Binding of Phosphate Species to Ca2+ and Mg2+ in Aqueous Solution.

Phosphate derivatives and their interaction with metal cations are involved in many important biological phenomena, so an accurate characterization of the phosphate-metal interaction is necessary to properly understand the role of phosphate-metal contacts in mediating biological function. Herein, we improved the standard 12-6 Lennard-Jones (LJ) potential via the usage of the 12-6-4 LJ model, which incorporates ion-induced dipole interactions. Via parameter scanning, we fine-tuned the 12-6-4 LJ polarizability values to obtain accurate absolute binding free energies for the phosphate anions H2PO4-, HPO42-, PO43- coordinating with Ca2+ and Mg2+. First, we modified the phosphate 12-6-4 LJ parameters to reproduce the solvation free energies of the series of phosphate anions using the thermodynamic integration (TI) method. Then, using the potential mean force (PMF) method, the polarizability of the metal-phosphate interaction was obtained. We show that the free energy profiles of phosphate ions coordinated to Ca2+ and Mg2+ generally show similar trends at longer metal-phosphate distances, while the absolute binding energy values increased with deprotonation. The resulting parameters demonstrate the flexibility of the 12-6-4 LJ-type nonbonded model and its usefulness in accurately describing cation-anion interactions.

Thermodynamics of Metal-Acetate Interactions.

Metal ions play crucial roles in protein- and ligand-mediated interactions. They not only act as catalysts to facilitate biological processes but are also important as protein structural elements. Accurately predicting metal ion interactions in computational studies has always been a challenge, and various methods have been suggested to improve these interactions. One such method is the 12-6-4 Lennard-Jones (LJ)-type nonbonded model. Using this model, it has been possible to successfully reproduce the experimental properties of metal ions in aqueous solution. The model includes induced dipole interactions typically ignored in the standard 12-6 LJ nonbonded model. In this we expand the applicability of this model to metal ion-carboxylate interactions. Using 12-6-4 parameters that reproduce the solvation free energies of the metal ions leads to an overestimation of metal ion-acetate interactions, thus, prompting us to fine-tune the model to specifically handle the latter. We also show that the standard 12-6 LJ model significantly falls short in reproducing the experimental binding free energy between acetate and 11 metal ions (Ni(II), Mg(II), Cu(II), Zn(II), Co(II), Cu(I), Fe(II), Mn(II), Cd(II), Ca(II), and Ag(I)). In this study, we describe optimized C4 parameters for the 12-6-4 LJ nonbonded model to be used with three widely employed water models (Transferable Intermolecular Potential with 3 Points (TIP3P), Simple Point Charge Extended (SPC/E), and Optimal Point Charge (OPC) water models). These parameters can accurately match the experimental binding free energy between 11 metal ions and acetate. These parameters can be applied to the study of metalloproteins and transition metal ion channels and transporters, as acetate serves as a representative of the negatively charged amino acid side chains from aspartate and glutamate.

Accurate Metal-Imidazole Interactions.

Modeling the interaction between a metal ion and small molecules can provide pivotal information to bridge and close the gap between two types of simulations: metal ions in water and metal ions in metalloproteins. As previously established, the 12-6-4 Lennard-Jones (LJ)-type nonbonded model, because of its ability to account for the induced dipole effect, has been highly successful in simulating metal ion systems. Using the potential of mean force (PMF) method, the polarizability of the metal-chelating nitrogen from two types of imidazole molecules, delta nitrogen protonated (HID) and epsilon nitrogen protonated (HIE), has been parametrized against experiment for 11 metals (Ag(I), Ca(II), Cd(II), Co(II), Cu(I), Cu(II), Fe(II), Mg(II), Mn(II), Ni(II), and Zn(II)) in conjunction with three commonly used water models (TIP3P, SPC/E, and OPC). We show that the standard 12-6 and unmodified 12-6-4 models are not able to accurately model these interactions and, indeed, predict that the complex should be unstable. The resultant parameters further establish the flexibility and the reliability of the 12-6-4 LJ-type nonbonded model, which can correctly describe three-component interactions between a metal, ligand, and solvent by simply tuning the polarizability of the chelating atom. Also, the transferability of this model was tested, showing the capability of describing metal-ligand interactions in various environments.

