We then used these optimized wild-type models to create over 60,000 structures for missense variants in the Deafness Variation Database, which are being incorporated into the Deafness Variation Database to inform deafness pathogenicity prediction. Finally, this work demonstrates that advanced polarizable atomic multipole force fields are efficient enough to repack the entire human proteome.

Tubulin response to intense nanosecond-scale electric field in molecular dynamics simulation

Intense pulsed electric fields are known to act at the cell membrane level and are already being exploited in biomedical and biotechnological applications. However, it is not clear if electric pulses within biomedically-attainable parameters could directly influence intra-cellular components such as cytoskeletal proteins. If so, a molecular mechanism of action could be uncovered for therapeutic applications of such electric fields. To help clarify this question, we first identified that a tubulin heterodimer is a natural biological target for intense electric fields due to its exceptional electric properties and crucial roles played in cell division. Using molecular dynamics simulations, we then demonstrated that an intense - yet experimentally attainable - electric field of nanosecond duration can affect the bβ-tubulin’s C-terminus conformations and also influence local electrostatic properties at the GTPase as well as the binding sites of major tubulin drugs site. Our results suggest that intense nanosecond electric pulses could be used for physical modulation of microtubule dynamics. Since a nanosecond pulsed electric field can penetrate the tissues and cellular membranes due to its broadband spectrum, our results are also potentially significant for the development of new therapeutic protocols.

A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness

The challenge of quantifying uncertainty propagation in real-world systems is rooted in the high-dimensionality of the stochastic input and the frequent lack of explicit knowledge of its probability distribution. Traditional approaches show limitations for such problems, especially when the size of the training data is limited. To address these difficulties, we have developed a general framework of constructing surrogate models on spaces of stochastic input with arbitrary probability measure irrespective of the mutual dependencies between individual components of the random inputs and the analytical form. The present Data-driven Sparsity-enhancing Rotation for Arbitrary Randomness (DSRAR) framework includes a data-driven construction of multivariate polynomial basis for arbitrary mutually dependent probability measures and a sparsity enhancement rotation procedure. This sparsity-enhancing rotation method was initially proposed in our previous work [1] for Gaussian density distributions, which may not be feasible for non-Gaussian distributions due to the loss of orthogonality after the rotation. To remedy such difficulties, we developed a new data-driven approach to construct orthonormal polynomials for arbitrary mutually dependent randomness, ensuring the constructed basis maintains the orthogonality/near-orthogonality with respect to the density of the rotated random vector, where directly applying the regular polynomial chaos including arbitrary polynomial chaos (aPC) [2] shows limitations due to the assumption of the mutual independence between the components of the random inputs. The developed DSRAR framework leads to accurate recovery, with only limited training data, of a sparse representation of the target functions. The effectiveness of our method is demonstrated in challenging problems such as partial differential equations and realistic molecular systems within high-dimensional (O(10)) conformational spaces where the underlying density is implicitly represented by a large collection of sample data, as well as systems with explicitly given non-Gaussian probabilistic measures.

Polarizable Force Fields for Biomolecular Simulations: Recent Advances and Applications

Realistic modeling of biomolecular systems requires an accurate treatment of electrostatics, including electronic polarization. Due to recent advances in physical models, simulation algorithms, and computing hardware, biomolecular simulations with advanced force fields at biologically relevant timescales are becoming increasingly promising. These advancements have not only led to new biophysical insights but also afforded opportunities to advance our understanding of fundamental intermolecular forces. This article describes the recent advances and applications, as well as future directions, of polarizable force fields in biomolecular simulations.

Improvements to the ABSINTH Force Field for Proteins Based on Experimentally Derived Amino Acid Specific Backbone Conformational Statistics

We present an improved version of the ABSINTH implicit solvation model and force field paradigm (termed ABSINTH-C) by incorporating a grid-based term that bootstraps against experimentally derived and computationally optimized conformational statistics for blocked amino acids. These statistics provide high-resolution descriptions of the intrinsic backbone dihedral angle preferences for all 20 amino acids. The original ABSINTH model generates Ramachandran plots that are too shallow in terms of the basin structures and too permissive in terms of dihedral angle preferences. We bootstrap against the reference optimized landscapes and incorporate CMAP-like residue-specific terms that help us reproduce the intrinsic dihedral angle preferences of individual amino acids. These corrections that lead to ABSINTH-C are achieved by balancing the incorporation of the new residue-specific terms with the accuracies inherent to the original ABSINTH model. We demonstrate the robustness of ABSINTH-C through a series of examples to highlight the preservation of accuracies as well as examples that demonstrate the improvements. Our efforts show how the recent experimentally derived and computationally optimized coil-library landscapes can be used as a touchstone for quantifying errors and making improvements to molecular mechanics force fields.