Force Field Parametrization of Metal Ions from Statistical Learning Techniques

Quantum Dynamics and Bi Metal Force Field Parameterization Yielding Significant Antileishmanial Targets

Amid emerging drug resistance to metal inhibitors, high toxicity, and onerous drug delivery procedures, the computational design of alternate formulations encompassing functional metal-containing compounds greatly relies on large-scale atomistic simulations. Simulations particularly with Au(I), Ag, Bi(V), and Sb(V) pose a major challenge to elucidate their molecular mechanism due to the absence of force field parameters. This study thus quantum mechanically derives force field parameters of Bi(V) as an extension of the previous experimental study conducted on heteroleptic triorganobismuth(V) biscarboxylates of type [BiR3(O2CR')2]. We have modeled two organo-bismuth(V) carboxylates, which are optimized and parameterized along with the famous pentavalent antimonial drug: meglumine antimoniate using quantum mechanics original Seminarian methods with the SBKJC effective core potential (ECP) basis set. Furthermore, molecular dynamics (MD) simulations of bismuth- and antimony-containing compounds in complex with two enzymes, trypanothione synthetase-amidase (TSA) and trypanothione reductase, are performed to target the (T(SH)2) pathway at multiple points. MD simulations provide novel insights into the binding mechanism of TSA and highlight the role of a single residue Arg569 in modulating the ligand dynamics. Moreover, the presence of an ortho group in a ligand is emphasized to facilitate interactions between Arg569 and the active site residue Arg313 for higher inhibitory activity of TSA. This preliminary generation of parameters specific to bismuth validated by simulations in replica will become a preamble of future computational and experimental research work to open avenues for newer and suitable drug targets.

Integration of Quantum Chemistry, Statistical Mechanics, and Artificial Intelligence for Computational Spectroscopy: The UV-Vis Spectrum of TEMPO Radical in Different Solvents

The ongoing integration of quantum chemistry, statistical mechanics, and artificial intelligence is paving the route toward more effective and accurate strategies for the investigation of the spectroscopic properties of medium-to-large size chromophores in condensed phases. In this context we are developing a novel workflow aimed at improving the generality, reliability, and ease of use of the available computational tools. In this paper we report our latest developments with specific reference to unsupervised atomistic simulations employing non periodic boundary conditions (NPBC) followed by clustering of the trajectories employing optimized feature spaces. Next accurate variational computations are performed for a representative point of each cluster, whereas intracluster fluctuations are taken into account by a cheap yet reliable perturbative approach. A number of methodological improvements have been introduced including, e.g., more realistic reaction field effects at the outer boundary of the simulation sphere, automatic definition of the feature space by continuous perception of solute–solvent interactions, full account of polarization and charge transfer in the first solvation shell, and inclusion of vibronic contributions. After its validation, this new approach has been applied to the challenging case of solvatochromic effects on the UV–vis spectra of a prototypical nitroxide radical (TEMPO) in different solvents. The reliability, effectiveness, and robustness of the new platform is demonstrated by the remarkable agreement with experiment of the results obtained through an unsupervised approach characterized by a strongly reduced computational cost as compared to that of conventional quantum mechanics and molecular mechanics models without any accuracy reduction.

MolE8: finding DFT potential energy surface minima values from force-field optimised organic molecules with new machine learning representations

The use of machine learning techniques in computational chemistry has gained significant momentum since large molecular databases are now readily available. Predictions of molecular properties using machine learning have advantages over the traditional quantum mechanics calculations because they can be cheaper computationally without losing the accuracy. We present a new extrapolatable and explainable molecular representation based on bonds, angles and dihedrals that can be used to train machine learning models. The trained models can accurately predict the electronic energy and the free energy of small organic molecules with atom types C, H N and O, with a mean absolute error of 1.2 kcal mol-1. The models can be extrapolated to larger organic molecules with an average error of less than 3.7 kcal mol-1 for 10 or fewer heavy atoms, which represent a chemical space two orders of magnitude larger. The rapid energy predictions of multiple molecules, up to 7 times faster than previous ML models of similar accuracy, has been achieved by sampling geometries around the potential energy surface minima. Therefore, the input geometries do not have to be located precisely on the minima and we show that accurate density functional theory energy predictions can be made from force-field optimised geometries with a mean absolute error 2.5 kcal mol-1.