Effect of an Inhibitor on the ACE2-Receptor-Binding Domain of SARS-CoV-2.

The recent outbreak of COVID-19 infection started in Wuhan, China, and spread across China and beyond. Since the WHO declared COVID-19 a pandemic (March 11, 2020), three vaccines and only one antiviral drug (remdesivir) have been approved (Oct 22, 2020) by the FDA. The coronavirus enters human epithelial cells by the binding of the densely glycosylated fusion spike protein (S protein) to a receptor (angiotensin-converting enzyme 2, ACE2) on the host cell surface. Therefore, inhibiting the viral entry is a promising treatment pathway for preventing or ameliorating the effects of COVID-19 infection. In the current work, we have used all-atom molecular dynamics (MD) simulations to investigate the influence of the MLN-4760 inhibitor on the conformational properties of ACE2 and its interaction with the receptor-binding domain (RBD) of SARS-CoV-2. We have found that the presence of an inhibitor tends to completely/partially open the ACE2 receptor where the two subdomains (I and II) move away from each other, while the absence results in partial or complete closure. The current study increases our understanding of ACE inhibition by MLN-4760 and how it modulates the conformational properties of ACE2.

FFENCODER-PL: Pair Wise Energy Descriptors for Protein-Ligand Pose Selection.

Scoring functions are the essential component in molecular docking methods. An accurate scoring function is expected to distinguish the native ligand pose from decoy poses. Our previous experience (Pei et al. J. Chem. Inf. Model. 2019, 59 (7), 3305-3315) proved that combining the random forest (RF) algorithm with knowledge-based potential functions can emphasize germane pair wise interactions and improve the performance of original knowledge-based potential functions on protein-ligand decoy detection. One of the most important potential function classes is the force field (FF) potential with one example being the Amber collection of FFs, which are widely available in the AMBER suite of simulation programs. However, for use in RF modeling studies, one needs pair wise energies that are hard to directly extract from Amber. To address this issue, FFENCODER-PL was constructed to calculate the pair wise energies based on the FF14SB and GAFF2 FFs in Amber. FFENCODER-PL was validated using 275 ligand and 21 protein-ligand structures. RF models were built by combining an RF classification algorithm with the pair wise energies calculated from FFENCODER-PL. CASF-2016 (Su et al. J. Chem. Inf. Model. 2019, 59, 895-913) was employed to test the performance of the resultant RF models, which outperformed 33 scoring functions on accuracy and native ranking tests. For the best decoy RMSD test, RF models give a best decoy with an RMSD of around 2 Å from the native pose after including the best decoy-decoy comparisons in the RF model. The relative importance of the RF algorithm and force field potentials was also tested with the results suggesting that both the RF algorithm and force field potentials are important and combining them is the only way to achieve high accuracy. Finally, FFENCODER-PL makes force field-based pair wise energies available for further development of machine learning-based scoring functions. The codes and data used in this paper can be found at https://github.com/JunPei000/Amber_protein_ligand_encoding.

Parametrization of Trivalent and Tetravalent Metal Ions for the OPC3, OPC, TIP3P-FB, and TIP4P-FB Water Models.

Commonly seen in rare-earth chemistry and materials science, highly charged metal ions play key roles in many chemical processes. Computer simulations have become an important tool for scientific research nowadays. Meaningful simulations require reliable parameters. In the present work, we parametrized 18 M(III) and 6 M(IV) metal ions for four new water models (OPC3, OPC, TIP3P-FB, TIP4P-FB) in conjunction with each of the 12-6 and 12-6-4 nonbonded models. Similar to what was observed previously, issues with the 12-6 model can be fixed by using the 12-6-4 model. Moreover, the four new water models showed comparable performance or considerable improvement over the previous water models (TIP3P, SPC/E, and TIP4PEW) in the same category (3-point or 4-point water models, respectively). Finally, we reported a study of a metalloprotein system demonstrating the capability of the 12-6-4 model to model metalloproteins. The reported parameters will facilitate accurate simulations of highly charged metal ions in aqueous solution.

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.

Evolution of Alchemical Free Energy Methods in Drug Discovery.

