General Amber Force Field Research Articles

A head-to-head comparison of MM/PBSA and MM/GBSA in predicting binding affinities for the CB1 cannabinoid ligands.

The substantial increase in the number of active and inactive-state CB1 receptor experimental structures has provided opportunities for CB1 drug discovery using various structure-based drug design methods, including the popular end-point methods for predicting binding free energies-Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA). In this study, we have therefore evaluated the performance of MM/PBSA and MM/GBSA in calculating binding free energies for CB1 receptor. Additionally, with both MM/PBSA and MM/GBSA being known for their highly individualized performance, we have evaluated the effects of various simulation parameters including the use of energy minimized structures, choice of solute dielectric constant, inclusion of entropy, and the effects of the five GB models. Generally, MM/GBSA provided higher correlations than MM/PBSA (rMM/GBSA = 0.433 - 0.652 vs. rMM/PBSA = 0.100 - 0.486) regardless of the simulation parameters, while also offering faster calculations. Improved correlations were observed with the use of molecular dynamics ensembles compared with energyminimized structures and larger solute dielectric constants. Incorporation of entropic terms led to unfavorable results for both MM/PBSA and MM/GBSA for a majority of the dataset, while the evaluation of the various GB models exerted a varying effect on both the datasets. The findings obtained in this study demonstrate the utility of MM/PBSA and MM/GBSA in predicting binding free energies for the CB1 receptor, hence providing a useful benchmark for their applicability in the endocannabinoid system as well as other G protein-coupled receptors. The study utilized the docked dataset (Induced Fit Docking with Glide XP scoring function) from Loo et al., consisting of 46 ligands-23 agonists and 23 antagonists. The equilibrated structures from Loo et al. were subjected to 30ns production simulations using GROMACS 2018 at 300K and 1atm with the velocity rescaling thermostat and the Parinello-Rahman barostat. AMBER ff99SB*-ILDN was used for the proteins, General Amber Force Field (GAFF) was used for the ligands, and Slipids parameters were used for lipids. MM/PBSA and MM/GBSA binding free energies were then calculated using gmx_MMPBSA. The solute dielectric constant was varied between 1, 2, and 4 to study the effect of different solute dielectric constants on the performance of MM/PB(GB)SA. The effect of entropy on MM/PB(GB)SA binding free energies was evaluated using the interaction entropy module implemented in gmx_MMPBSA. Five GB models, GBHCT, GBOBC1, GBOBC2, GBNeck, and GBNeck2, were evaluated to study the effect of the choice of GB models in the performance of MM/GBSA. Pearson correlation coefficients were used to measure the correlation between experimental and predicted binding free energies.

Structural and free energy landscape analysis for the discovery of antiviral compounds targeting the cap-binding domain of influenza polymerase PB2

Influenza poses a significant threat to global health, with the ability to cause severe epidemics and pandemics. The polymerase basic protein 2 (PB2) of the influenza virus plays a crucial role in the viral replication process, making the CAP-binding domain of PB2 an attractive target for antiviral drug development. This study aimed to identify and evaluate potential inhibitors of the influenza polymerase PB2 CAP-binding domain using computational drug discovery methods. We employed a comprehensive computational approach involving virtual screening, molecular docking, and 500 ns molecular dynamics (MD) simulations. Compounds were selected from the Diverse lib database and assessed for their binding affinity and stability in interaction with the PB2 CAP-binding domain. The study utilized the generalized amber force field (GAFF) for MD simulations to further evaluate the dynamic behaviour and stability of the interactions. Among the screened compounds, compounds 1, 3, and 4 showed promising binding affinities. Compound 4 demonstrated the highest binding stability and the most favourable free energy profile, indicating strong and consistent interaction with the target domain. Compound 3 displayed moderate stability with dynamic conformational changes, while Compound 1 maintained robust interactions throughout the simulations. Comparative analyses of these compounds against a control compound highlighted their potential efficacy. Compound 4 emerged as the most promising inhibitor, with substantial stability and strong binding affinity to the PB2 CAP-binding domain. These findings suggest that compound 4, along with compounds 1 and 3, holds the potential for further development into effective antiviral agents against influenza. Future studies should focus on experimental validation of these compounds and exploration of resistance mechanisms to enhance their therapeutic utility.

Force Fields, Quantum-Mechanical- and Molecular-Dynamics-Based Descriptors of Radiometal-Chelator Complexes.

