Evaluation of Generalized Born Models for Large Scale Affinity Prediction of Cyclodextrin Host–Guest Complexes

Computational and experimental analysis of Luteolin-β-cyclodextrin supramolecular complexes: Insights into conformational dynamics and phase solubility

Investigating the structural stability of poorly-soluble luteolin (LuT) after encapsulation within cyclodextrins (CDs) is crucial for unlocking the therapeutic potential of LuT bioactive molecule. Herein, native and modified β-CD were employed to investigate LuT inclusion complex formation. Molecular mechanics (MM) and quantum mechanics (QM) were utilized for structural dynamics analysis. Microsecond timescale MD simulations yielded insights into LuT-CD interactions. The binding affinity between LuT and selected β-CDs was assessed by calculating the binding free energy using MM-PBSA and umbrella sampling simulations. The MM-PBSA results indicated that Heptakis-O-(2-hydroxypropyl)-β-CD (HP-β-CD) (-82.59+/-11.67 kJ/mol) and Di-O-methyl-β-CD (DM-β-CD) (-54.01+/-11.07 kJ/mol) exhibited good binding affinity for LuT. Subsequently, derivative screening of HP-β-CD revealed that only 2-HP-β-CD (HP-β-CD-1)/LuT (-21.38 kJ/mol) displayed a superior binding free energy (obtained from umbrella sampling) than HP-β-CD/LuT (-19.15 kJ/mol) inclusion complex. We conducted QM calculations on the top three complexes namelly HP-β-CD, DM-β-CD, and HP-β-CD-1 employing wB97X-D/6-311 + G(d,p) model chemistry to strengthen the MM results. The computational analysis aligns with experimental findings (phase solubility analysis), validating HP-β-CD-1 as most effective cavitand molecule for improving the solubility of LuT. This study offers critical structural insights for developing novel HP-β-CD derivatives with enhanced host capacity to encapsulate guest molecules efficiently.

Calculation of protein-ligand binding entropies using a rule-based molecular fingerprint

The use of fast in silico prediction methods for protein-ligand binding free energies holds significant promise for the initial phases of drug development. Numerous traditional physics-based models (e.g., implicit solvent models), however, tend to either neglect or heavily approximate entropic contributions to binding due to their computational complexity. Consequently, such methods often yield imprecise assessments of binding strength. Machine learning models provide accurate predictions and can often outperform physics-based models. They, however, are often prone to overfitting, and the interpretation of their results can be difficult. Physics-guided machine learning models combine the consistency of physics-based models with the accuracy of modern data-driven algorithms. This work integrates physics-based model conformational entropies into a graph convolutional network. We introduce a new neural network architecture (a rule-based graph convolutional network) that generates molecular fingerprints according to predefined rules specifically optimized for binding free energy calculations. Our results on 100 small host-guest systems demonstrate significant improvements in convergence and preventing overfitting. We additionally demonstrate the transferability of our proposed hybrid model by training it on the aforementioned host-guest systems and then testing it on six unrelated protein-ligand systems. Our new model shows little difference in training set accuracy compared to a previous model but an order-of-magnitude improvement in test set accuracy. Finally, we show how the results of our hybrid model can be interpreted in a straightforward fashion.

In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations

The Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge series is focused on identifying small molecule inhibitors of protein targets using computational methods. Each challenge contains two phases, hit-finding and follow-up optimization, each of which is followed by experimental validation of the computational predictions. For the CACHE Challenge #1, the Leucine-Rich Repeat Kinase 2 (LRRK2) WD40 Repeat (WDR) domain was selected as the target for in silico hit-finding and optimization. Mutations in LRRK2 are the most common genetic cause of the familial form of Parkinson's disease. The LRRK2 WDR domain is an understudied drug target with no known molecular inhibitors. Herein we detail the first phase of our winning submission to the CACHE Challenge #1. We developed a framework for the high-throughput structure-based virtual screening of a chemically diverse small molecule space. Hit identification was performed using the large-scale Deep Docking (DD) protocol followed by absolute binding free energy (ABFE) simulations. ABFEs were computed using an automated molecular dynamics (MD)-based thermodynamic integration (TI) approach. 4.1 billion ligands from Enamine REAL were screened with DD followed by ABFEs computed by MD TI for 793 ligands. 76 ligands were prioritized for experimental validation, with 59 compounds successfully synthesized and 5 compounds identified as hits, yielding a 8.5% hit rate. Our results demonstrate the efficacy of the combined DD and ABFE approaches for hit identification for a target with no previously known hits. This approach is widely applicable for the efficient screening of ultra-large chemical libraries as well as rigorous protein-ligand binding affinity estimation leveraging modern computational resources.