The goal of the present manuscript is to succinctly trace the key technological steps in the evolution of alchemical free energy methods (AFEMs) from a purely theoretical construct to a method that is now widely used in the biotechnological and pharmaceutical industries. More specifically, we focus on relative binding free energy (RBFE) computations which are more routinely applied in computer aided drug design (CADD) campaigns rather than the more computationally intensive absolute binding free energy (ABFE) computations. We have not been exhaustive in the development of our timeline but rather try to weave a story about how theoretical ideas were ultimately converted into contemporary free energy capabilities. Necessarily this story-telling approach limits us from citing all work on AFEMs, and we apologize for this shortcoming. However, for those interested in a broad delineation of all the work done in this area they are directed to the many excellent reviews that are extant.

Systematic Parametrization of Divalent Metal Ions for the OPC3, OPC, TIP3P-FB, and TIP4P-FB Water Models.

Divalent metal ions play important roles in biological and materials systems. Molecular dynamics simulation is an efficient tool to investigate these systems at the microscopic level. Recently, four new water models (OPC3, OPC, TIP3P-FB, and TIP4P-FB) have been developed and better represent the physical properties of water than previous models. Metal ion parameters are dependent on the water model employed, making it necessary to develop metal ion parameters for select new water models. In the present work, we performed parameter scanning for the 12-6 Lennard-Jones nonbonded model of divalent metal ions in conjunction with the four new water models as well as four previous water models (TIP3P, SPC/E, TIP4P, and TIP4P-Ew). We found that these new three-point and four-point water models provide comparable or significantly improved performance for the simulation of divalent metal ions when compared to previous water models in the same category. Among all eight water models, the OPC3 water model yields the best performance for the simulation of divalent metal ions in the aqueous phase when using the 12-6 model. On the basis of the scanning results, we independently parametrized the 12-6 model for 24 divalent metal ions with each of the four new water models. As noted previously, the 12-6 model still fails to simultaneously reproduce the experimental hydration free energy (HFE) and ion-oxygen distance (IOD) values even with these new water models. To solve this problem, we parametrized the 12-6-4 model for the 16 divalent metal ions for which we have both experimental HFE and IOD values for each of the four new water models. The final parameters are able to reproduce both the experimental HFE and IOD values accurately. To validate the transferability of our parameters, we carried out benchmark calculations to predict the energies and geometries of ion-water clusters as well as the ion diffusivity coefficient of Mg2+. By comparison to quantum chemical calculations and experimental data, these results show that our parameters are well designed and have excellent transferability. The metal ion parameters for the 12-6 and 12-6-4 models reported herein can be employed in simulations of various biological and materials systems when using the OPC3, OPC, TIP3P-FB, or TIP4P-FB water model.

Pair Potentials as Machine Learning Features.

Atom pairwise potential functions make up an essential part of many scoring functions for protein decoy detection. With the development of machine learning (ML) tools, there are multiple ways to combine potential functions to create novel ML models and methods. Potential function parameters can be easily extracted; however, it is usually hard to directly obtain the calculated atom pairwise energies from scoring functions. Amber, as one of the most popular suites of modeling programs, has an extensive history and library of force field potential functions. In this work, we directly used the force field parameters in ff94 and ff14SB from Amber and encoded them to calculate atom pairwise energies for different interactions. Two sets of structures (single amino acid set and a dipeptide set) were used to evaluate the performance of our encoded Amber potentials. From the comparison results between energy terms obtained from our encoding and Amber, we find energy difference within ±0.06 kcal/mol for all tested structures. Previously we have shown that the Random Forest (RF) model can help to emphasize more important atom pairwise interactions and ignore insignificant ones [Pei, J.; Zheng, Z.; Merz, K. M. J. Chem. Inf. Model. 2019, 59, 1919-1929]. Here, as an example of combining ML methods with traditional potential functions, we followed the same work flow to combine the RF models with force field potential functions from Amber. To determine the performance of our RF models with force field potential functions, 224 different protein native-decoy systems were used as our training and testing sets We find that the RF models with ff94 and ff14SB force field parameters outperformed all other scoring functions (RF models with KECSA2, RWplus, DFIRE, dDFIRE, and GOAP) considered in this work for native structure detection, and they performed similarly in detecting the best decoy. Through inclusion of best decoy to decoy comparisons in building our RF models, we were able to generate models that outperformed the score functions tested herein both on accuracy and best decoy detection, again showing the performance and flexibility of our RF models to tackle this problem. Finally, the importance of the RF algorithm and force field parameters were also tested and the comparison results suggest that both the RF algorithm and force field potentials are important with the ML scoring function achieving its best performance only by combining them together. All code and data used in this work are available at https://github.com/JunPei000/FFENCODER_for_Protein_Folding_Pose_Selection.

