Molecular Mechanics Force Field Research Articles

Predicting the Antifungal Activity of Small Organic Compounds on Aspergillus niger Mold using Molecular Dynamics Simulations.

Atomistic models of the plasma membrane of the pathogenic mold Aspergillus niger are developed. These models are described with an empirical molecular mechanical (MM) force field in combination with molecular dynamics (MD) simulations. The solvated plasma membrane models are brought into contact with 35 small organic compounds to observe their impact on a variety of membrane properties. All compounds are added at a constant total mass of 1% of the membrane mass. In addition, the ability of these compounds to inhibit the pathogenic cell growth of mold has been measured. Diffusion of compounds into the membrane model is readily observed during MD simulations. Changes in membrane properties found in simulations are not found to correlate with measured antifungal activities of compounds, suggesting that MD simulations of up to 1 μs are not sufficiently long to adequately describe compound-induced membrane disruption. However, properties related to the position and orientation of compounds relative to the membrane surface as well as hydrogen bonds formed between the compounds and the membrane show clear trends that correlate well with measured activities. A combination of these properties enables an activity prediction of compounds in good agreement with measurements. Activity is found predominantly for compounds that can be decomposed into a single continuous hydrophobic and hydrophilic moiety. Such active compounds can be energetically inserted most favorably into the membrane. These insertions destabilize the membrane by disrupting the internal membrane hydrogen bond network and by sliding between neighboring lipids, thereby separating them.

Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol.

Progress in machine learning has facilitated the development of potentials that offer both the accuracy of first-principles techniques and vast increases in the speed of evaluation. Recently, Δ-machine learning has been used to elevate the quality of a potential energy surface (PES) based on low-level, e.g., density functional theory (DFT) energies and gradients to close to the gold-standard coupled cluster level of accuracy. We have demonstrated the success of this approach for molecules, ranging in size from H3O+ to 15-atom acetyl-acetone and tropolone. These were all done using the B3LYP functional. Here, we investigate the generality of this approach for the PBE, M06, M06-2X, and PBE0 + MBD functionals, using ethanol as the example molecule. Linear regression with permutationally invariant polynomials is used to fit both low-level and correction PESs. These PESs are employed for standard RMSE analysis for training and test data sets, and then general fidelity tests such as energetics of stationary points, normal-mode frequencies, and torsional potentials are examined. We achieve similar improvements in all cases. Interestingly, we obtained significant improvement over DFT gradients where coupled cluster gradients were not used to correct the low-level PES. Finally, we present some results for correcting a recent molecular mechanics force field for ethanol and comment on the possible generality of this approach.

Modeling conformational changes in alginic acid oligomers induced by external forces

In this study, the mechanism and nature of mechanical force-induced conformational transitions of alginate oligomers with different ratios of β-D-mannuronic acid (M unit) and α-L-guluronic acid (G unit) units were investigated. The influence of the type of glycosidic linkage in either homo- or heterooligomers on the nature of conformational transitions was also considered. For this purpose, two different theoretical methods were used: quantum mechanics (QM) at the DFT level with the EGO (Enforced Geometry Optimization) approach previously tested also for other saccharide systems, and molecular dynamics (MD) simulations within hybrid interaction potentials, which take into account both the ab initio (QM) level of theory and classical molecular mechanics (MM) force fields. This allowed to characterize in detail the structural and energetic properties of the conformational transition occurring upon the influence of external, mechanical forces (e.g. ring conformations at the path of ring-inversion process as well as the energies corresponding to initial, final and intermediate states). The results indicate qualitatively various responses against the applied force, depending on the G:M ratio, which have their source in differing topologies of glycosidic linkage in either G or M units. This is of potential relevance for determining the content of naturally heterogeneous alginate chains by the AFM experimental studies. The effects of explicit solvent and non-zero temperature are not of primarily importance in the context of determined stretching properties.

Quantum Mechanics Characterization of Non-Covalent Interaction in Nucleotide Fragments.