Tuning Potential Functions to Host-Guest Binding Data

Software to more rapidly and accurately predict protein-ligand binding affinities is of high interest for early-stage drug discovery, and physics-based methods are among the most widely used technologies for this purpose. The accuracy of these methods depends critically on the accuracy of the potential functions that they use. Potential functions are typically trained against a combination of quantum chemical and experimental data. However, although binding affinities are among the most important quantities to predict, experimental binding affinities have not to date been integrated into the experimental data set used to train potential functions. In recent years, the use of host-guest complexes as simple and tractable models of binding thermodynamics has gained popularity due to their small size and simplicity, relative to protein-ligand systems. Host-guest complexes can also avoid ambiguities that arise in protein-ligand systems such as uncertain protonation states. Thus, experimental host-guest binding data are an appealing additional data type to integrate into the experimental data set used to optimize potential functions. Here, we report the extension of the Open Force Field Evaluator framework to enable the systematic calculation of host-guest binding free energies and their gradients with respect to force field parameters, coupled with the curation of 126 host-guest complexes with available experimental binding free energies. As an initial application of this novel infrastructure, we optimized generalized Born (GB) cavity radii for the OBC2 GB implicit solvent model against experimental data for 36 host-guest systems. This refitting led to a dramatic improvement in accuracy for both the training set and a separate test set with 90 additional host-guest systems. The optimized radii also showed encouraging transferability from host-guest systems to 59 protein-ligand systems. However, the new radii are significantly smaller than the baseline radii and lead to excessively favorable hydration free energies (HFEs). Thus, users of the OBC2 GB model currently may choose between GB cavity radii that yield more accurate binding affinities and GB cavity radii that yield more accurate HFEs. We suspect that achieving good accuracy on both will require more far-reaching adjustments to the GB model. We note that binding free-energy calculations using the OBC2 model in OpenMM gain about a 10× speedup relative to corresponding explicit solvent calculations, suggesting a future role for implicit solvent absolute binding free-energy (ABFE) calculations in virtual compound screening. This study proves the principle of using host-guest systems to train potential functions that are transferrable to protein-ligand systems and provides an infrastructure that enables a range of applications.

Highly robust quantum mechanics and umbrella sampling studies on inclusion complexes of curcumin and β-cyclodextrin

The poor aqueous solubility of curcumin (CUR) obstructs its wide utilization in nutraceuticals, cosmetics, and pharmaceutical companies. This study is dedicated to investigate the stability of CUR inside the hydrophobic pocket of β-cyclodextrin (β-CD), hydroxypropyl-β-CD (HP-β-CD), and 2,6-Di-O-methyl-β-CD (DM-β-CD). Initially, molecular mechanics (MM) calculations and subsequently quantum mechanical (QM) calculations were performed on inclusion complexes to strengthen the MM results. We performed microsecond timescale MD simulations to observe the structural dynamics of CUR inside the cavity of CDs. We elucidated the most stable binding orientations of CUR inside the cavity of CDs based on binding free energy obtained from the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) and umbrella sampling simulations. Furthermore, the two-layered ONIOM (B3LYP/6-311+G(2d,p):PM7) method with CPCM implicit water model was used to derive the complete energetics and thermodynamics of inclusion complexes at 1:1 stoichiometry. Each inclusion reaction was exothermic and spontaneous. The chemical reactivity and kinetic stability of inclusion complexes were described by HOMO-LUMO molecular orbital energies. In conclusion, our studies revealed that HP-β-CD had the highest binding affinity for CUR with the most negative complexation energy (-6520.69 kJ/mol) and Gibb's free energy change (-6448.20 kJ/mol). The atomic-level investigation of noncovalent interactions between CUR and the hydroxypropyl groups in HP-β-CD/CUR complex may be helpful to drive new derivatives of HP-β-CD with better host capacity. The computational strategy adopted here might serve as a benchmark for increasing the solubility of numerous clinically significant molecules.