Thermodynamics of Transition Metal Ion Binding to Proteins.

Modeling the thermodynamics of a transition metal (TM) ion assembly be it in proteins or in coordination complexes affords us a better understanding of the assembly and function of metalloclusters in diverse application areas including metal organic framework design, TM-based catalyst design, the trafficking of TM ions in biological systems, and drug design in metalloprotein platforms. While the structural details of TM ions bound to metalloproteins are generally well understood via experimental and computational approaches, accurate studies describing the thermodynamics of TM ion binding are rare. Herein, we demonstrate that we can obtain accurate structural and absolute binding free energies of Co2+ and Ni2+ to the enzyme glyoxalase I using an optimized 12-6-4 (m12-6-4) potential. Critically, this model simultaneously reproduces the solvation free energy of the individual TM ions and reproduces the thermodynamics of TM ion-ligand coordination as well as the thermodynamics of TM ion binding to a protein active site unlike extant models. We find the incorporation of the thermodynamics associated with protonation state changes for the TM ion (un)binding to be crucial. The high accuracy of m12-6-4 potential in this study presents an accurate route to explore more complicated processes associated with TM cluster assembly and TM ion transport.

Random Forest Refinement of Pairwise Potentials for Protein-Ligand Decoy Detection.

An accurate scoring function is expected to correctly select the most stable structure from a set of pose candidates. One can hypothesize that a scoring function's ability to identify the most stable structure might be improved by emphasizing the most relevant atom pairwise interactions. However, it is hard to evaluate the relevant importance for each atom pair using traditional means. With the introduction of machine learning (ML) methods, it has become possible to determine the relative importance for each atom pair present in a scoring function. In this work, we use the Random Forest (RF) method to refine a pair potential developed by our laboratory (GARF; Zhang , Z. J. Chem. Theory Comput. 2018 , 14 , 5045 ) by identifying relevant atom pairs that optimize the performance of the potential on our given task. Our goal is to construct a ML model that can accurately differentiate the native ligand binding pose from candidate poses using a potential refined by RF optimization. We successfully constructed RF models on an unbalanced data set with the "comparison" concept, and the resultant RF models were tested on CASF-2013 ( Li , Y. J. Chem. Inf.Model. 2014 , 54 , 1700 ). In a comparison of the performance of our RF models against 29 scoring functions, we found that our models outperformed the other scoring functions in predicting the native pose. In addition, we created two artificially designed potential function sets to address the importance of the GARF potential in the RF models: (1) a scrambled probability function set, which was obtained by mixing up atom pairs and probability functions in GARF, and (2) a uniform probability function set, which shares the same peak positions with GARF but has fixed peak heights. The results of accuracy comparison from RF models based on the scrambled, uniform, and original GARF potential clearly showed that the peak positions in the GARF potential are important while the well depths are not. All code and data used in this work are available at https://github.com/JunPei000/random_forest_protein_ligand_decoy_detection .

Using AMBER18 for Relative Free Energy Calculations.

With renewed interest in free energy methods in contemporary structure-based drug design, there is a pressing need to validate against multiple targets and force fields to assess the overall ability of these methods to accurately predict relative binding free energies. We computed relative binding free energies using graphics processing unit accelerated thermodynamic integration (GPU-TI) on a data set originally assembled by Schrödinger, Inc. Using their GPU free energy code (FEP+) and the OPLS2.1 force field combined with the REST2 enhanced sampling approach, these authors obtained an overall MUE of 0.9 kcal/mol and an overall RMSD of 1.14 kcal/mol. In our study using GPU-TI from AMBER with the AMBER14SB/GAFF1.8 force field but without enhanced sampling, we obtained an overall MUE of 1.17 kcal/mol and an overall RMSD of 1.50 kcal/mol for the 330 perturbations contained in this data set. A more detailed analysis of our results suggested that the observed differences between the two studies arise from differences in sampling protocols along with differences in the force fields employed. Future work should address the problem of establishing benchmark quality results with robust statistical error bars obtained through multiple independent runs and enhanced sampling, which is possible with the GPU-accelerated features in AMBER.