Radiopharmaceuticals are currently a key tool in cancer diagnosis and therapy. Metal-based radiopharmaceuticals are characterized by a radiometal-chelator moiety linked to a bio-vector that binds the biological target (e.g., a protein overexpressed in a particular tumor). The right match between radiometal and chelator influences the stability of the complex and the drug's efficacy. Therefore, the coupling of the radioactive element to the correct chelator requires consideration of several features of the radiometal, such as its oxidation state, ionic radius, and coordination geometry. In this work, we systematically investigated about 120 radiometal-chelator complexes taken from the Cambridge Structural Database. We considered 25 radiometals and about 30 chelators, featuring both cyclic and acyclic geometries. We used quantum mechanics methods at the density functional theoretical level to generate the general AMBER force field parameters and to perform 1 µs-long all-atom molecular dynamics simulations in explicit water solution. From these calculations, we extracted several key molecular descriptors accounting for both electronic- and dynamical-based properties. The whole workflow was carefully validated, and selected test-cases were investigated in detail. Molecular descriptors and force field parameters for the complexes considered in this study are made freely available, thus enabling their use in predictive models, molecular modelling, and molecular dynamics investigations of the interaction of compounds with macromolecular targets. Our work provides new insights in understanding the properties of radiometal-chelator complexes, with a direct impact for rational drug design of this important class of drugs.

An improved dataset of force fields, electronic and physicochemical descriptors of metabolic substrates

In silico prediction of xenobiotic metabolism is an important strategy to accelerate the drug discovery process, as candidate compounds often fail in clinical phases due to their poor pharmacokinetic profiles. Here we present MetaQM, a dataset of quantum-mechanical (QM) optimized metabolic substrates, including force field parameters, electronic and physicochemical properties. MetaQM comprises 2054 metabolic substrates extracted from the MetaQSAR database. We provide QM-optimized geometries, General Amber Force Field (FF) parameters for all studied molecules, and an extended set of structural and physicochemical descriptors as calculated by DFT and PM7 methods. The generated data can be used in different types of analysis. FF parameters can be applied to perform classical molecular mechanics calculations as exemplified by the validating molecular dynamics simulations reported here. The calculated descriptors can represent input features for developing improved predictive models for metabolism and drug design, as exemplified in this work. Finally, the QM-optimized molecular structures are valuable starting points for both ligand- and structure-based analyses such as pharmacophore mapping and docking simulations.

In silico-guided discovery and in vitro validation of novel sugar-tethered lysinated carbon nanotubes for targeted drug delivery of doxorubicin

ContextMultiwalled carbon nanotubes (MWCNTs) functionalized with lysine via 1,3-dipolar cycloaddition and conjugated to galactose or mannose are potential nanocarriers that can effectively bind to the lectin receptor in MDA-MB-231 or MCF-7 breast cancer cells. In this work, a method based on molecular dynamics (MD) simulation was used to predict the interaction of these functionalized MWCNTs with doxorubicin and obtain structural evidence that allows a better understanding of the drug loading and release process. The MD simulations showed that while doxorubicin only interacted with pristine MWCNTs through π-π stacking interactions, functionalized MWCNTs were also able to establish hydrogen bonds, suggesting that the functionalized groups improve doxorubicin loading. Moreover, the elevated adsorption levels observed for functionalized nanotubes further support this enhancement in loading efficiency. MD simulations also shed light on the intratumoral pH-specific release of doxorubicin from functionalized MWCNTs, which is induced by protonation of the daunosamine moiety. The simulations show that this change in protonation leads to a lower absorption of doxorubicin to the MWCNTs. The MD studies were then experimentally validated, where functionalized MWCNTs showed improved dispersion in aqueous medium compared to pristine MWCNTs and, in agreement with the computational predictions, increased drug loading capacity. Doxorubicin-loaded functionalized MWCNTs demonstrated specific release of doxorubicin in tumor microenvironment (pH = 5.0) with negligible release in the physiological pH (pH = 7.4). Furthermore, doxorubicin-free MWNCT nanoformulations exhibited insignificant cytotoxicity. The experimental studies yielded nearly identical results to the MD studies, underlining the usefulness of the method. Our functionalized MWCNTs represent promising non-toxic nanoplatforms with enhanced aqueous dispersibility and the potential for conjugation with ligands for targeted delivery of anti-cancer drugs to breast cancer cells.MethodsThe computational model of a pristine carbon nanotube was created with the buildCstruct 1.2 Python script. The lysinated functionalized groups were added with PyMOL and VMD. The carbon nanotubes and doxorubicin molecules were parameterized using the general AMBER force field, and RESP charges were determined using Gaussian 09. Molecular dynamics simulations were carried out with the AMBER 20 software package. Adsorption levels were calculated using the water-shell function of cpptraj. Cytotoxicity was evaluated via a MTT assay using MDA-MB-231 and MCF-7 breast cancer cells. Drug uptake of doxorubicin and doxorubicin-loaded MWCNTs was measured by fluorescence microscopy.