Dispersion-corrected DFT calculations and umbrella sampling simulations to investigate stability of Chrysin-cyclodextrin inclusion complexes

The study of inclusion complexes of Chrysin (ChR) with three forms of cyclodextrins (CDs) α-, β-, and γ-CD was accomplished to examine the stability of ChR inside the central cavities of CDs. The aim of study was to identify the most suitable form of CD to improve the hydro-solubility of poorly soluble ChR bioactive molecule. Microsecond timescale molecular dynamics (MD) simulations were performed on four inclusion complexes (α-CD/ChR, β-CD/ChR, and two conformations of γ-CD/ChR) to examine the dynamics of ChR inside the cavity of CDs. The first conformation of γ-CD/ChR inclusion complex (γ-CD1/ChR) was identified to possess the highest affinity between host and guest molecule on the basis of binding energy calculated by employing Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) and umbrella sampling simulations. To further strengthen the claims of classical and biased MD studies, Our own N-layered Integrated molecular Orbital and Molecular mechanics (ONIOM) (wB97XD/6-311+g(d,p):pm7) calculations were performed on the selected inclusion complexes. The ONIOM based complexation energy reaffirmed that ChR had highest affinity for the γ-CD1 host molecule. Further, the non-covalent interaction analysis was conducted using Multiwfn software on QM-optimized inclusion complexes with wB97XD/6-311+G(d,p) model chemistry, revealing non-covalent interactions between ChR and CDs. This atomic level information helped us to gain better insights into critical atoms of ChR and CD that participated in intermolecular interactions and identify γ-CD as a suitable host molecule for improving the hydro-solubulity of ChR. The structural insights would help to derive new derivatives of γ-CD with better host capacity.

Host Dynamics under General-Purpose Force Fields

Macrocyclic hosts as prototypical receptors to gaseous and drug-like guests are crucial components in pharmaceutical research. The external guests are often coordinated at the center of these macromolecular containers. The formation of host-guest coordination is accompanied by the broken of host-water and host-ion interactions and sometimes also involves some conformational rearrangements of the host. A balanced description of various components of interacting terms is indispensable. However, up to now, the modeling community still lacks a general yet detailed understanding of commonly employed general-purpose force fields and the host dynamics produced by these popular selections. To fill this critical gap, in this paper, we profile the energetics and dynamics of four types of popular macrocycles, including cucurbiturils, pillararenes, cyclodextrins, and octa acids. The presented investigations of force field definitions, refitting, and evaluations are unprecedently detailed. Based on the valuable observations and insightful explanations, we finally summarize some general guidelines on force field parametrization and selection in host-guest modeling.

A comparative study on inclusion complex formation between formononetin and β-cyclodextrin derivatives through multiscale classical and umbrella sampling simulations