Generation of Pairwise Potentials Using Multidimensional Data Mining.

The rapid development of molecular structural databases provides the chemistry community access to an enormous array of experimental data that can be used to build and validate computational models. Using radial distribution functions collected from experimentally available X-ray and NMR structures, a number of so-called statistical potentials have been developed over the years using the structural data mining strategy. These potentials have been developed within the context of the two-particle Kirkwood equation by extending its original use for isotropic monatomic systems to anisotropic biomolecular systems. However, the accuracy and the unclear physical meaning of statistical potentials have long formed the central arguments against such methods. In this work, we present a new approach to generate molecular energy functions using structural data mining. Instead of employing the Kirkwood equation and introducing the "reference state" approximation, we model the multidimensional probability distributions of the molecular system using graphical models and generate the target pairwise Boltzmann probabilities using the Bayesian field theory. Different from the current statistical potentials that mimic the "knowledge-based" PMF based on the 2-particle Kirkwood equation, the graphical-model-based structure-derived potential developed in this study focuses on the generation of lower-dimensional Boltzmann distributions of atoms through reduction of dimensionality. We have named this new scoring function GARF, and in this work we focus on the mathematical derivation of our novel approach followed by validation studies on its ability to predict protein-ligand interactions.

Detailed potential of mean force studies on host-guest systems from the SAMPL6 challenge.

Accurately predicting receptor-ligand binding free energies is one of the holy grails of computational chemistry with many applications in chemistry and biology. Many successes have been reported, but issues relating to sampling and force field accuracy remain significant issues affecting our ability to reliably calculate binding free energies. In order to explore these issues in more detail we have examined a series of small host-guest complexes from the SAMPL6 blind challenge, namely octa-acids (OAs)-guest complexes and Curcurbit[8]uril (CB8)-guest complexes. Specifically, potential of mean force studies using umbrella sampling combined with the weighted histogram method were carried out on both systems with both known and unknown binding affinities. We find that using standard force fields and straightforward simulation protocols we are able to obtain satisfactory results, but that simply scaling our results allows us to significantly improve our predictive ability for the unknown test sets: the overall RMSD of the binding free energy versus experiment is reduced from 5.59 to 2.36 kcal/mol; for the CB8 test system, the RMSD goes from 8.04 to 3.51 kcal/mol, while for the OAs test system, the RSMD goes from 2.89 to 0.95 kcal/mol. The scaling approach was inspired by studies on structurally related known benchmark sets: by simply scaling, the RMSD was reduced from 6.23 to 1.19 kcal/mol and from 2.96 to 0.62 kcal/mol for the CB8 benchmark system and the OA benchmark system, respectively. We find this scaling procedure to correct absolute binding affinities to be highly effective especially when working across a "congeneric" series with similar charge states. It is less successful when applied to mixed ligands with varied charges and chemical characteristics, but improvement is still realized in the present case. This approach suggests that there are large systematic errors in absolute binding free energy calculations that can be straightforwardly accounted for using a scaling procedure. Random errors are still an issue, but near chemical accuracy can be obtained using the present strategy in select cases.

The Role of the Active Site Flap in Streptavidin/Biotin Complex Formation.

Obtaining a detailed description of how active site flap motion affects substrate or ligand binding will advance structure-based drug design (SBDD) efforts on systems including the kinases, HSP90, HIV protease, ureases, etc. Through this understanding, we will be able to design better inhibitors and better proteins that have desired functions. Herein we address this issue by generating the relevant configurational states of a protein flap on the molecular energy landscape using an approach we call MTFlex-b and then following this with a procedure to estimate the free energy associated with the motion of the flap region. To illustrate our overall workflow, we explored the free energy changes in the streptavidin/biotin system upon introducing conformational flexibility in loop3-4 in the biotin unbound ( apo) and bound ( holo) state. The free energy surfaces were created using the Movable Type free energy method, and for further validation, we compared them to potential of mean force (PMF) generated free energy surfaces using MD simulations employing the FF99SBILDN and FF14SB force fields. We also estimated the free energy thermodynamic cycle using an ensemble of closed-like and open-like end states for the ligand unbound and bound states and estimated the binding free energy to be approximately -16.2 kcal/mol (experimental -18.3 kcal/mol). The good agreement between MTFlex-b in combination with the MT method with experiment and MD simulations supports the effectiveness of our strategy in obtaining unique insights into the motions in proteins that can then be used in a range of biological and biomedical applications.

