Prediction of Solvation Free Energies with Thermodynamic Integration Using the General Amber Force Field

Assessment of Halogen Off-Center Point-Charge Models Using Explicit Solvent Simulations

Compounds containing halogens can form halogen bonds (XBs) with biological targets such as proteins and membranes due to their anisotropic electrostatic potential. To accurately describe this anisotropy, off-center point-charge (EP) models are commonly used in force field methods, allowing the description of XBs at the molecular mechanics and molecular dynamics level. Various EP implementations have been documented in the literature, and despite being efficient in reproducing protein-ligand geometries and sampling of XBs, it is unclear how well these EP models predict experimental properties such as hydration free energies (ΔGhyd), which are often used to validate force field performance. In this work, we report the first assessment of three EP models using alchemical free energy calculations to predict ΔGhyd values. We show that describing the halogen anisotropy using some EP models can lead to a slight improvement in the prediction of the ΔGhyd when compared with the models without EP, especially for the chlorinated compounds; however, this improvement is not related to the establishment of XBs but is most likely due to the improvement of the sampling of hydrogen bonds. We also highlight the importance of the choice of the EP model, especially for the iodinated molecules, since a slight tendency to improve the prediction is observed for compounds with a larger σ-hole but significantly worse results were obtained for compounds that are weaker XB donors.

Number of Hydrogen Bonds per Unit Solvent Accessible Surface Area: A Descriptor of Functional States of Proteins

Proteins function close to native and near-native conformations. These states are evolutionarily selected to ensure the effect of mutations is minimized. The structural organization of a protein is hierarchical and modular, which reduces the dimensionality of the configurational space of the native states. Thus, finding appropriate descriptors that define the native state among all possible states of a protein is a problem of immense interest. The present study explores the correlation between solvent accessible surface areas (SASAs) and different intraprotein as well as protein-water hydrogen bonds of 55 single-chain globular proteins from four different structural classes (all α, all β, α+β, and α/β), 16 multichain proteins, and 4 macromolecular complexes. A systematic analysis of the solvent accessible surface area and intraprotein and protein-water hydrogen bonds suggests a linear relationship between SASAs and hydrogen bonds. The number of protein-water hydrogen bonds per unit SASA ranges from 3 to 4 for all the different structural protein classes. In contrast, the number of intramolecular hydrogen bonds per unit SASA, including the mainchain-mainchain, mainchain-sidechain, and sidechain-sidechain, varies between 0.75 to 2. The solvation free energy of a protein linearly decreases with SASA. Our study also shows that the solvation free energy/SASA varies from -75 to -105 kJ mol^-1 nm^-2 across all the native states studied here. The number conservancy of intraprotein hydrogen bonds per unit SASA possibly imparts structural stability to the native structure. On the other hand, 3-4 protein-water hydrogen bonds per unit SASA are possibly required to maintain a balance between the solubility and functionality of the native states. This study provides a basis for synthetic biologists to design new folds with improved functionality.

Automated Molecular Cluster Growing for Explicit Solvation by Efficient Force Field and Tight Binding Methods

An automated and broadly applicable workflow for the description of solvation effects in an explicit manner is introduced. This method, termed quantum cluster growth (QCG), is based on the semiempirical GFN2-xTB/GFN-FF methods, enabling efficient geometry optimizations and MD simulations. Fast structure generation is provided using the intermolecular force field xTB-IFF. Additionally, the approach uses an efficient implicit solvation model for the electrostatic embedding of the growing clusters. The novel QCG procedure presents a robust cluster generation tool for subsequent application of higher-level (e.g., DFT) methods to study solvation effects on molecular geometries explicitly or to average spectroscopic properties over cluster ensembles. Furthermore, the computation of the solvation free energy with a supermolecular approach can be carried out with QCG. The underlying growing process is physically motivated by computing the leading-order solute-solvent interactions first and can account for conformational and chemical changes due to solvation for low-energy barrier processes. The conformational space is explored with the NCI-MTD algorithm as implemented in the CREST program, using a combination of metadynamics and MD simulations. QCG with GFN2-xTB yields realistic solution geometries and reasonable solvation free energies for various systems without introducing many empirical parameters. Computed IR spectra of some solutes with QCG show a better match to the experimental data compared to well-established implicit solvation models.