Formononetin, a naturally occurring isoflavone exhibits a wide range of therapeutic applications including antioxidant, anti-tumor, antiviral, anti-diabetic and neuroprotective activities. However, the low hydro-solubility of formononetin has limited its prospective use in cosmetic, neutraceutical and pharmaceutical industries. Cyclodextrins (CDs), especially β-CD and its derivatives have emerged as promising agents to improve the water solubility of poorly hydrosoluble compounds by the formation of inclusion complexes. We employed multiscale (1000 ns) explicit solvent and umbrella sampling molecular dynamics (MD) simulations to study the interactions and thermodynamic parameters of inclusion complex formation between formononetin and five most commonly used β-CD derivatives. Classical MD simulations revealed two possible binding conformations of formononetin inside the central cavity of hydroxypropyl-β-CD (HP-β-CD), randomly methylated-β-CD (ME-β-CD), and sulfobutylether-β-CD (SBE-β-CD). The binding conformation with the benzopyrone ring of formononetin inside the central cavity of β-CD derivatives was more frequent than the phenyl group occupying the hydrophobic cavity. These interactions were supported by a variety of non-bonded contacts including hydrogen bonds, pi-lone pair, pi-sigma, and pi-alkyl interactions. Formononetin showed favorable end-state MD-driven thermodynamic binding free energies with all the selected β-CD derivatives, except succinyl-β-CD (S-β-CD). Furthermore, umbrella sampling simulations were used to investigate the interactions and thermodynamic parameters of the host-guest inclusion complexes. The SBE-β-CD/formononetin inclusion complex showed the lowest binding energy signifying the highest affinity among all the selected host-guest inclusion complexes. Our study could be used as a standard for analyzing and comparing the ability of different β-CD derivatives to enhance the hydro-solubility of poorly soluble molecules.

Comparative Interaction Studies of Quercetin with 2-Hydroxyl-propyl-β-cyclodextrin and 2,6-Methylated-β-cyclodextrin

Quercetin (QUE) is a well-known natural product that can exert beneficial properties on human health. However, due to its low solubility its bioavailability is limited. In the present study, we examine whether its formulation with two cyclodextrins (CDs) may enhance its pharmacological profile. Comparative interaction studies of quercetin with 2-hydroxyl-propyl-β-cyclodextrin (2HP-β-CD) and 2,6-methylated cyclodextrin (2,6Me-β-CD) were performed using NMR spectroscopy, DFT calculations, and in silico molecular dynamics (MD) simulations. Using T1 relaxation experiments and 2D DOSY it was illustrated that both cyclodextrin vehicles can host quercetin. Quantum mechanical calculations showed the formation of hydrogen bonds between QUE with 2HP-β-CD and 2,6Μe-β-CD. Six hydrogen bonds are formed ranging between 2 to 2.8 Å with 2HP-β-CD and four hydrogen bonds within 2.8 Å with 2,6Μe-β-CD. Calculations of absolute binding free energies show that quercetin binds favorably to both 2,6Me-β-CD and 2HP-β-CD. MM/GBSA results show equally favorable binding of quercetin in the two CDs. Fluorescence spectroscopy shows moderate binding of quercetin in 2HP-β-CD (520 M-1) and 2,6Me-β-CD (770 M-1). Thus, we propose that both formulations (2HP-β-CD:quercetin, 2,6Me-β-CD:quercetin) could be further explored and exploited as small molecule carriers in biological studies.

Modeling and Optimisation of Comlexity by the β-Cyclodextrin of an Organic Pollutant Model: m-Methyl Red

Studies of cyclodextrin chemistry using quantum chemical methods are mainly adopted to investigate the formation of the inclusion complex causing changes in the physicochemical properties of the cyclodextrin guest. In this paper, we conducted a computational modeling study of the inclusion complexes of β-cyclodextrin (β-CD) with m-Methyl Red (m-MR) by using parametric method 6 (PM6), the semi empirical molecular orbital calculations and the natural bond orbital method (NBO). The inclusion process is carried out by maintaining the coordinates of the β-CD fixed and by displacing the guest molecule. The different relative positions between m-MR and β-CD are measured with respect to the distance between the reference atom (N) in the guest molecule and the origin of the coordinates from the equatorial plane of β-CD. The m-MR/β-CD (B) inclusion complex has a lower negative value of ΔG compared to another m-MR/β-CD (A) complex, highlighting the spontaneous behavior of the inclusion process. In addition, during the process of inclusion, the complexation energy is negative, which allows us to affirm that the complexation of m-MR in the β-CD is thermodynamically favorable. Among two directions A and B, the minimum energy generated from the PM6 was obtained in the orientation B and the guest molecule is partially encapsulated in the cavity of β-CD. In the NBO analysis, the stabilization energy is also usually used to characterize the hydrogen bond interaction between a lone pair (LP(Y)) of an atom Y and an anti-bonding orbital (BD٭(X-H)).