Bicyclo-DNA mimics with enhanced protein binding affinities: insights from molecular dynamics simulations

DNA-protein interactions occur at all levels of DNA expression and replication and are crucial determinants for the survival of a cell. Several modified nucleotides have been utilized to manipulate these interactions and have implications in drug discovery. In the present article, we evaluated the binding of bicyclo-nucleotides (generated by forming a methylene bridge between C1' and C5' in sugar, leading to a bicyclo system with C2' axis of symmetry at the nucleotide level) to proteins. We utilized four ssDNA-protein complexes with experimentally known binding free energies and investigated the binding of modified nucleotides to proteins via all-atom explicit solvent molecular dynamics (MD) simulations (200 ns), and compared the binding with control ssDNA-protein systems. The modified ssDNA displayed enhanced binding to proteins as compared to the control ssDNA, as seen by means of MD simulations followed by MM-PBSA calculations. Further, the Delphi-based electrostatic estimation revealed that the high binding of modified ssDNA to protein might be related to the enhanced electrostatic complementarity displayed by the modified ssDNA molecules in all the four systems considered for the study. The improved binding achieved with modified nucleotides can be utilized to design and develop anticancer/antisense molecules capable of targeting proteins or ssRNAs.Communicated by Ramaswamy H. Sarma.

Unlocking the computational design of metal-organic cages

Metal-organic cages are macrocyclic structures that can possess an intrinsic void that can hold molecules for encapsulation, adsorption, sensing, and catalysis applications. As metal-organic cages may be comprised from nearly any combination of organic and metal-containing components, cages can form with diverse shapes and sizes, allowing for tuning toward targeted properties. Therefore, their near-infinite design space is almost impossible to explore through experimentation alone and computational design can play a crucial role in exploring new systems. Although high-throughput computational design and screening workflows have long been known as powerful tools in drug and materials discovery, their application in exploring metal-organic cages is more recent. We show examples of structure prediction and host-guest/catalytic property evaluation of metal-organic cages. These examples are facilitated by advances in methods that handle metal-containing systems with improved accuracy and are the beginning of the development of automated cage design workflows. We finally outline a scope for how high-throughput computational methods can assist and drive experimental decisions as the field pushes toward functional and complex metal-organic cages. In particular, we highlight the importance of considering realistic, flexible systems.

Molecular Dynamics Simulations Enforcing Nonperiodic Boundary Conditions: New Developments and Application to the Solvent Shifts of Nitroxide Magnetic Parameters

Multiscale methods combining quantum mechanics and molecular mechanics (QM/MM) have become the most suitable and effective strategies for the investigation of the spectroscopic properties of medium-to-large size chromophores in condensed phases. In this context, we are developing a novel workflow aimed at improving the generality, reliability, and ease of use of the available computational tools. In this paper, we report our latest developments with specific reference to a general protocol based on atomistic simulations, carried out under nonperiodic boundary conditions (NPBC). In particular, we add to our in house MD engine a new efficient treatment of mean field electrostatic contributions to energy and forces, together with the capability of performing the simulations either in the canonical (NVT) or in the isothermal–isobaric (NPT) ensemble. Next, we provide convincing evidence that the NBPC approach enhanced by specific tweaks for rigid body propagation, allows for the simulation of solute–solvent systems with a minimum number of degrees of freedom and large integration time step. After its validation, this new approach is applied to the challenging case of solvatochromic effects on the electron paramagnetic resonance (EPR) spectrum of a prototypical nitroxide radical. To this end, we propose and validate also an automated protocol to extract and weight simulation snapshots, making use of a continuous description of the strength of solute–solvent hydrogen bridges. While further developments are being worked on, the effectiveness of our approach, even in its present form, is demonstrated by the accuracy of the results obtained through an unsupervised approach characterized by a strongly reduced computational cost as compared to that of conventional QM/MM models, without any appreciable deterioration of accuracy.