The automated optimisation of a coarse-grained force field using free energy data

Atomistic models provide a detailed representation of molecular systems, but are sometimes inadequate for simulations of large systems over long timescales. Coarse-grained models enable accelerated simulations by reducing the number of degrees of freedom, at the cost of reduced accuracy. New optimisation processes to parameterise these models could improve their quality and range of applicability. We present an automated approach for the optimisation of coarse-grained force fields, by reproducing free energy data derived from atomistic molecular simulations. To illustrate the approach, we implemented hydration free energy gradients as a new target for force field optimisation in ForceBalance and applied it successfully to optimise the un-charged side-chains and the protein backbone in the SIRAH protein coarse-grain force field. The optimised parameters closely reproduced hydration free energies of atomistic models and gave improved agreement with experiment.

Optimized Halogen Atomic Radii for PBSA Calculations Using Off-Center Point Charges

In force-field methods, the usage of off-center point charges, also called extra points (EPs), is a common strategy to tackle the anisotropy of the electrostatic potential of covalently bonded halogens (X), thus allowing the description of halogen bonds (XBs) at the molecular mechanics/molecular dynamics (MM/MD) level. Diverse EP implementations exist in the literature differing on the charge sets and/or the X-EP distances. Poisson-Boltzmann and surface area (PBSA) calculations can be used to obtain solvation free energies (ΔG_solv) of small molecules, often to compute binding free energies (ΔG_bind) at the MM-PBSA level. This method depends, among other parameters, on the empirical assignment of atomic radii (PB radii). Given the multiplicity of off-center point-charge models and the lack of specific PB radii for halogens compatible with such implementations, in this work, we assessed the performance of PBSA calculations for the estimation of ΔG_solv values in water (ΔG_hyd), also conducting an optimization of the halogen PB radii (Cl, Br, and I) for each EP model. We not only expand the usage of EP models in the scope of the general AMBER force field (GAFF) but also provide the first optimized halogen PB radii in the context of the CHARMM general force field (CGenFF), thus contributing to improving the description of halogenated compounds in PBSA calculations.

Parameterization of Monovalent Ions for the OPC3, OPC, TIP3P-FB, and TIP4P-FB Water Models

Monovalent ions play significant roles in various biological and material systems. Recently, four new water models (OPC3, OPC, TIP3P-FB, and TIP4P-FB), with significantly improved descriptions of condensed phase water, have been developed. The pairwise interaction between the metal ion and water necessitates the development of ion parameters specifically for these water models. Herein, we parameterized the 12-6 and the 12-6-4 nonbonded models for 12 monovalent ions with the respective four new water models. These monovalent ions contain eight cations including alkali metal ions (Li⁺, Na⁺, K⁺, Rb⁺, Cs⁺), transition-metal ions (Cu⁺ and Ag⁺), and Tl⁺ from the boron family, along with four halide anions (F^-, Cl^-, Br^-, I^-). Our parameters were designed to reproduce the target hydration free energies (the 12-6 hydration free energy (HFE) set), the ion-oxygen distances (the 12-6 ion-oxygen distance (IOD) set), or both of them (the 12-6-4 set). The 12-6-4 parameter set provides highly accurate structural features overcoming the limitations of the routinely used 12-6 nonbonded model for ions. Specifically, we note that the 12-6-4 parameter set is able to reproduce experimental hydration free energies within 1 kcal/mol and experimental ion-oxygen distances within 0.01 Å simultaneously. We further reproduced the experimentally determined activity derivatives for salt solutions, validating the ion parameters for simulations of ion pairs. The improved performance of the present water models over our previous parameter sets for the TIP3P, TIP4P, and SPC/E water models (Li, P. et al J. Chem. Theory Comput. 2015 11 1645 1657) highlights the importance of the choice of water model in conjunction with the metal ion parameter set.