Advancing Force Fields Parameterization: A Directed Graph Attention Networks Approach.

Force fields (FFs) are an established tool for simulating large and complex molecular systems. However, parametrizing FFs is a challenging and time-consuming task that relies on empirical heuristics, experimental data, and computational data. Recent efforts aim to automate the assignment of FF parameters using pre-existing databases and on-the-fly ab initio data. In this study, we propose a graph-based force field (GB-FFs) model to directly derive parameters for the Generalized Amber Force Field (GAFF) from chemical environments and research into the influence of functional forms. Our end-to-end parametrization approach predicts parameters by aggregating the basic information in directed molecular graphs, eliminating the need for expert-defined procedures and enhances the accuracy and transferability of GAFF across a broader range of molecular complexes. Simulation results are compared to the original GAFF parametrization. In practice, our results demonstrate an improved transferability of the model, showcasing its improved accuracy in modeling intermolecular and torsional interactions, as well as improved solvation free energies. The optimization approach developed in this work is fully applicable to other nonpolarizable FFs as well as to polarizable ones.

Exploring the Limits of the Generalized CHARMM and AMBER Force Fields through Predictions of Hydration Free Energy of Small Molecules.

Accurate force field parameters, potential energy functions, and receptor-ligand models are essential for modeling the solvation and binding of drug-like molecules to a receptor. A large and ever-growing chemical space of medicinally relevant scaffolds has also required these factors, especially force field parameters, to be highly transferable. Generalized force fields such as the CHARMM General Force Field (CGenFF) and the generalized AMBER force field (GAFF) have accomplished this feat along with other contemporaneous ones like OPLS. Here, we analyze the limits in the parametrization of drug-like small molecules by CGenFF and GAFF in terms of the various functional groups represented within them. Specifically, we link the presence of specific functional groups to the error in the absolute hydration free energy of over 600 small molecules, predicted by alchemical free energy methods implemented in the CHARMM program. Our investigation reveals that molecules with (i) a nitro group in CGenFF and GAFF are, respectively, over- or undersolubilized in aqueous medium, (ii) amine groups are undersolubilized more so in CGenFF than in GAFF, and (iii) carboxyl groups are more oversolubilized in GAFF than in CGenFF. We present our analyses of the potential factors underlying these trends. We also showcase the use of a machine-learning-based approach combined with the SHapley Additive exPlanations framework to attribute these trends to specific functional groups, which can be easily adopted to explore the limits of other general force fields.

Molecular dynamics simulation of methane hydrates: Prediction of the phase equilibria using extracted microscopic parameters from SAFT-VR Mie EOS

This paper explores the three-phase equilibrium of structure-I methane hydrate by utilizing the extracted Mie potential parameters for non-bonded molecular interactions in the LAMMPS molecular dynamics simulator. An in-house code was developed to numerically solve the SAFT-VR Mie equation of state (EOS). To reduce numerical errors, unlike common practices, we incorporated all partial derivatives analytically in the code. The microscopic simulation parameters for methane and water molecules were selected so that saturation pressure and liquid density data from EOS were fitted on experimental data. Using this approach, the three-phase (V−Lw−H) equilibrium temperatures for pressure values of 30, 70, 100, and 300 bar were calculated. The solubility of methane in the aqueous phase at three-phase equilibrium was also determined. The results were validated against available laboratory data. To evaluate the performance of Mie potential parameters, the MD simulations were repeated using three well-known force fields for methane molecules, namely, Optimized Potentials for Liquid Simulations-United Atom (OPLS-UA), Universal force field (UFF), and General Amber force field (GAFF). The TIP4P/Ice force field was used for water molecules in all cases. The comparison with experimental data reveals that while the accuracy of the UFF force field is the highest, our simulation approach outperforms OPLS-UA and GAFF force fields in predicting methane solubility in the aqueous phase. Besides molecular dynamics simulation, the van der Waals-Platteeuw model was combined with the SAFT VR Mie EOS to account for the hydrate phase. In our case study, the SAFT-VR Mie + vdW-P model proved to be more accurate in predicting the three-phase equilibrium data compared to the MD approach.