Computational Investigation of Structural Basis for Enhanced Binding of Isoflavone Analogues with Mitochondrial Aldehyde Dehydrogenase

Isoflavone compounds are potent inhibitors against mitochondrial aldehyde dehydrogenase (ALDH2) for the treatment of alcoholism and drug addiction, and an in-depth understanding of the underlying structural basis helps design new inhibitors for enhanced binding. Here, we investigated the binding poses and strengths of eight isoflavone analogues (including CVT-10216 and daidzin) with ALDH2 via computational methods of molecular docking, molecular dynamics (MD) simulation, molecular mechanics Poisson-Boltzmann surface area (MM-PBSA), steered MD, and umbrella sampling. Neither the Vina scoring of docked and MD-sampled complexes nor the nonbonded protein-inhibitor interaction energy from MD simulations is able to reproduce the relative binding strength of the inhibitors compared to experimental IC₅₀ values. Considering the solvation contribution, MM-PBSA and relatively expensive umbrella sampling yield good performance for the relative binding (free) energies. The isoflavone skeleton prefers to form π-π stacking, π-sulfur, and π-alkyl interactions with planar (Phe and Trp) or sulfur-containing (Cys and Met) residues. The enhanced inhibition of CVT-10216 originates from both end groups of the isoflavone skeleton offering strong van der Waals contacts and from the methylsulfonamide group at the 4' position by hydrogen bonding (HB) with neighboring receptor residues. These results indicate that the hydrophobic binding tunnel of ALDH2 is larger than the isoflavone skeleton in length and thus an extended hydrophobic core is likely a premise for potent inhibitors.

Toward a Computational Ecotoxicity Assay

Thousands of anthropogenic chemicals are released into the environment each year, posing potential hazards to human and environmental health. Toxic chemicals may cause a variety of adverse health effects, triggering immediate symptoms or delayed effects over longer periods of time. It is thus crucial to develop methods that can rapidly screen and predict the toxicity of chemicals to limit the potential harmful impacts of chemical pollutants. Computational methods are being increasingly used in toxicity predictions. Here, the method of molecular docking is assessed for screening potential toxicity of a variety of xenobiotic compounds, including pesticides, pharmaceuticals, pollutants, and toxins derived from the chemical industry. The method predicts the binding energy of pollutants to a set of carefully selected receptors under the assumption that toxicity in many cases is related to interference with biochemical pathways. The strength of the applied method lies in its rapid generation of interaction maps between potential toxins and the targeted enzymes, which could quickly yield molecular-level information and insight into potential perturbation pathways, aiding in the prioritization of chemicals for further tests. Two scoring functions are compared: Autodock Vina and the machine-learning scoring function RF-Score-VS. The results are promising, although hampered by the accuracy of the scoring functions. The strengths and weaknesses of the docking protocol are discussed, as well as future directions for improving the accuracy for the purpose of toxicity predictions.

"Big Data" Fast Chemoinformatics Model to Predict Generalized Born Radius and Solvent Accessibility as a Function of Geometry

The Generalized Born (GB) solvent model is offering the best accuracy/computing effort ratio yet requires drastic simplifications to estimate of the Effective Born Radii (EBR) in bypassing a too expensive volume integration step. EBRs are a measure of the degree of burial of an atom and not very sensitive to small changes of geometry: in molecular dynamics, the costly EBR update procedure is not mandatory at every step. This work however aims at implementing a GB model into the Sampler for Multiple Protein-Ligand Entities (S4MPLE) evolutionary algorithm with mandatory EBR updates at each step triggering arbitrarily large geometric changes. Therefore, a quantitative structure-property relationship has been developed in order to express the EBRs as a linear function of both the topological neighborhood and geometric occupancy of the space around atoms. A training set of 810 molecular systems, starting from fragment-like to drug-like compounds, proteins, host-guest systems, and ligand-protein complexes, has been compiled. For each species, S4MPLE generated several hundreds of random conformers. For each atom in each geometry of each species, its "standard" EBR was calculated by numeric integration and associated to topological and geometric descriptors of the atom neighborhood. This training set (EBR, atom descriptors) involving >5 M entries was subjected to a boot-strapping multilinear regression process with descriptor selection. In parallel, the strategy was repurposed to also learn atomic solvent-accessible areas (SA) based on the same descriptors. Resulting linear equations were challenged to predict EBR and SA values for a similarly compiled external set of >2000 new molecular systems. Solvation energies calculated with estimated EBR and SA match "standard" energies within the typical error of a force-field-based approach (a few kilocalories per mole). Given the extreme diversity of molecular systems covered by the model, this simple EBR/SA estimator covers a vast applicability domain.