Discovery of novel modulators targeting human TRPC5: Docking-based virtual screening, molecular dynamics simulation and binding affinity predication

Canonical transient receptor potential channel 5 (TRPC5) plays a key role in the regulation of central nervous system, cardiovascular system, kidney disease, cancer, and could be also involved in liver function, arthritis, diabetes-associated complications and so on. However, evidence of TRPC5 function on cellular or organismic levels is sparse. There is still a need for identifying novel and efficient TRPC5 channel modulators to study TRPC5 function. In this study, based on the hTRPC5 structure obtained by homology modeling and the predicted binding site, we have performed virtual screening of 212,736 compounds from the specs database（http://www.specs.net) to find potential hTRPC5 modulators. Lipinski and Veber rules, ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) and PAINS (Pan Assay Interference structures) filters were used to screen the large database. Further, multi-software combination docking, cluster analysis and interaction analysis were used to select 20 potential active candidates with novel skeleton. 4 Hits, bearing appreciable binding affinity with hTRPC5 were selected for 40ns all-atom molecular dynamics (MD) simulations under explicit water conditions. The MD simulation results suggested that the 4 Hits binding induces a slight structural change and stabilizes the hTRPC5 structure. In addition, decomposition free energy demonstrated that residues TRP434, LEU437, MET438, ALA441, ILE484, ILE487, LEU488, LEU491, LEU515, ILE517, LEU518, LEU521, PHE531, THR607, VAL610, ILE611, VAL615 played the critical role on system stability. 4 Hits, as potential modulators of hTRPC5, may be potential leads to develop effective therapeutics hTRPC5-associated diseases.

An Approach to Computing Solvent Reorganization Energy

A method for accurate calculation of reorganization free energy of an explicit solvent solvating a solute molecule is presented. The method relies on the knowledge of solvation free energy known either from experimental measurement or from accurate calculation. An important part of this approach lies in the calculation of entropy in solute-solvent interaction free energy using the interaction entropy method, combined with the calculation of enthalpy of solvent reorganization based on a finite number of solvent molecules near the solute molecule. This interaction entropy-solvent reorganization or IESR method enables one to compute both enthalpy and entropy components of the solvent reorganization energy from MD simulation of the solvated system. The calculated results are determined by the molecular force field only without any empirical parameter. The current method is applied to computing the reorganization energies of water solvent solvating a wide range of solute molecules including both hydrophilic and hydrophobic ones. The accuracy of the approach is indirectly verified by the excellent agreement of individual enthalpies and entropies of the solvation energies between theory and experiment.

A fast and high-quality charge model for the next generation general AMBER force field

The General AMBER Force Field (GAFF) has been broadly used by researchers all over the world to perform in silico simulations and modelings on diverse scientific topics, especially in the field of computer-aided drug design whose primary task is to accurately predict the affinity and selectivity of receptor-ligand binding. The atomic partial charges in GAFF and the second generation of GAFF (GAFF2) were originally developed with the quantum mechanics derived restrained electrostatic potential charge, but in practice, users usually adopt an efficient charge method, Austin Model 1-bond charge corrections (AM1-BCC), based on which, without expensive ab initio calculations, the atomic charges could be efficiently and conveniently obtained with the ANTECHAMBER module implemented in the AMBER software package. In this work, we developed a new set of BCC parameters specifically for GAFF2 using 442 neutral organic solutes covering diverse functional groups in aqueous solution. Compared to the original BCC parameter set, the new parameter set significantly reduced the mean unsigned error (MUE) of hydration free energies from 1.03 kcal/mol to 0.37 kcal/mol. More excitingly, this new AM1-BCC model also showed excellent performance in the solvation free energy (SFE) calculation on diverse solutes in various organic solvents across a range of different dielectric constants. In this large-scale test with totally 895 neutral organic solvent-solute systems, the new parameter set led to accurate SFE predictions with the MUE and the root-mean-square-error of 0.51 kcal/mol and 0.65 kcal/mol, respectively. This newly developed charge model, ABCG2, paved a promising path for the next generation GAFF development.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