Predicting Solvation Free Energies from the Minnesota Solvation Database Using Classical Density Functional Theory Based on the PC-SAFT Equation of State.

We critically assess the capabilities of classical density functional theory (DFT) based on the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state to predict the solvation free energies of small molecules in various hydrocarbon solvents. We compare DFT results with experimental data from the Minnesota solvation database and utilize statistical methods to analyze the accuracy of our approach, as well as its weaknesses. The mean absolute error of the solvation free energies is 3.7 kJ mol-1 for n-alkane solvents, ranging from pentane to hexadecane, with 473 solute-solvent systems. For solvents consisting of cyclic hydrocarbons (cyclohexane, benzene, toluene, and ethylbenzene) with 245 solute-solvent systems, we report a slightly larger mean absolute error of 4.2 kJ mol-1. We identify three possible sources of errors: (i) the neglect of solute-solvent and solvent-solvent Coulomb interactions, which limits the applicability of PC-SAFT DFT to nonpolar and weakly polar molecules; (ii) the solute's Lennard-Jones parameters supplied by the general AMBER force field, which are not parametrized toward solvation free energies; and (iii) the application of the Lorentz-Berthelot combining rules to the dispersive interactions between a segment of the PC-SAFT solvent and a Lennard-Jones interaction site of the solute. The approach is more accurate than standard implementations of phenomenological models in common chemistry software packages, which exhibit mean absolute errors larger than 9.12 kJ mol-1, even though newer phenomenological models achieve a mean absolute error of about 2 kJ mol-1. PC-SAFT DFT is more computationally efficient than state of the art explicit molecular simulations in combination with free energy perturbation methods. It is predictive with respect to solvation free energies, i.e., the input for the model is the (element-specific) molecular force field, the solute configuration from molecular dynamics simulations, and the (substance-specific) PC-SAFT parameters. The PC-SAFT parametrization uses pure-component data and does not require experimental solvation free energies. The PC-SAFT equation of state, without applying a DFT formalism, can also be used to calculate solvation free energies, provided that the PC-SAFT parameters for the solute are available. A large number of substances was recently parametrized by members of our group (Esper, T.; Bauer, G.; Rehner, P.; Gross, J. Ind. Eng. Chem. Res. 2023, 62), which enables a comparison to the DFT approach for 103 substances. Accurate results are obtained from the PC-SAFT equation of state with an MAE below 2.51 kJ mol-1. The DFT approach does not require PC-SAFT parameters for the solute and can be applied to all solutes that can be represented by the molecular force field.

Multitype Electronic Interactions in Precursor Solutions of Molecular Doped P3HT Polymer.

Spin-casting of molecularly doped polymer solution mixtures is one of the commonly used methods to obtain conductive organic semiconductor films. In spin-casted films, electronic interaction between the dopant and polymer is one of the crucial factors that dictates the doping efficiency. Here, we investigate excitonic couplings using ultrafast two-dimensional electronic spectroscopy to examine the different types of electronic interactions in ion pairs of the prototype F4TCNQ-doped P3HT polymer system in a precursor solution mixture for spin-casting. Off-diagonal peaks in the 2D spectra clearly establish the excitonic coupling between P3HT+ and F4TCNQ- ions in solution. The observed excitonic coupling is the direct manifestation of a Coulombic interaction between the ion pair. The excited-state lifetime of F4TCNQ- in ion pairs shows biexponential decay at 30 and 200 fs, which hints toward the presence of a heterogeneous population with different interaction strengths. To examine the nature of these different types of interactions in solution mixtures, we study the system using molecular dynamics simulations on a fully solvated model employing the generalized Amber force field. We retrieve three dominant interaction modes of F4TCNQ anions with P3HT: side chain, π-stack, and slipped stack. To quantify these interactions, we complement our studies with electronic structure calculations, which reveal the excitonic coupling strengths of ∼ 75 cm-1 for side chain, ∼ 150 cm-1 for π-π-stack, and ∼69 cm-1 for slipped stack. These various interaction modes provide information about the key geometries of the seed structures in precursor solution mixtures, which may determine the final structures in spin-casted films. The insights gained from our study may guide new strategies to control and ultimately tune Coulomb interactions in polymer-dopant solutions.