A multiscale approach to predict the binding mode of metallo beta-lactamase inhibitors

Antibiotic resistance is a major threat to global public health. β-lactamases, which catalyze breakdown of β-lactam antibiotics, are a principal cause. Metallo β-lactamases (MBLs) represent a particular challenge because they hydrolyze almost all β-lactams and to date no MBL inhibitor has been approved for clinical use. Molecular simulations can aid drug discovery, for example, predicting inhibitor complexes, but empirical molecular mechanics (MM) methods often perform poorly for metalloproteins. Here we present a multiscale approach to model thiol inhibitor binding to IMP-1, a clinically important MBL containing two catalytic zinc ions, and predict the binding mode of a 2-mercaptomethyl thiazolidine (MMTZ) inhibitor. Inhibitors were first docked into the IMP-1 active site, testing different docking programs and scoring functions on multiple crystal structures. Complexes were then subjected to molecular dynamics (MD) simulations and subsequently refined through QM/MM optimization with a density functional theory (DFT) method, B3LYP/6-31G(d), increasing the accuracy of the method with successive steps. This workflow was tested on two IMP-1:MMTZ complexes, for which it reproduced crystallographically observed binding, and applied to predict the binding mode of a third MMTZ inhibitor for which a complex structure was crystallographically intractable. We also tested a 12-6-4 nonbonded interaction model in MD simulations and optimization with a SCC-DFTB QM/MM approach. The results show the limitations of empirical models for treating these systems and indicate the need for higher level calculations, for example, DFT/MM, for reliable structural predictions. This study demonstrates a reliable computational pipeline that can be applied to inhibitor design for MBLs and other zinc-metalloenzyme systems.

Modeling Kinetics and Thermodynamics of Guest Encapsulation into the [M4L6]12- Supramolecular Organometallic Cage

The encapsulation of molecular guests into supramolecular hosts is a complex molecular recognition process in which the guest displaces the solvent from the host cavity, while the host deforms to let the guest in. An atomistic description of the association would provide valuable insights on the physicochemical properties that guide it. This understanding may be used to design novel host assemblies with improved properties (i.e., affinities) toward a given class of guests. Molecular simulations may be conveniently used to model the association processes. It is thus of interest to establish efficient protocols to trace the encapsulation process and to predict the associated magnitudes ΔG_bind and ΔG_bind^⧧. Here, we report the calculation of the Gibbs energy barrier and Gibbs binding energy by means of explicit solvent molecular simulations for the [Ga₄L₆]^12– metallocage encapsulating a series of cationic molecules. The ΔG_bind^⧧ for encapsulation was estimated by means of umbrella sampling simulations. The steps involved were identified, including ion-pair formation and naphthalene rotation (from L ligands of the metallocage) during the guest’s entrance. The ΔG_bind values were computed using the attach–pull–release method. The results reveal the sensitivity of the estimates on the force field parameters, in particular on atomic charges, showing that higher accuracy is obtained when charges are derived from implicit solvent quantum chemical calculations. Correlation analysis identified some indicators for the binding affinity trends. All computed magnitudes are in very good agreement with experimental observations. This work provides, on one side, a benchmarked way to computationally model a highly charged metallocage encapsulation process. This includes a nonstandard parameterization and charge derivation procedure. On the other hand, it gives specific mechanistic information on the binding processes of [Ga₄L₆]^12– at the molecular level where key motions are depicted. Taken together, the study provides an interesting option for the future design of metal–organic cages.