Accurate calculation of non-covalent interaction energies in nucleotides is crucial for understanding the driving forces governing nucleic acid structure and function, as well as developing advanced molecular mechanics forcefields or machine learning potentials tailored to nucleic acids. Here, we dissect the nucleotides' structure into three main constituents: nucleobases (A, G, C, T, and U), sugar moieties (ribose and deoxyribose), and phosphate group. The interactions among these fragments and between fragments and water were analyzed. Different quantum mechanical methods were compared for their accuracy in capturing the interaction energy. The non-covalent interaction energy was decomposed into electrostatics, exchange-repulsion, dispersion, and induction using two ab initio methods: Symmetry-Adapted Perturbation Theory (SAPT) and Absolutely Localized Molecular Orbitals (ALMO). These calculations provide a benchmark for different QM methods, in addition to providing a valuable understanding of the roles of various intermolecular forces in hydrogen bonding and aromatic stacking. With SAPT, a higher theory level and/or larger basis set did not necessarily give more accuracy. It is hard to know which combination would be best for a given system. In contrast, ALMO EDA2 did not show dependence on theory level or basis set; additionally, it is faster.

Molecular docking and molecular dynamics studies of Glu‐Glu‐Arg, Glu‐Pro‐Arg, and Pro‐Arg‐Pro tripeptides to reveal their anticancer and antiviral potentials

AbstractBioactive peptides have been emerging as drug candidates with increasing importance in the last few decades. In this study, to evaluate the anticancer and antiviral properties of EER (Glu‐Glu‐Arg), EPR (Glu‐Pro‐Arg), and PRP (Pro‐Arg‐Pro) tripeptides, firstly their conformation preferences were searched, and the most stable optimized structure of each tripeptide was determined, using the molecular mechanics force field (MMFF) method and the Spartan06 program. Afterwards, each tripeptide was docked to SARS‐CoV‐2 spike protein receptor‐binding domain (6M0J), SARS‐CoV‐2 main protease (6M03, 6LU7), spike glycoprotein (6VXX), DNA (1BNA), integrins (4WK0, 3ZDX, 1JV2) and epidermal growth factor receptor tyrosine kinase (4HJO). Moreover, molecular dynamics (MD) simulations were performed to validate the stability of the EER, EPR and PRP tripeptides docked to SARS‐CoV‐2 main protease, MPro (6M03) and epidermal growth factor receptor tyrosine kinase (4HJO) within 100 ns time scale and ligand‐receptor interactions were evaluated. The metrics root‐mean‐square deviation, root‐mean‐square fluctuation, intermolecular hydrogen bonding, and radius of gyration revealed that the EER, EPR, and PRP tripeptides form energetically stable complexes with the target proteins. The binding free energies were calculated by the combination of Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and Molecular Mechanics/Poisson‐Boltzmann Surface Area (MM‐PBSA) methods (MM/PB(GB)SA). Principal Component Analysis on MD data was performed to evaluate the energy and structural information of the tripeptide‐protein complexes. Additionally, in‐silico structure‐based pharmacological predictions were made and the anticancer and antibacterial activities of the tripeptides were predicted.

CHARMM-GUI QM/MM Interfacer for a Quantum Mechanical and Molecular Mechanical (QM/MM) Simulation Setup: 1. Semiempirical Methods.

Quantum mechanical (QM) treatments, when combined with molecular mechanical (MM) force fields, can effectively handle enzyme-catalyzed reactions without significantly increasing the computational cost. In this context, we present CHARMM-GUI QM/MM Interfacer, a web-based cyberinfrastructure designed to streamline the preparation of various QM/MM simulation inputs with ligand modification. The development of QM/MM Interfacer has been achieved through integration with existing CHARMM-GUI modules, such as PDB Reader and Manipulator, Solution Builder, and Membrane Builder. In addition, new functionalities have been developed to facilitate the one-stop preparation of QM/MM systems and enable interactive and intuitive ligand modifications and QM atom selections. QM/MM Interfacer offers support for a range of semiempirical QM methods, including AM1(+/d), PM3(+/PDDG), MNDO(+/d, +/PDDG), PM6, RM1, and SCC-DFTB, tailored for both AMBER and CHARMM. A nontrivial setup related to ligand modification, link-atom insertion, and charge distribution is automatized through intuitive user interfaces. To illustrate the robustness of QM/MM Interfacer, we conducted QM/MM simulations of three enzyme-substrate systems: dihydrofolate reductase, insulin receptor kinase, and oligosaccharyltransferase. In addition, we have created three tutorial videos about building these systems, which can be found at https://www.charmm-gui.org/demo/qmi. QM/MM Interfacer is expected to be a valuable and accessible web-based tool that simplifies and accelerates the setup process for hybrid QM/MM simulations.