Accurate ranking of compounds with regards to their binding affinity to a protein using computational methods is of great interest to pharmaceutical research. Physics-based free energy calculations are regarded as the most rigorous way to estimate binding affinity. In recent years, many retrospective studies carried out both in academia and industry have demonstrated its potential. Here, we present the results of large-scale prospective application of the FEP+ method in active drug discovery projects in an industry setting at Merck KGaA, Darmstadt, Germany. We compare these prospective data to results obtained on a new diverse, public benchmark of eight pharmaceutically relevant targets. Our results offer insights into the challenges faced when using free energy calculations in real-life drug discovery projects and identify limitations that could be tackled by future method development. The new public data set we provide to the community can support further method development and comparative benchmarking of free energy calculations.

Hybrid Alchemical Free Energy/Machine-Learning Methodology for the Computation of Hydration Free Energies

A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the FreeSolv database and retrospectively shown to outperform most submissions from the SAMPL4 competition. Compared to pure machine-learning approaches, FEP/ML yields more precise estimates of free energies of hydration and requires a fraction of the training set size to outperform standalone FEP calculations. The ML-derived correction terms are further shown to be transferable to a range of related FEP simulation protocols. The approach may be used to inexpensively improve the accuracy of FEP calculations and to flag molecules which will benefit the most from bespoke force field parametrization efforts.

Molecular dynamics simulation of polystyrene copolymer with octyl short-chain branches in toluene

In this study, dimensional, conformational and dynamic behaviors of a short-chain branched styrene/1-octene copolymer chain with different 1-octene percentages, i.e., 0, 2, 4 and 6%, in toluene are investigated at the temperature of 298.15 K via molecular dynamics simulation. The chain dimensions and flexibility in the solvent are evaluated by calculating the radius of gyration (R_g), end-to-end distance (<r₀>), surface area (A_ch), and volume (V_ch) of the copolymer chain. The mean square displacement (MSD) and diffusivity coefficient for each copolymer chain are measured to determine its dynamic behavior and mobility in aromatic media. To consider the effect of increasing the 1-octene co-monomer percentage on the copolymer chain affinity to the solvent molecules, the interaction energy (E_int) and Flory-Huggins (FH) interaction parameter are calculated for each equilibrated solution model. The simulation results indicate that the co-monomer level increment in the copolymer structure reduces the chain R_g amount and its interaction with the solvent. The <r₀> of the chain increases up to 4% co-monomer content, while further co-monomer content decreases the <r₀> value. Additionally, the viscosity of the equilibrated dilute solutions is calculated via non-equilibrium molecular dynamics simulation (NEMD). Moreover, the steric hindrance of the copolymers and the solvent molecules capturing in the dilute solution is determined via radial distribution function (RDF) analysis. Helmholtz free energy and the system entropy changes are calculated to evaluate the tendency of the copolymer to the solvent molecules and its dilute solution irregularity, respectively. Graphical abstract The figure shows the variations trend of the poly(styrene-co-1-octene) chain dimensions in toluene aromatic solvent by increasing the 1-octene content (x), after the equilibration state. Red and blue colors represent the carbon atoms of the copolymer chain backbone and 1-octene side chains, respectively. The styrene rings and the hydrogen atoms of the chains were removed for better view.

Molecular Dynamics of Water Embedded Carbon Nanocones: Surface Waves Observation

We employed molecular dynamics simulations on the water solvation of conically shaped carbon nanoparticles. We explored the hydrophobic behaviour of the nanoparticles and investigated microscopically the cavitation of water in a conical confinement with different angles. We performed additional molecular dynamics simulations in which the carbon structures do not interact with water as if they were in vacuum. We detected a waving on the surface of the cones that resembles the shape agitations of artificial water channels and biological porins. The surface waves were induced by the pentagonal carbon rings (in an otherwise hexagonal network of carbon rings) concentrated near the apex of the cones. The waves were affected by the curvature gradients on the surface. They were almost undetected for the case of an armchair nanotube. Understanding such nanoscale phenomena is the key to better designed molecular models for membrane systems and nanodevices for energy applications and separation.