Coarse-Grained Modeling and Molecular Dynamics Simulations of Ca2+-Calmodulin

Calmodulin (CaM) is a calcium-binding protein that transduces signals to downstream proteins through target binding upon calcium binding in a time-dependent manner. Understanding the target binding process that tunes CaM’s affinity for the calcium ions (Ca²⁺), or vice versa, may provide insight into how Ca²⁺-CaM selects its target binding proteins. However, modeling of Ca²⁺-CaM in molecular simulations is challenging because of the gross structural changes in its central linker regions while the two lobes are relatively rigid due to tight binding of the Ca²⁺ to the calcium-binding loops where the loop forms a pentagonal bipyramidal coordination geometry with Ca²⁺. This feature that underlies the reciprocal relation between Ca²⁺ binding and target binding of CaM, however, has yet to be considered in the structural modeling. Here, we presented a coarse-grained model based on the Associative memory, Water mediated, Structure, and Energy Model (AWSEM) protein force field, to investigate the salient features of CaM. Particularly, we optimized the force field of CaM and that of Ca²⁺ ions by using its coordination chemistry in the calcium-binding loops to match with experimental observations. We presented a “community model” of CaM that is capable of sampling various conformations of CaM, incorporating various calcium-binding states, and carrying the memory of binding with various targets, which sets the foundation of the reciprocal relation of target binding and Ca²⁺ binding in future studies.

Low Density Interior in Supercooled Aqueous Nanodroplets Expels Ions to the Subsurface

The interaction between water and ions within droplets plays a key role in the chemical reactivity of atmospheric and man-made aerosols. Here we report direct computational evidence that in supercooled aqueous nanodroplets a lower density core of tetrahedrally coordinated water expels the cosmotropic ions to the denser and more disordered subsurface. In contrast, at room temperature, depending on the nature of the ion, the radial distribution in the droplet core is nearly uniform or elevated toward the center. We analyze the spatial distribution of a single ion in terms of a reference electrostatic model. The energy of the system in the analytical model is expressed as the sum of the electrostatic and surface energy of a deformable droplet. The model predicts that the ion is subject to a harmonic potential centered at the droplet's center of mass. We name this effect "electrostatic confinement". The model's predictions are consistent with the simulation findings for a single ion at room temperature but not at supercooling. We anticipate this study to be the starting point for investigating the structure of supercooled (electro)sprayed droplets that are used to preserve the conformations of macromolecules originating from the bulk solution.

Integration of theory, simulation, artificial intelligence and virtual reality: a four-pillar approach for reconciling accuracy and interpretability in computational spectroscopy

The established pillars of computational spectroscopy are theory and computer based simulations. Recently, artificial intelligence and virtual reality are becoming the third and fourth pillars of an integrated strategy for the investigation of complex phenomena. The main goal of the present contribution is the description of some new perspectives for computational spectroscopy, in the framework of a strategy in which computational methodologies at the state of the art, high-performance computing, artificial intelligence and virtual reality tools are integrated with the aim of improving research throughput and achieving goals otherwise not possible. Some of the key tools (e.g., continuous molecular perception model and virtual multifrequency spectrometer) and theoretical developments (e.g., non-periodic boundaries, joint variational-perturbative models) are shortly sketched and their application illustrated by means of representative case studies taken from recent work by the authors. Some of the results presented are already well beyond the state of the art in the field of computational spectroscopy, thereby also providing a proof of concept for other research fields.

A Perspective on Modelling Metallic Magnetic Nanoparticles in Biomedicine: From Monometals to Nanoalloys and Ligand-Protected Particles

The focus of this review is on the physical and magnetic properties that are related to the efficiency of monometallic magnetic nanoparticles used in biomedical applications, such as magnetic resonance imaging (MRI) or magnetic nanoparticle hyperthermia, and how to model these by theoretical methods, where the discussion is based on the example of cobalt nanoparticles. Different simulation systems (cluster, extended slab, and nanoparticle models) are critically appraised for their efficacy in the determination of reactivity, magnetic behaviour, and ligand-induced modifications of relevant properties. Simulations of the effects of nanoscale alloying with other metallic phases are also briefly reviewed.