Optimization of Protein-Ligand Electrostatic Interactions Using an Alchemical Free-Energy Method

We present an explicit solvent alchemical free-energy method for optimizing the partial charges of a ligand to maximize the binding affinity with a receptor. This methodology can be applied to known ligand–protein complexes to determine an optimized set of ligand partial atomic changes. Three protein–ligand complexes have been optimized in this work: FXa, P38, and the androgen receptor. The sets of optimized charges can be used to identify design principles for chemical changes to the ligands which improve the binding affinity for all three systems. In this work, beneficial chemical mutations are generated from these principles and the resulting molecules tested using free-energy perturbation calculations. We show that three quarters of our chemical changes are predicted to improve the binding affinity, with an average improvement for the beneficial mutations of approximately 1 kcal/mol. In the cases where experimental data are available, the agreement between prediction and experiment is also good. The results demonstrate that charge optimization in explicit solvent is a useful tool for predicting beneficial chemical changes such as pyridinations, fluorinations, and oxygen to sulfur mutations.

Role of Displacing Confined Solvent in the Conformational Equilibrium of β-Cyclodextrin

This study investigates the role of hydration and its relationship to the conformational equilibrium of the host molecule β-cyclodextrin. Molecular dynamics simulations indicate that the unbound β-cyclodextrin exhibits two state behavior in explicit solvent due to the opening and closing of its cavity. In implicit solvent, these transitions are not observed, and there is one dominant conformation of β-cyclodextrin with an open cavity. Based on these observations, we investigate the hypothesis that the expulsion of thermodynamically unfavorable water molecules into the bulk plays an important role in controlling the accessibility of the closed macrostate at room temperature. We compare the results of the molecular mechanics analytical generalized Born plus nonpolar solvation approach to those obtained through grid inhomogeneous solvation theory analysis with explicit solvation to elucidate the thermodynamic forces at play. The work illustrates the use of continuum solvent models to tease out solvation effects related to the inhomogeneity and the molecular nature of water and demonstrates the key role of the thermodynamics of enclosed hydration in driving the conformational equilibrium of molecules in solution.

Affinity Calculations of Cyclodextrin Host-Guest Complexes: Assessment of Strengths and Weaknesses of End-Point Free Energy Methods

The end-point methods like MM/PBSA or MM/GBSA estimate the free energy of a biomolecule by combining its molecular mechanics energy with solvation free energy and entropy terms. On the one hand, their performance largely depends on the particular system of interest, and despite numerous attempts to improve their reliability that have resulted in many variants, there is still no clear alternative to improve their accuracy. On the other hand, the relatively small cyclodextrin host-guest complexes, for which high-quality binding calorimetric data are usually available, are becoming reference models for testing the accuracy of free energy methods. In this work, we further assess the performance of various MM/PBSA-like approaches as applied to cyclodextrin complexes. To this end, we select a set of complexes between β-cyclodextrin and 57 small organic molecules that has been previously studied with the binding energy distribution analysis method in combination with an implicit solvent model ( Wickstrom, L.; He, P.; Gallicchio, E.; Levy, R. M. J. Chem. Theory Comput. 2013 , 9 , 3136 - 3150 ). For each complex, a conventional 1.0 μs molecular dynamics simulation in explicit solvent is performed. Then we employ semiempirical quantum chemical calculations, several variants of the MM-PB(GB)SA methods, entropy estimations, etc., to assess the reliability of the end-point affinity calculations. The best end-point protocol in this study, which combines DFTB3 energies with entropy corrections, yields estimations of the binding free energies that still have substantial errors (RMSE = 2.2 kcal/mol), but it exhibits a good prediction capacity in terms of ligand ranking ( R² = 0.66) that is close to or even better than that of rigorous free energy methodologies. Our results can be helpful to discriminate between the intrinsic limitations of the end-point methods and other sources of error, such as the underlying energy and continuum solvation methods.