Tackling Halogenated Species with PBSA: Effect of Emulating the σ-Hole

To model halogen-bond phenomena using classical force fields, an extra point (EP) of charge is frequently introduced at a given distance from the halogen (X) to emulate the σ-hole. The resulting molecular dynamics (MD) trajectories can be used in subsequent molecular mechanics (MM) combined with Poisson-Boltzmann and surface area calculations (PBSA) to estimate protein-ligand binding free energies (Δ G_bind). While EP addition improves the MM/MD description of halogen-containing systems, its effect on the calculation of solvation free energies (Δ G_solv) using the PBSA approach is yet to be assessed. As the PBSA calculations depend, among other parameters, on the empirical assignment of radii (PB radii), a problematic issue arises, since standard halogen radii are smaller than the typical X···EP distances, thus placing the EP within the solvent dielectric. Herein, we took a common literature EP parametrization scheme, which uses X···EP = R_min and RESP charges in the context of GAFF, and performed a comprehensive study on the performance of PBSA (using three different setups) in the calculation of Δ G_solv values for 142 halogenated compounds (bearing Cl, Br, or I) for which the experimental values are known. By conducting an optimization (minimizing the error against experimental values), we provide a new optimized set of halogen PB radii, for each PBSA setup, that should be used in the context of the aforementioned scenario. A simultaneous optimization of PB radii and X···EP distances shows that a wide range of distance/radius pairs can be used without significant loss of accuracy, therefore laying the basis for expanding this halogen radii optimization strategy to other force fields and EP implementations. As ligand Δ G_solv estimation is an important term in the determination of protein-ligand Δ G_bind, this work is particularly relevant in the framework of structure-based virtual screening and related computer-aided drug design routines.

DG-GL: Differential geometry-based geometric learning of molecular datasets

MOTIVATION

Despite its great success in various physical modeling, differential geometry (DG) has rarely been devised as a versatile tool for analyzing large, diverse, and complex molecular and biomolecular datasets because of the limited understanding of its potential power in dimensionality reduction and its ability to encode essential chemical and biological information in differentiable manifolds.

RESULTS

We put forward a differential geometry-based geometric learning (DG-GL) hypothesis that the intrinsic physics of three-dimensional (3D) molecular structures lies on a family of low-dimensional manifolds embedded in a high-dimensional data space. We encode crucial chemical, physical, and biological information into 2D element interactive manifolds, extracted from a high-dimensional structural data space via a multiscale discrete-to-continuum mapping using differentiable density estimators. Differential geometry apparatuses are utilized to construct element interactive curvatures in analytical forms for certain analytically differentiable density estimators. These low-dimensional differential geometry representations are paired with a robust machine learning algorithm to showcase their descriptive and predictive powers for large, diverse, and complex molecular and biomolecular datasets. Extensive numerical experiments are carried out to demonstrate that the proposed DG-GL strategy outperforms other advanced methods in the predictions of drug discovery-related protein-ligand binding affinity, drug toxicity, and molecular solvation free energy.

AVAILABILITY AND IMPLEMENTATION

http://weilab.math.msu.edu/DG-GL/ Contact: wei@math.msu.edu.