Reweighting from Molecular Mechanics Force Fields to the ANI-2x Neural Network Potential.

To achieve chemical accuracy in free energy calculations, it is necessary to accurately describe the system's potential energy surface and efficiently sample configurations from its Boltzmann distribution. While neural network potentials (NNPs) have shown significantly higher accuracy than classical molecular mechanics (MM) force fields, they have a limited range of applicability and are considerably slower than MM potentials, often by orders of magnitude. To address this challenge, Rufa et al. [Rufa et al. bioRxiv 2020, 10.1101/2020.07.29.227959.] suggested a two-stage approach that uses a fast and established MM alchemical energy protocol, followed by reweighting the results using NNPs, known as endstate correction or indirect free energy calculation. This study systematically investigates the accuracy and robustness of reweighting from an MM reference to a neural network target potential (ANI-2x) for an established data set in vacuum, using single-step free-energy perturbation (FEP) and nonequilibrium (NEQ) switching simulation. We assess the influence of longer switching lengths and the impact of slow degrees of freedom on outliers in the work distribution and compare the results to those of multistate equilibrium free energy simulations. Our results demonstrate that free energy calculations between NNPs and MM potentials should be preferably performed using NEQ switching simulations to obtain accurate free energy estimates. NEQ switching simulations between the MM potentials and NNPs are efficient, robust, and trivial to implement.

PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences.

The last few years have seen the development of numerous deep learning-based protein-ligand docking methods. They offer huge promise in terms of speed and accuracy. However, despite claims of state-of-the-art performance in terms of crystallographic root-mean-square deviation (RMSD), upon closer inspection, it has become apparent that they often produce physically implausible molecular structures. It is therefore not sufficient to evaluate these methods solely by RMSD to a native binding mode. It is vital, particularly for deep learning-based methods, that they are also evaluated on steric and energetic criteria. We present PoseBusters, a Python package that performs a series of standard quality checks using the well-established cheminformatics toolkit RDKit. The PoseBusters test suite validates chemical and geometric consistency of a ligand including its stereochemistry, and the physical plausibility of intra- and intermolecular measurements such as the planarity of aromatic rings, standard bond lengths, and protein-ligand clashes. Only methods that both pass these checks and predict native-like binding modes should be classed as having "state-of-the-art" performance. We use PoseBusters to compare five deep learning-based docking methods (DeepDock, DiffDock, EquiBind, TankBind, and Uni-Mol) and two well-established standard docking methods (AutoDock Vina and CCDC Gold) with and without an additional post-prediction energy minimisation step using a molecular mechanics force field. We show that both in terms of physical plausibility and the ability to generalise to examples that are distinct from the training data, no deep learning-based method yet outperforms classical docking tools. In addition, we find that molecular mechanics force fields contain docking-relevant physics missing from deep-learning methods. PoseBusters allows practitioners to assess docking and molecular generation methods and may inspire new inductive biases still required to improve deep learning-based methods, which will help drive the development of more accurate and more realistic predictions.

Machine-learned molecular mechanics force fields from large-scale quantum chemical data.

The development of reliable and extensible molecular mechanics (MM) force fields-fast, empirical models characterizing the potential energy surface of molecular systems-is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1 M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides and folded proteins, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest.

Development and Comprehensive Benchmark of a High-Quality AMBER-Consistent Small Molecule Force Field with Broad Chemical Space Coverage for Molecular Modeling and Free Energy Calculation.