Binding Thermodynamics and Kinetics Calculations Using Chemical Host and Guest: A Comprehensive Picture of Molecular Recognition

Understanding the fine balance between changes of entropy and enthalpy and the competition between a guest and water molecules in molecular binding is crucial in fundamental studies and practical applications. Experiments provide measurements. However, illustrating the binding/unbinding processes gives a complete picture of molecular recognition not directly available from experiments, and computational methods bridge the gaps. Here, we investigated guest association/dissociation with β-cyclodextrin (β-CD) by using microsecond-time-scale molecular dynamics (MD) simulations, postanalysis and numerical calculations. We computed association and dissociation rate constants, enthalpy, and solvent and solute entropy of binding. All the computed values of kon, koff, ΔH, ΔS, and ΔG using GAFF-CD and q4MD-CD force fields for β-CD could be compared with experimental data directly and agreed reasonably with experiment findings. In addition, our study further interprets experiments. Both force fields resulted in similar computed ΔG from independently computed kinetics rates, ΔG = -RT ln(kon·C0/koff), and thermodynamics properties, ΔG = ΔH - TΔS. The water entropy calculations show that the entropy gain of desolvating water molecules are a major driving force, and both force fields have the same strength of nonpolar attractions between solutes and β-CD as well. Water molecules play a crucial role in guest binding to β-CD. However, collective water/β-CD motions could contribute to different computed kon and ΔH values by different force fields, mainly because the parameters of β-CD provide different motions of β-CD, hydrogen-bond networks of water molecules in the cavity of free β-CD, and strength of desolvation penalty. As a result, q4MD-CD suggests that guest binding is mostly driven by enthalpy, while GAFF-CD shows that gaining entropy is the major driving force of binding. The study deepens our understanding of ligand-receptor recognition and suggests strategies for force field parametrization for accurately modeling molecular systems.

Evaluating Force Field Performance in Thermodynamic Calculations of Cyclodextrin Host-Guest Binding: Water Models, Partial Charges, and Host Force Field Parameters

Computational prediction of noncovalent binding free energies with methods based on molecular mechanical force fields has become increasingly routine in drug discovery projects, where they promise to speed the discovery of small molecule ligands to bind targeted proteins with high affinity. Because the reliability of free energy methods still has significant room for improvement, new force fields, or modifications of existing ones, are regularly introduced with the aim of improving the accuracy of molecular simulations. However, comparatively little work has been done to systematically assess how well force fields perform, particularly in relation to the calculation of binding affinities. Hardware advances have made these calculations feasible, but comprehensive force field assessments for protein–ligand sized systems still remain costly. Here, we turn to cyclodextrin host–guest systems, which feature many hallmarks of protein–ligand binding interactions but are generally much more tractable due to their small size. We present absolute binding free energy and enthalpy calculations, using the attach-pull-release (APR) approach, on a set of 43 cyclodextrin-guest pairs for which experimental ITC data are available. The test set comprises both α- and β-cyclodextrin hosts binding a series of small organic guests, each with one of three functional groups: ammonium, alcohol, or carboxylate. Four water models are considered (TIP3P, TIP4Pew, SPC/E, and OPC), along with two partial charge assignment procedures (RESP and AM1-BCC) and two cyclodextrin host force fields. The results suggest a complex set of considerations when choosing a force field for biomolecular simulations. For example, some force field combinations clearly outperform others at the binding enthalpy calculations but not for the binding free energy. Additionally, a force field combination which we expected to be the worst performer gave the most accurate binding free energies – but the least accurate binding enthalpies. The results have implications for the development of improved force fields, and we propose this test set, and potential future elaborations of it, as a powerful validation suite to evaluate new force fields and help guide future force field development.