Molecular Geometry Impact on Deep Learning Predictions of Inverted Singlet-Triplet Gaps.

We present a deep learning model able to predict excited singlet-triplet gaps with a mean absolute error (MAE) of ≈20 meV to obtain potential inverted singlet-triplet (IST) candidates. We exploit cutting-edge spherical message passing graph neural networks designed specifically for generating 3D graph representations in molecular learning. In a nutshell, the model takes as input a list of unsaturated heavy atom Cartesian coordinates and atomic numbers, producing singlet-triplet gaps as output. We exploited available large data collections to train the model on ≈40,000 heterogeneous density functional theory (DFT) geometries with available ADC(2)/cc-pVDZ singlet-triplet gaps. We ascertain the predictive power of the model from a quantitative perspective obtaining predictions on a test set of ≈14,000 molecules, whose geometries have been generated at DFT level (the same employed for the geometries in the training set), at GFN2-xTB level, and through Molecular Mechanics. We notice performance degradation upon switching to lower-quality geometries, with GFN2-xTB ones maintaining satisfactory results (MAE ≈ 50 meV on GFN2-xTB geometries, MAE ≈ 180 meV on generalized AMBER force field geometries), hinting at caution when dealing with specific chemical classes. Finally, we verify the performance of the model from the qualitative point of view, obtaining predictions on a different data set of ≈15,000 molecules already used to identify new IST molecules. We obtained predictions using both DFT and experimental X-ray geometries, with results on IST candidates similar to those provided by quantum chemical methods, with clear hints for the path toward improved performance.

Correlation between lithium-ion diffusion and coordination environment in solid polymer electrolytes: a molecular dynamics study

Lithium-ion diffusion in solid polymer electrolytes (SPEs) is a pivotal characteristic that significantly influences overall lithium-ion battery performance. This characteristic can be affected by the coordination environment of lithium ions within the polymer matrix. However, the correlation between lithium-ion diffusion and its coordination environment in biopolymer-based SPEs such as carboxymethyl chitosan (CMCS) remains understudied. In this study, we used molecular dynamics (MD) simulations to investigate this correlation. Lithium bis(trifluoromethylsulfonyl)imide (LiTFSI) was used as the lithium salt in the simulated systems. All MD simulations were conducted using the GROMACS package with the general AMBER force field (GAFF). The coordination structures around Li+ were successfully estimated using the radial distribution function obtained from the MD simulations. These results indicate a preference for Li+ coordination with oxygen atoms, both from the CMCS polymer chains (OCMCS) and TFSI− ions (OTFSI-). The coordination number between Li+ and OCMCS decreases as the concentration of LiTFSI increases. The diffusion coefficients of Li+ varied depending on the concentration of LiTFSI and demonstrated a sensitivity to the coordination structure of Li+. A high diffusion coefficient of Li+ ions was observed at low LiTFSI concentrations, where Li+ was primarily coordinated with oxygen atoms from the CMCS polymer chains.

Thermophysical properties of polyethylene glycol oligomers via molecular dynamics simulations.

Polyethylene glycol (PEG) is a versatile chemical with numerous applications in various fields, including biomedical research, pharmaceutical development, and industrial manufacturing. Molecular dynamics (MD) is a powerful tool for investigating the thermophysical properties of PEG molecules. In this study, we employ the General AMBER force field (GAFF) to perform MD simulations on various PEG oligomers, focusing on the calculation of density, self-diffusion coefficients, shear viscosity, and thermal conductivity. The results demonstrate excellent agreement with experimental data, where GAFF outperforms other force fields in reproducing thermophysical properties. For a PEG tetramer, the GAFF force field reproduces experimental data within 5% for the density, 5% for the diffusion coefficient, and 10% for the viscosity. In comparison, the OPLS force field displays significant deviations exceeding 80% for the diffusion coefficient and 400% for the viscosity. A detailed analysis of partial charge distributions and dihedral angles reveals that they significantly impact the structural behavior of PEG oligomers. The findings highlight the GAFF force field as one of the most accurate and reliable options for simulating systems with PEGs.

Computational predictions of interfacial tension, surface tension, and surfactant adsorption isotherms.