Symmetric Nucleosides as Potent Purine Nucleoside Phosphorylase Inhibitors

Nucleic acids are one of the most enigmatic biomolecules crucial to several biological processes. Nucleic acid-protein interactions are vital for the coordinated and controlled functioning of a cell, leading to the design of several nucleoside/nucleotide analogues capable of mimicking these interactions and hold paramount importance in the field of drug discovery. Purine nucleoside phosphorylase is a well-established drug target due to its association with numerous immunodeficiency diseases. Here, we study the binding of human purine nucleoside phosphorylase (PNP) to some bidirectional symmetric nucleosides, a class of nucleoside analogues that are more flexible due to the absence of sugar pucker restraints. We compared the binding energies of PNP-symmetric nucleosides to the binding energies of PNP-inosine/Imm-H (a transition-state analogue), by means of 200 ns long all-atom explicit-solvent Gaussian accelerated molecular dynamics simulations followed by energetics estimation using the MM-PBSA methodology. Quite interestingly, we observed that a few symmetric nucleosides, namely, ν3 and ν4, showed strong binding with PNP (-14.1 and -12.6 kcal/mol, respectively), higher than inosine (-6.3 kcal/mol) and Imm-H (-9.6 kcal/mol). This is rationalized by an enhanced hydrogen-bond network for symmetric nucleosides compared to inosine and Imm-H while maintaining similar van der Waals contacts. We note that the chemical structures of both ν3 and ν4, due to an additional unsaturation in them, resemble enzymatic transition states and fall in the category of transition-state analogues (TSAs), which are quite popular.

Marshall's nucleic acid: From double-helical structure to a potent intercalator

Deoxyribonucleic acid (DNA) not only stores genetic information but also emerged as a popular drug target. Modified nucleotides/nucleosides have been extensively studied in recent years wherein the sugar/nucleobase/phosphate-backbone has been altered. Several such molecules are FDA approved, capable of targeting nucleic acids and proteins. In this article, we modified negatively charged phosphate backbone to marshall's acid-based neutral backbone and analyzed the resultant structures by utilizing Gaussian accelerated molecular dynamics simulations (1 μs) in aqueous media at 150 mM salt concentration. We noted that the double-helical marshall's nucleic acid structure was partially denatured during the course of simulations, however, after using conformationally locked sugar, the marshall's nucleic acid (hereby called MNA) maintained the double-helical structure throughout the simulations. Despite the fact that MNA has a more extended backbone than the regular DNA, surprisingly, both showed similar helical rise (~3.4 Å) along with a comparable Watson-Crick hydrogen bond profile. The backbone difference was majorly compensated in terms of helical twist (~56° (MNA) and ~ 35° (control DNA)). Further, we examined a few MNA based ss-dinucleotides as intercalating ligands for a regular B-DNA. Quite strikingly, the ligands unwinded the DNA and showed intercalating properties with high DNA binding affinities. Hence, the use of small fragments of MNA based molecules in DNA targeted drug discovery is foreseen.

Computational Spectroscopy in Solution by Integration of Variational and Perturbative Approaches on Top of Clusterized Molecular Dynamics

Multiscale QM/MM approaches have become the most suitable and effective methods for the investigation of spectroscopic properties of medium- or large-size chromophores in condensed phases. On these grounds, we are developing a novel workflow aimed at improving the generality, reliability, and ease of use of the available tools. In the present paper, we report the latest developments of such an approach with specific reference to a general workplan starting with the addition of acetonitrile to the panel of solvents already available in the General Liquid Optimized Boundary (GLOB) model enforcing nonperiodic boundary conditions (NPBC). Next, the solvatochromic shifts induced by acetonitrile on both rigid (uracil and thymine) and flexible (thyrosine) chromophores have been studied introducing in our software a number of new features ranging from rigid-geometry NPBC molecular dynamics based on the quaternion formalism to a full integration of variational (ONIOM) and perturbative (perturbed matrix method (PMM)) approaches for describing different solute–solvent topologies and local fluctuations, respectively. Finally, thymine and uracil have been studied also in methanol to point out the generality of the computational strategy. While further developments are surely needed, the strengths of our integrated approach even in its present version are demonstrated by the accuracy of the results obtained by an unsupervised approach and coupled to a computational cost strongly reduced with respect to that of conventional QM/MM models without any appreciable accuracy deterioration.