Biomolecular simulations have become an essential tool in contemporary drug discovery, and molecular mechanics force fields (FFs) constitute its cornerstone. Developing a high quality and broad coverage general FF is a significant undertaking that requires substantial expert knowledge and computing resources, which is beyond the scope of general practitioners. Existing FFs originate from only a limited number of groups and organizations, and they either suffer from limited numbers of training sets, lower than desired quality because of oversimplified representations, or are costly for the molecular modeling community to access. To address these issues, in this work, we developed an AMBER-consistent small molecule FF with extensive chemical space coverage, and we provide Open Access parameters for the entire modeling community. To validate our FF, we carried out benchmarks of quantum mechanics (QM)/molecular mechanics conformer comparison and free energy perturbation calculations on several benchmark data sets. Our FF achieves a higher level of performance at reproducing QM energies and geometries than two popular open-source FFs, OpenFF2 and GAFF2. In relative binding free energy calculations for 31 protein-ligand data sets, comprising 1079 pairs of ligands, the new FF achieves an overall root-mean-square error of 1.19 kcal/mol for ΔΔG and 0.92 kcal/mol for ΔG on a subset of 463 ligands without bespoke fitting to the data sets. The results are on par with those of the leading commercial series of OPLS FFs.

Electron Spin Resonance Spectrum Simulations of Possible Radicals of the Ketoprofen Molecule

In this study, the molecular structure of Ketoprofen molecule, which is the drug active ingredient , was revealed by using the Density Functional Theory method and the Molecular Mechanical Force Field method. First of all, conformational space scanning in the Ketoprofen molecule was performed by the Molecular Mechanical Force Field method. The most stable structure of the ketoprofen molecule was found with the help of the Density Functional Theory method. The geometry parameters of the ketoprofen molecule and the Nuclear Magnetic Resonance parameters were calculated with the help of the Density Functional Theory method. Possible radicals were modeled using the most stable structure of the ketoprofen molecule. Electron Spin Resonance parameters of these possible radicals were calculated with the Density Functions Theory method. The calculated Electron Spin Resonance parameters were used in the JEOL IsoSimu/Fa Version 2.2.0 simulation program and theoretical Electron Spin Resonance spectra of possible radicals were obtained.

Simulating the Fluorescence of the Locally Excited State of DMABN in Solvents of Different Polarities.

4-(N,N-Dimethylamino)benzonitrile (DMABN) is a luminescent probe that can be used for tracking changes in the surrounding solvent due to the large change in polarity between its ground and excited states. An important characteristic of DMABN is that it exhibits dual fluorescence with two different emission energies that can be monitored, allowing for better characterization of the surrounding system. The first excited state is called the locally excited (LE) state and is characterized by the movement of charge over the conjugated ring structure. In nonpolar solvents and in the gas phase, the fluorescence of DMABN is entirely attributed to the transition from the near-planar LE state. In more polar environments, emission occurs from both the LE and a second excited state, corresponding to a twisted intramolecular charge-transfer (ICT) structure. For the sake of simplicity, this work considers transitions between only the ground and LE state. Molecular mechanical force field models of DMABN in its ground and LE states have been developed to investigate the sensitivity of the LE state to the polarity of the solvent. Both nonpolarizable and polarizable force fields were developed to simulate the molecule in a series of 10 different solvents of different polarities. The calculated Stokes shift of DMABN increases with increasing orientation polarizability of the surrounding solvent, which is the expected trend, as seen in experimental studies.

Simulating the Fluorescence of Di-8-ANEPPS in Solvents of Different Polarity.

Voltage-sensitive fluorescent probes, such as di-8-amino-naphthyl-ethylene-pyridinium-propyl-sulfonate (di-8-ANEPPS), are extremely useful for monitoring the membrane potential in biological and biophysical studies. However, because di-8-ANEPPS is very sensitive to its environment, it can be difficult to distinguish the degree to which a given external factor affects the observed fluorescence. Molecular dynamics simulations based on detailed atomic models make it possible to examine the particular characteristics of the system and predict the effects of the surroundings. Here, the sensitivity of the spectra of di-8-ANEPPS to solvent polarity is investigated by modeling the electronic transition between the ground and excited states using classical molecular mechanical force fields. The absorption and emission of di-8-ANEPPS were simulated in 12 solvents of increasing polarity using nonpolarizable and polarizable force fields. While the computational results and experimental data do not match perfectly, classical Lippert plots of both models show the expected increase of the Stokes shift of di-8-ANEPPS with the orientation polarizability of the surrounding solvent.