All-atom (AA) molecular dynamics (MD) simulations are employed to predict interfacial tensions (IFT) and surface tensions (ST) of both ionic and non-ionic surfactants. The general AMBER force field (GAFF) and variants are examined in terms of their performance in predicting accurate IFT/ST, γ, values for chosen water models, together with the hydration free energy, ΔGhyd, and density, ρ, predictions for organic bulk phases. A strong correlation is observed between the quality of ρ and γ predictions. Based on the results, the GAFF-LIPID force field, which provides improved ρ predictions is selected for simulating surfactant tail groups. Good γ predictions are obtained with GAFF/GAFF-LIPID parameters and the TIP3P water model for IFT simulations at a water-triolein interface, and for GAFF/GAFF-LIPID parameters together with the OPC4 water model for ST simulations at a water-vacuum interface. Using a combined molecular dynamics-molecular thermodynamics theory (MD-MTT) framework, a mole fraction of C12E6 molecule of 1.477 × 10-6 (from the experimental critical micelle concentration, CMC) gives a simulated surface excess concentration, ΓMAX, of 76 C12E6 molecules at a 36 nm2 water-vacuum surface (3.5 × 10-10 mol cm-2), which corresponds to a simulated ST of 35 mN m-1. The results compare favourably with an experimental ΓMAX of C12E6 of 3.7 × 10-10 mol cm-2 (80 surfactants for a 36 nm2 surface) and experimental ST of C12E6 of 32 mN m-1 at the CMC.

Development and test of highly accurate endpoint free energy methods. 3: partition coefficient prediction using a Poisson-Boltzmann method combined with a solvent accessible surface area model for SAMPL challenges.

Accurately predicting solvation free energy is the key to predict protein-ligand binding free energy. In addition, the partition coefficient (log P), which is an important physicochemical property that determines the distribution of a drug in vivo, can be derived directly from transfer free energies, i.e., the difference between solvation free energies (SFEs) in different solvents. Within the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) 9 challenge, we applied the Poisson-Boltzmann (PB) surface area (SA) approach to predict the toluene/water transfer free energy and partition coefficient (log Ptoluene/water) from SFEs. For each solute, only a single conformation automatically generated by the free software Open Babel was used. The PB calculation directly adopts our previously optimized boundary definition - a set of general AMBER force field 2 (GAFF2) atom-type based sphere radii for solute atoms. For the non-polar SA model, we newly developed the solvent-related molecular surface tension parameters γ and offset b for toluene and cyclohexane targeting experimental SFEs. This approach yielded the highest predictive accuracy in terms of root mean square error (RMSE) of 1.52 kcal mol-1 in transfer free energy for 16 small drug molecules among all 18 submissions in the SAMPL9 blind prediction challenge. The re-evaluation of the challenge set using multi-conformation strategies based on molecular dynamics (MD) simulations further reduces the prediction RMSE to 1.33 kcal mol-1. At the same time, an additional evaluation of our PBSA method on the SAMPL5 cyclohexane/water distribution coefficient (log Dcyclohexane/water) prediction revealed that our model outperformed COSMO-RS, the best submission model with RMSEPBSA = 1.88 versus RMSECOSMO-RS = 2.11 log units. Two external log Ptoluene/water and log Pcyclohexane/water datasets that contain 110 and 87 data points, respectively, are collected for extra validation and provide an in-depth insight into the error source of the PBSA method.

Upgrading of the general AMBER force field 2 for fluorinated alcohol biosolvents: A validation for water solutions and melittin solvation.

The standard GAFF2 force field parameterization has been refined for the fluorinated alcohols 2,2,2-trifluoroethanol (TFE), 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP), and 1,1,1,3,3,3-hexafluoropropan-2-one (HFA), which are commonly used to study proteins and peptides in biomimetic media. The structural and dynamic properties of both proteins and peptides are significantly influenced by the biomimetic environment created by the presence of these cosolvents in aqueous solutions. Quantum mechanical calculations on stable conformers were used to parameterize the atomic charges. Different systems, such as pure liquids, aqueous solutions, and systems formed by melittin protein and cosolvent/water solutions, have been used to validate the new models. The calculated macroscopic and structural properties are in agreement with experimental findings, supporting the validity of the newly proposed models.