The role of hydrogen bond donor and water content on the electrochemical reduction of Ni2+ from solvents - an experimental and modelling study

Deep Eutectic Solvents (DESs) are hygroscopic liquids composed of a hydrogen bond donor (HBD) and acceptor (HBA). Their physical, chemical and electrochemical properties can be tailored to use them as solvents for different applications, i.e. electrodeposition, catalysis, extraction, etc. This can be done by changing the HBD, as well by adding water. However, the interrelated influence of H2O and HBD on the structure of the electrolyte, and on the behavior of the active species is not fully understood. In this work, we select nickel electrodeposition as a case study and we combine electrochemical techniques (cyclic voltammetry, chronoamperometry) with UV-vis spectroscopy and molecular dynamics to understand the influence of water and HBD on the electrochemical behaviour of DESs. The unique combination of these different experimental and modelling techniques provides new insights into the field. The addition of H2O changes, not only the interactions between the constituents of the liquid, but also the coordination of metal cations, which is reflected in the electrochemical performance of different DESs. More importantly, we show that, in the presence of very little (between 0.1 wt% and 2.8 wt%) and high (>4.5 wt%) water contents, DESs behave differently, and the changes in their electrochemical behavior are caused by both the complexation of metal cations and the electrolyte transport properties.

Upgraded AMBER Force Field for Zinc-Binding Residues and Ligands for Predicting Structural Properties and Binding Affinities in Zinc-Proteins

We developed a novel force field in the context of AMBER parameterization for glutamate and aspartate zinc(II)-binding residues. The interaction between the zinc ion and the coordinating atoms is represented by a spherical nonbonded parameterization. The polarization effect due to the zinc ion has been taken into account by redefining the atomic charges on the residues through accurate quantum mechanical calculations. The new zinc-binding ASP and GLU residues, along with the CYS and HIS zinc-binding residues, parameterized in a recent work [Macchiagodena M.;J. Chem. Inf. Model.2019, 59, 3803-3816], allow users to reliably simulate 96% of the Zn-proteins available in the Protein Data Bank. The upgraded force field for zinc(II)-bound residues has been tested performing molecular dynamics simulations with an explicit solvent and comparing the structural information with experimental data for five different proteins binding zinc(II) with GLU, ASP, HIS, and CYS. We further validated our approach by evaluating the binding free energy of (R)-2-benzyl-3-nitropropanoic acid to carboxypeptidase A using a recently developed nonequilibrium alchemical method. We demonstrated that in this setting it is crucial to take into account polarization effects also on the metal-bound inhibitor.

Support Vector Regression-Based Monte Carlo Simulation of Flexible Water Clusters

Molecular simulations based on classical force fields are computationally efficient but lack accuracy due to the empirical formulation of non-bonded interactions. Quantum mechanical (QM) methods, albeit accurate, have inhibitory computational costs for large molecules and clusters. Hence, to overcome the bottleneck, machine learning (ML)-based methods have been employed in the recent years. We had earlier reported a combined scheme of many-body expansion (MBE) and ML to predict the interaction energies of rigid water clusters. In this work, we proceed toward building a flexible water model using the ML-MBE scheme. This ML-MBE scheme has an error of <1% for interaction energy prediction in comparison to the parent QM method for flexible water decamers. Machine learning-based Monte Carlo simulations (MLMC) are performed with this water model, and the structural properties of these configurations are compared with those obtained from ab initio molecular dynamics (AIMD) and the TIP3P classical force field. The radial distribution functions, tetrahedral order parameters, and number of hydrogen bonds in AIMD and MLMC have a similar qualitative and quantitative trend, whereas the classical force fields show a significant deviation.

Comparing Nonbonded Metal Ion Models in the Divalent Cation Binding Protein PsaA

Divalent metal cations are essential for many biological processes; however, accurately modeling divalent metal ions has proved a significant challenge for molecular dynamics force fields. Here we show that the choice of ion model influences the observed dynamics in PsaA, a metal binding protein from Streptococcus pneumoniae. We conduct extensive unbiased simulations and free energy calculations of PsaA bound to its cognate ligand Mn²⁺ and inhibitory ligand Zn²⁺ using three nonbonded ion models: a 12-6 model, a 12-6-4 model, and a multisite model. The observed coordination geometries and metal binding dynamics are sensitive to the choice of ion model, with the most dramatic differences observed in free energy calculations of ion release. We show that the conformational ensemble of Mn-bound PsaA is more similar to the crystallographic metal bound open state. This work extends the current model of PsaA metal binding and provides a framework for the rationalization of experimentally determined metal binding behavior. Our findings support the use of the 12-6-4 ion model for further simulations of divalent cation binding proteins.