Solvation Free Energy Calculation Using a Fixed-Charge Model: Implicit and Explicit Treatments of the Polarization Effect

In this work, IPolQ-Mod charges and the reference potential scheme are used to calculate the solvation free energies of a set of organic molecules. Both methods could capture the phase transfer of a solute with accompanying polarization cost utilizing a fixed-charge model. The IPolQ-Mod charges, which are the average of two charge sets fitted in a vacuum state and a condensed phase, take account of the polarization effect implicitly. For the reference potential method, the quantum mechanics polarization corrections are calculated explicitly by thermodynamic perturbation. The polarization effect captured by the IPolQ-Mod charges is an approximation to that of the reference potential method theoretically. In the present study, the reference potential method shows a slight improvement over the classical restrained electrostatic potential (RESP) charges, which perform pretty well in predicting the solvation free energy. However, IPolQ-Mod(MP2) shows a poor agreement with the experimental data. Compared with IPolQ-Mod(MP2), IPolQ-Mod(M06-2X) or IPolQ-Mod(ωB97X) is found to give more appropriate prediction of the molecule's dipole and the solvation free energies calculated by IPolQ-Mod(M06-2X) or IPolQ-Mod(ωB97X) are more compatible with those of the RESP charges. If the other force field parameters remain unchanged, M06-2X or ωB97X is recommended to derive the IPolQ-Mod charges.

Estimations of the thermodynamic properties of halogenated benzenes as they relate to their environment mobility

In this work, several simple new equations for predicting important environmental mobility properties, at T = 298.15 K, were derived for halogenated benzenes: standard Gibbs energy of hydration, aqueous solubility, octanol-water partition coefficients, and Henry's law constants. A discussion on our previous estimates of other related properties (standard Gibbs energy and vapor pressure of sublimation and of vaporization) and their relation with entropy of fusion is also presented. As we aimed to estimate these properties for any of the ca. 1500 halogenated benzenes that may exist theoretically, an equation for estimating the temperature of fusion was also derived, since some of the proposed predictive equations (solubility of solids and Gibbs energy of sublimation) require its knowledge. For the other estimated properties just the number of each halogen that replaces hydrogen atoms in the halogenated benzene is needed. It was found that the coefficients that multiply the number of halogen atoms in the predictive equations vary linearly with the volume of the halogen atom.

Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field

The effects of electronic polarization in biomolecular interactions will differ depending on the local dielectric constant of the environment, such as in solvent, DNA, proteins, and membranes. Here the performance of the AMOEBA polarizable force field is evaluated under nonaqueous conditions by calculating the solvation free energies of small molecules in four common organic solvents. Results are compared with experimental data and equivalent simulations performed with the GAFF pairwise-additive force field. Although AMOEBA results give mean errors close to "chemical accuracy," GAFF performs surprisingly well, with statistically significantly more accurate results than AMOEBA in some solvents. However, for both models, free energies calculated in chloroform show worst agreement to experiment and individual solutes are consistently poor performers, suggesting non-potential-specific errors also contribute to inaccuracy. Scope for the improvement of both potentials remains limited by the lack of high quality experimental data across multiple solvents, particularly those of high dielectric constant. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Evaluating Parametrization Protocols for Hydration Free Energy Calculations with the AMOEBA Polarizable Force Field

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

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism

Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.

Impact of Quadrupolar Electrostatics on Atoms Adjacent to the Sigma-Hole in Condensed-Phase Simulations

Halogenation is one of the cases for which advanced molecular simulation methods are mandatory for quantitative and predictive studies. The present work provides a systematic investigation of the importance of higher-order multipoles on specific sites of halobenzenes, other than the halogen, for static and dynamic properties in condensed-phase simulations. For that purpose, solute-solvent interactions using point charge (PC), multipole (MTP), and hybrid point charge/multipole (HYB) electrostatic models are analyzed in regions of halogen bonding and extended to regions of π orbitals of phenyl carbons. Using molecular dynamics simulations and quantum chemical methods, it is found that the sigma-hole does not only affect the halogen and the carbon bound to it but its effect extends to the carbons adjacent to the CX bond. This effect increases with the magnitude of the positive potential of the sigma-hole. With the MTP and HYB3 models, all hydration free energies of the PhX compounds are reproduced within 0.1 kcal/mol. Analysis of pair distribution functions and hydration free energies of halogenated benzenes provides a microscopic explanation why "point charge"-based representations with off-site charges fail in reproducing thermodynamic properties of the sigma-hole. Application of the hybrid models to study protein-ligand binding demonstrates both their accuracy and computational efficiency.