In silico pharmacological study of lacourtianal, a new terpenoid isolated from the stem bark of Chrysophyllum lacourtianum De Wild (Sapotaceae) against Alzheimer's disease

Urgent need to treat, prevent, delay the onset, slow the progression and reduce the symptoms of Alzheimer's disease (AD) is dictated by the growing number of people affected by the disease. This research requires an in silico approach. The aim of this work was to carry out an in silico pharmacological study of lacourtianal, a new terpenoid against Alzheimer's disease. The structure of lacourtianal and reference drugs were drawn on chemdraw 2D. Energy minimization was performed using the molecular mechanics force field (MM2) and saved in the PDB using chemdraw 3D. The compound's Smiles format was entered into the pk-CSM web servers for ADME/Tox parameter prediction. Conformational site analysis and docking parameters such as binding energy, interaction profiles with AD target residues (AchE, BuchE, β-secretase and GSK-3β) were determined using AutoDock 4.2 and Discovery Studio visualizer. Lacourtianal is a valuable oral drug candidate. It crosses the Blood-brain barrier (BBB) and shows considerable docking scores for AD targets. These scores were higher for BuchE and β-secretase compared with reference compounds. It binds to residues Phe297, Phe 338 and Tyr31, Trp286 in the acyl pocket and peripheral site of AchE, respectively. Importantly, it establishes low-energy interactions (hydrogen and pi) with the His438 residue of the Butyrylcholinesterase catalytic triad. It also establishes favorable interactions with residues Tyr71 and Tyr216, which are residues controlling substrate accessibility to the active site of the BACE1 and GSK 3β enzymes respectively. These results show that lacourtianal has promising therapeutic potential for the treatment of Alzheimer's disease.
 Keywords: Lacourtianal, in silico, AchE, BuchE, β-secretase, GSK-3β and AD

Boosting Graph Neural Networks with Molecular Mechanics: A Case Study of Sigma Profile Prediction.

Sigma profiles are quantum-chemistry-derived molecular descriptors that encode the polarity of molecules. They have shown great performance when used as a feature in machine learning applications. To accelerate the development of these models and the construction of large sigma profile databases, this work proposes a graph convolutional network (GCN) architecture to predict sigma profiles from molecule structures. To do so, the usage of molecular mechanics (force field atom types) is explored as a computationally inexpensive node-level featurization technique to encode the local and global chemical environments of atoms in molecules. The GCN models developed in this work accurately predict the sigma profiles of assorted organic and inorganic compounds. The best GCN model here reported, obtained using Merck molecular force field (MMFF) atom types, displayed training and testing set coefficients of determination of 0.98 and 0.96, respectively, which are superior to previous methodologies reported in the literature. This performance boost is shown to be due to both the usage of a convolutional architecture and node-level features based on force field atom types. Finally, to demonstrate their practical applicability, we used GCN-predicted sigma profiles as the input to machine learning models previously developed in the literature that predict boiling temperatures and aqueous solubilities. Using the predicted sigma profiles as input, these models were able to compute both physicochemical properties using significantly less computational resources and displayed only a slight decrease in performance when compared with sigma profiles obtained from quantum chemistry methods.

Adaptive-Partitioning Multilayer Dynamics Simulations: 2. Implementations of the Permuted and Interpolated Adaptive-Partitioning Gradients.

Recently, an adaptive-partitioning multilayer Q1/Q2/MM method was proposed, where Q1 and Q2 denote, respectively, two distinct quantum-mechanical levels of theory and MM, the molecular-mechanical force fields. Such a multilayer model resembles the ONIOM (our own N-layered integrated molecular orbital and molecular mechanics) model by Morokuma and co-workers, but it is distinguished by on-the-fly reclassifying atoms to be Q1, Q2, or MM in dynamics simulations. To smoothly blend the levels of descriptions of the atoms, buffer zones are introduced between adjacent layers, and the energy is smoothly interpolated. In particular, the Q1/Q2 interaction energy was expressed in two different formalisms: permuted and interpolated adaptive-partitioning (PAP and IAP), respectively. While the PAP energy is based on a weighted many-body expansion, the IAP energy is derived via alchemical quantum calculations with interpolated Fock and overlap matrices. In this article, we examine in-depth the irregularities in the IAP energy near the boundary between the buffer and Q2 zones, which were found prominent in some calculations. These irregularities are due to basis-set linear dependencies, which can be effectively suppressed using a cutoff for the weighted atomic orbital coefficients. Furthermore, we derived and implemented the gradients for both PAP and IAP. Test calculations on a series of water cluster models show perfectly smooth gradients in PAP, while a minor discontinuity occurs in IAP gradients at the buffer/Q2 boundary. The energy and gradient discontinuities in IAP become smaller when moving the buffer/Q2 boundary further away from the Q1 center and when increasing the size of the basis sets used. Overall, those discontinuities are controllable, and possible ways to further diminish them are discussed.