CBSF: A New Empirical Scoring Function for Docking Parameterized by Weights of Neural Network

A new CBSF empirical scoring function for the estimation of binding energies between proteins and small molecules is proposed in this report. The final score is obtained as a sum of three energy terms calculated using descriptors based on a simple counting of the interacting protein-ligand atomic pairs. All the required weighting coefficients for this method were derived from a pretrained neural network. The proposed method demonstrates a high accuracy and reproduces binding energies of protein-ligand complexes from the CASF-2016 test set with a standard deviation of 2.063 kcal/mol (1.511 log units) and an average error of 1.682 kcal/mol (1.232 log units). Thus, CBSF has a significant potential for the development of rapid and accurate estimates of the protein-ligand interaction energies.

Quantum Mechanical and Molecular Mechanics Modeling of Membrane-Embedded Rhodopsins

Computational chemistry provides versatile methods for studying the properties and functioning of biological systems at different levels of precision and at different time scales. The aim of this article is to review the computational methodologies that are applicable to rhodopsins as archetypes for photoactive membrane proteins that are of great importance both in nature and in modern technologies. For each class of computational techniques, from methods that use quantum mechanics for simulating rhodopsin photophysics to less-accurate coarse-grained methodologies used for long-scale protein dynamics, we consider possible applications and the main directions for improvement.

Upgrading and Validation of the AMBER Force Field for Histidine and Cysteine Zinc(II)-Binding Residues in Sites with Four Protein Ligands

We developed and validated a novel force field in the context of the AMBER parameterization for the simulation of zinc(II)-binding proteins. The proposed force field assumes nonbonded spherical interactions between the central zinc(II) and the coordinating residues. A crucial innovative aspect of our approach is to account for the polarization effects of the cation by redefining the atomic charges of the coordinating residues and an adjustment of Lennard-Jones parameters of Zn-interacting atoms to reproduce mean distance distributions. The optimal transferable parametrization was obtained by performing accurate quantum mechanical calculations on a training set of high-quality protein structures, encompassing the most common folds of zinc(II) sites. The addressed sites contain a zinc(II) ion tetra-coordinated by histidine and cysteine residues and represent about 70% of all physiologically relevant zinc(II) sites in the Protein Data Bank. Molecular dynamics simulations with explicit solvent, carried out on several zinc(II)-binding proteins not included in the training set, show that our model for zinc(II) sites preserves the tetra-coordination of the metal site with remarkable stability, yielding zinc(II)-X mean distances similar to experimental data. Finally, the model was tested by evaluating the zinc(II)-binding affinities, using the alchemical free energy perturbation approach. The calculated dissociation constants correlate satisfactorily with the experimental counterpart demonstrating the validity and transferability of the proposed parameterization for zinc(II)-binding proteins.

Development of Nonbonded Models for Metal Cations Using the Electronic Continuum Correction

The parametrization of classical nonbonded models of metal ions has been widely addressed in the recent years. Despite the continuous development of novel and more physically inspired functional forms, the 12-6 Lennard-Jones plus Coulomb potential is still the most adopted force field in molecular dynamics (MD) codes, owing to its simple form and easy implementation. However, due to the integer formal charge, unpolarizable force fields of ions may suffer from overestimated interatomic electrostatic interactions, leading to nonphysical clustering or repulsion between such full charges. The electronic continuum correction (ECC) can fix this problem through a simple inclusion of solvent polarization effects via ionic charge rescaling. In this work, the development of novel nonbonded models for mono, divalent, and highly charged metal ions is presented. For each metal species, the ionic charge has been scaled, according to the ECC. Lennard-Jones parameters have been optimized using experimental structural and thermodynamic properties as target quantities. Performances of the proposed models are discussed and compared with the literature data, while transferability attitudes among different and well-known water models are evaluated. © 2019 Wiley Periodicals, Inc.

Two-level stochastic search of low-energy conformers for molecular spectroscopy: implementation and validation of MM and QM models

The search for stationary points in the molecular potential energy surfaces (PES) is a problem of increasing relevance in molecular sciences especially for large, flexible systems featuring several large-amplitude internal motions.