Mechanism of Formation of the Nonstandard Product in the Prenyltransferase Reaction of the G115T Mutant of FtmPT1: A Case of Reaction Dynamics Calling the Shots?

FtmPT1 is a fungal indole prenyltransferase that affords Tryprostatin B from Brevianamide F and dimethylallyl pyrophosphate; however, when a single residue in the active site is mutated (Gly115Thr), a novel five-membered ring compound is obtained as the major product with Tryprostatin B as the minor product. Herein, we describe detailed studies of the catalysis of the Gly115Thr mutant of FtmPT1 with a focus on the observed regioselectivity of the reaction. We employ one- and two-dimensional potential of mean force simulations to explore the catalytic mechanism, along with molecular dynamics simulations exploring the reaction dynamics of the prenyl transfer reaction. Single-point electronic structure calculations were also used to explore the performance of the self-consistent charge density functional tight-binding method to model specific reaction steps. Importantly, we observe that the two reaction pathways have comparable activation parameters and propose that the origin of the novel product is predicated, at least in part, on the topology of the potential energy surface as revealed by reaction dynamics studies.

Systematic Parameterization of Monovalent Ions Employing the Nonbonded Model.

Monovalent ions play fundamental roles in many biological processes in organisms. Modeling these ions in molecular simulations continues to be a challenging problem. The 12-6 Lennard-Jones (LJ) nonbonded model is widely used to model monovalent ions in classical molecular dynamics simulations. A lot of parameterization efforts have been reported for these ions with a number of experimental end points. However, some reported parameter sets do not have a good balance between the two Lennard-Jones parameters (the van der Waals (VDW) radius and potential well depth), which affects their transferability. In the present work, via the use of a noble gas curve we fitted in former work (J. Chem. Theory Comput. 2013, 9, 2733), we reoptimized the 12-6 LJ parameters for 15 monovalent ions (11 positive and 4 negative ions) for three extensively used water models (TIP3P, SPC/E, and TIP4P(EW)). Since the 12-6 LJ nonbonded model performs poorly in some instances for these ions, we have also parameterized the 12-6-4 LJ-type nonbonded model (J. Chem. Theory Comput. 2014, 10, 289) using the same three water models. The three derived parameter sets focused on reproducing the hydration free energies (the HFE set) and the ion-oxygen distance (the IOD set) using the 12-6 LJ nonbonded model and the 12-6-4 LJ-type nonbonded model (the 12-6-4 set) overall give improved results. In particular, the final parameter sets showed better agreement with quantum mechanically calculated VDW radii and improved transferability to ion-pair solutions when compared to previous parameter sets.

Parameterization of highly charged metal ions using the 12-6-4 LJ-type nonbonded model in explicit water.

Highly charged metal ions act as catalytic centers and structural elements in a broad range of chemical complexes. The nonbonded model for metal ions is extensively used in molecular simulations due to its simple form, computational speed, and transferability. We have proposed and parametrized a 12-6-4 LJ (Lennard-Jones)-type nonbonded model for divalent metal ions in previous work, which showed a marked improvement over the 12-6 LJ nonbonded model. In the present study, by treating the experimental hydration free energies and ion-oxygen distances of the first solvation shell as targets for our parametrization, we evaluated 12-6 LJ parameters for 18 M(III) and 6 M(IV) metal ions for three widely used water models (TIP3P, SPC/E, and TIP4PEW). As expected, the interaction energy underestimation of the 12-6 LJ nonbonded model increases dramatically for the highly charged metal ions. We then parametrized the 12-6-4 LJ-type nonbonded model for these metal ions with the three water models. The final parameters reproduced the target values with good accuracy, which is consistent with our previous experience using this potential. Finally, tests were performed on a protein system, and the obtained results validate the transferability of these nonbonded model parameters.