Exploring computational tools for improved structural design and stability of helical AApeptides

Peptidomimetics have garnered increased attention due to their unique structural properties and self-assembly, as well as their promising applications in material sciences and peptidomimetic drug design. N-acetylated-N-aminoethyl amino acid oligomers (AApeptides), a novel class of helical foldamer, have been effectively utilized to imitate protein helices and regulate protein-protein interactions. While the structures of a series of AApeptides have been determined experimentally, the chemical diversity of AApeptides is impeding the advancement of peptide inhibitor development and high-level architecture design through purely experimental methods. Consequently, there's an urgent need for effective computational tools to facilitate the structural design of more complex systems using AApeptides. While a general AMBER force field (GAFF) can be applied to simulate AApeptides, it is crucial to evaluate the accuracy of such a force field and consider alternatives to enhance accuracy. In this study, we employed molecular dynamics simulations (MD) to assess the stability of a helical AApeptide. Our findings indicate that GAFF alone is insufficient to stabilize the helical structure of our AApeptide. We suggest the use of restraints derived from experimentally determined structures to guide the simulation and maintain this helical structure. Although any set of restraint definitions used in this study can simulate helical packing, a minimum of three points per AApeptide building block restraint is necessary to accurately reproduce the hydrogen bonding pattern.

Computational-Simulation-Based Behavioral Analysis of Chemical Compounds

This research focuses on obtaining the behavior of chemical compounds with respect to their molecular weight and optimization energy based on the variation in properties in organic carbon links. Here, behavioral analysis of compounds is used in the application of a metal organic framework to denote the high-grade compounds. The grade was selected based on the essential measure of optimization energy and molecular weight, and in turn, depicts the stability of material. Computation of the optimization energy and molecular weight of chemical compounds was performed with Avogadro software. Several force fields can be considered to compute optimized energy. Exclusively, three force fields, namely, the Universal Force Field (UFF), the General Amber Force Field (GAFF), and the Ghemical force field (Ghemical) were selected from Avogadro as these were more relevant to compounds considered in this research. The various chemical compounds examined in this work are Aluminum (Al), Boron (Br), Calcium (Ca), Chlorine (Cl), Indium (In), Potassium (K), Scandium (Sc), Silicon (Si), and Tungsten (W). Hence, molecular modeling of different compounds incorporated with three different force fields was evaluated in this work. In this study, we found that the In structure has more energy reduction, of 22.673 kJ mol−1 in UFF, when compared with the other two force fields. Thus, In has higher potential with more stability.

Insight into the structural and transport properties of methyl and benzyl triphenyl phosphonium based deep eutectic solvents using molecular dynamics simulations

Molecular dynamics simulation of twelve phosphonium based DESs based on methyl triphenyl phosphonium bromide and benzyl triphenyl phosphonium chloride as hydrogen bond acceptors (HBA) with glycerol, ethylene glycol, triethylene glycol and trifluoroacetamide as hydrogen bond donors (HBDs) are carried out with the Generalized Amber Force Field between 25 °C and 95 °C at 1 atm pressure. Isobaric-isothermal ensemble is used for predicting the structural properties, namely, radial distribution function and coordination number. Further, time resolved trajectory data is correlated to predict the transport properties such as, self-diffusivity, binary-diffusivity, ionic conductivity and viscosity. The temperature dependence of predicted density is observed with 4.96 × 10−2 absolute average deviations from experimental dataset. Further, the self-diffusivities are predicted using Einstein's approach and ionic conductivity is calculated from Nernst-Einstein equation as well as Green-Kubo formulation. Further, the viscosity is also predicted using the Green-Kubo formulation. The predicted ionic conductivities and viscosities are fitted in the Vogel-Fulcher-Tammann (VFT) equation to calculate the VFT parameters for the respective DESs.

Morphology prediction for organic molecular crystals using various force fields in ADDICT

AbstractAll‐atom force fields are important for calculating interaction energies between growth units to predict crystal morphology. Our group at UCSB developed the ADDICT morphology design software to predict crystal morphology using mechanistic growth models driven by the generalized Amber force field (GAFF). In this study, the universal force field (UFF), the consistent force field (CFF), and the Coulomb‐London‐Pauli (CLP) force field are incorporated into ADDICT. We systematically test the lattice energies and crystal morphologies of olanzapine, adipic acid, rubrene, biphenyl, and naphthalene using all these force fields. The unit cells for these crystals are represented by the original CSD experimental structure, as well as optimized structures. Considering the high accuracy of CLP, its wide range of applications, and the fact that ADDICT can automatically specify the atomic charge and type required for this force field, we find that CLP is the best all‐atom model to drive morphology calculations.