Mechanistic insights into metal ions transit through threefold ferritin channel

BACKGROUND

The mechanism of how the hydrophilic threefold channel (C3) of ferritin nanocages facilitates diffusion of diverse metal ions into the internal cavity remains poorly explored.

METHODS

Computational modeling and free energy estimations were carried out on R. catesbeiana H´ ferritin. Transit features and associated energetics for Fe²⁺, Mg²⁺, Zn²⁺ ions through the C3 channel have been examined.

RESULTS

We highlight that iron conduction requires the involvement of two Fe²⁺ ions in the channel. In such doubly occupied configuration, as observed in X-ray structures, Fe²⁺ is displaced from the internal site (stabilized by D127) at lower energetic cost. Moreover, comparison of Fe²⁺, Mg²⁺ and Zn²⁺ transit features shows that E130 geometric constriction provides not only an electrostatic anchor to the incoming ions but also differentially influence their diffusion kinetics.

CONCLUSIONS

Overall, the study provides insights into Fe²⁺ entry mechanism and characteristic features of metal-protein interactions that influence the metal ions passage. The dynamics data suggest that E130 may act as a metal selectivity gate. This implicates an ion-specific entry mechanism through the channel with the distinct diffusion kinetics being the discriminating factor.

GENERAL SIGNIFICANCE

Ferritin nanocages not only act as biological iron reservoirs but also have gained importance in material science as template scaffolds for synthesizing metal nanoparticles. This study provides mechanistic understanding on the conduction of different metal ions through the channel.

Machine learning prediction of interaction energies in rigid water clusters

Classical force fields form a computationally efficient avenue for calculating the energetics of large systems. However, due to the constraints of the underlying analytical form, it is sometimes not accurate enough. Quantum mechanical (QM) methods, although accurate, are computationally prohibitive for large systems. In order to circumvent the bottle-neck of interaction energy estimation of large systems, data driven approaches based on machine learning (ML) have been employed in recent years. In most of these studies, the method of choice is artificial neural networks (ANN). In this work, we have shown an alternative ML method, support vector regression (SVR), that provides comparable accuracy with better computational efficiency. We have further used many body expansion (MBE) along with SVR to predict interaction energies in water clusters (decamers). In the case of dimer and trimer interaction energies, the root mean square errors (RMSEs) of the SVR based scheme are 0.12 kcal mol-1 and 0.34 kcal mol-1, respectively. We show that the SVR and MBE based scheme has a RMSE of 2.78% in the estimation of decamer interaction energy against the parent QM method in a computationally efficient way.

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

We construct a robust chemistry consisting of a nearsighted neural network potential, TensorMol-0.1, with screened long-range electrostatic and van der Waals physics. It is offered in an open-source Python package and achieves millihartree accuracy and a scalability to tens-of-thousands of atoms on ordinary laptops.

Traditional force fields cannot model chemical reactivity, and suffer from low generality without re-fitting. Neural network potentials promise to address these problems, offering energies and forces with near ab initio accuracy at low cost. However a data-driven approach is naturally inefficient for long-range interatomic forces that have simple physical formulas. In this manuscript we construct a hybrid model chemistry consisting of a nearsighted neural network potential with screened long-range electrostatic and van der Waals physics. This trained potential, simply dubbed “TensorMol-0.1”, is offered in an open-source Python package capable of many of the simulation types commonly used to study chemistry: geometry optimizations, harmonic spectra, open or periodic molecular dynamics, Monte Carlo, and nudged elastic band calculations. We describe the robustness and speed of the package, demonstrating its millihartree accuracy and scalability to tens-of-thousands of atoms on ordinary laptops. We demonstrate the performance of the model by reproducing vibrational spectra, and simulating the molecular dynamics of a protein. Our comparisons with electronic structure theory and experimental data demonstrate that neural network molecular dynamics is poised to become an important tool for molecular simulation, lowering the resource barrier to simulating chemistry.

Towards the SMART workflow system for computational spectroscopy

Is it possible to convert highly specialized research in the field of computational spectroscopy into robust and user-friendly aids to experiments and industrial applications?