Simple, near-universal relationships between bond lengths, strengths, and anharmonicities

Harmonic bond force constants and bond lengths are shown to generally obey the simple relationships, ke=ζ2Re−3 (hydrides) and ke=10ζ1/2Re−4 (all other bond types), where ζ is the reduced nuclear charge and Re is the equilibrium bond length. Equally simple power-law relationships are found for higher-order bond force constants. Although not spectroscopically accurate, these models are nonetheless of significant heuristic value for identifying strongly multireference states of diatomic molecules (including electronically coupled excited states ill-suited for inclusion in laser-cooling schemes), rationalizing the observed trends in vibrational frequencies for diatomics and/or local mode oscillators within molecules or complexes and estimating and/or validating covalent bonding parameters within molecular mechanics force fields. Particular advantages of our approach over other bond length-strength scaling relationships proposed in the literature include its simplicity and generality and its appropriate asymptotic behavior. Notably, the relationships derived in this work can be used to predict harmonic and higher-order force constant bonds between any pair of atoms in the Periodic Table (including transition metals and lanthanides) without requiring row- or column-dependent parameterization, to accuracies commensurate with conventional force field transferability errors. We therefore anticipate that they will expedite force field development for metal-containing complexes and materials, which are structurally well-characterized but challenging to parameterize ab initio.

Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing.

Characterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure-function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue-specific deuterium labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB-disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Lastly, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.

Neural potentials of proteins extrapolate beyond training data.

We evaluate neural network (NN) coarse-grained (CG) force fields compared to traditional CG molecular mechanics force fields. We conclude that NN force fields are able to extrapolate and sample from unseen regions of the free energy surface when trained with limited data. Our results come from 88 NN force fields trained on different combinations of clustered free energy surfaces from four protein mapped trajectories. We used a statistical measure named total variation similarity to assess the agreement between reference free energy surfaces from mapped atomistic simulations and CG simulations from trained NN force fields. Our conclusions support the hypothesis that NN CG force fields trained with samples from one region of the proteins' free energy surface can, indeed, extrapolate to unseen regions. Additionally, the force matching error was found to only be weakly correlated with a force field's ability to reconstruct the correct free energy surface.

Development and application of fragment-based de novo inhibitor design approaches against Plasmodium falciparum GST.

Modulation of disease progression is frequently started by identifying biochemical pathway catalyzed by biomolecule that is prone to inhibition by small molecular weight ligands. Such ligands (leads) can be obtained from natural resources or synthetic libraries. However, de novo design based on fragments assembly and optimization is showing increasing success. Plasmodium falciparum parasite depends on glutathione-S-transferase (PfGST) in buffering oxidative heme as an approach to resist some antimalarials. Therefore, PfGST is considered an attractive target for drug development. In this research, fragment-based approaches were used to design molecules that can fit to glutathione (GSH) binding site (G-site) of PfGST. The involved approaches build molecules from fragments that are either isosteric to GSH sub-moieties (ligand-based) or successfully docked to GSH binding sub-pockets (structure-based). Compared to reference GST inhibitor of S-hexyl GSH, ligands with improved rigidity, synthetic accessibility, and affinity to receptor were successfully designed. The method involves joining fragments to create ligands. The ligands were thenexplored using molecular docking, Cartesian coordinate's optimization, and simplified free energy determination as well as MD simulation and MMPBSA calculations. Several tools were used which includeOPENEYE toolkit, Open Babel, Autodock Vina, Gromacs, and SwissParam server, and molecular mechanics force field of MMFF94 for optimization and CHARMM27 for MD simulation. In addition,in-house scripts written in Matlab were used to control fragments connectionand automation of the tools.