Improved Peptide and Protein Torsional Energetics with the OPLSAA Force Field

When Order Meets Disorder: Modeling and Function of the Protein Interface in Fuzzy Complexes

The degree of proteins structural organization ranges from highly structured, compact folding to intrinsic disorder, where each degree of self-organization corresponds to specific functions: well-organized structural motifs in enzymes offer a proper environment for precisely positioned functional groups to participate in catalytic reactions; at the other end of the self-organization spectrum, intrinsically disordered proteins act as binding hubs via the formation of multiple, transient and often non-specific interactions. This review focusses on cases where structurally organized proteins or domains associate with highly disordered protein chains, leading to the formation of interfaces with varying degrees of fuzziness. We present a review of the computational methods developed to provide us with information on such fuzzy interfaces, and how they integrate experimental information. The discussion focusses on two specific cases, microtubules and homologous recombination nucleoprotein filaments, where a network of intrinsically disordered tails exerts regulatory function in recruiting partner macromolecules, proteins or DNA and tuning the atomic level association. Notably, we show how computational approaches such as molecular dynamics simulations can bring new knowledge to help bridging the gap between experimental analysis, that mostly concerns ensemble properties, and the behavior of individual disordered protein chains that contribute to regulation functions.

Development and Benchmarking of Open Force Field v1.0.0-the Parsley Small-Molecule Force Field

We present a methodology for defining and optimizing a general force field for classical molecular simulations, and we describe its use to derive the Open Force Field 1.0.0 small-molecule force field, codenamed Parsley. Rather than using traditional atom typing, our approach is built on the SMIRKS-native Open Force Field (SMIRNOFF) parameter assignment formalism, which handles increases in the diversity and specificity of the force field definition without needlessly increasing the complexity of the specification. Parameters are optimized with the ForceBalance tool, based on reference quantum chemical data that include torsion potential energy profiles, optimized gas-phase structures, and vibrational frequencies. These quantum reference data are computed and are maintained with QCArchive, an open-source and freely available distributed computing and database software ecosystem. In this initial application of the method, we present essentially a full optimization of all valence parameters and report tests of the resulting force field against compounds and data types outside the training set. These tests show improvements in optimized geometries and conformational energetics and demonstrate that Parsley's accuracy for liquid properties is similar to that of other general force fields, as is accuracy on binding free energies. We find that this initial Parsley force field affords accuracy similar to that of other general force fields when used to calculate relative binding free energies spanning 199 protein-ligand systems. Additionally, the resulting infrastructure allows us to rapidly optimize an entirely new force field with minimal human intervention.

Hypervariability of accessible and inaccessible conformational space of proteins

Proteins undergo motions in a range of amplitudes, from domain motions to backbone rotations, leading to changes in (φ,ψ) torsion angles and small-scale bond vibrations and angle bending. Here, we study the extent of variations in (φ,ψ) values in proteins and the effects of bond geometry variations due to vibrational motions in a protein on the accessible, (steric clash-free) (φ,ψ) space. We perform 1-fs timestep unconstrained molecular dynamics simulations on super-high-resolution protein structures. Extent of variations in bond geometry during the simulation is within acceptable ranges of bond lengths and angles. However, the steric clash-free (φ,ψ) space continuously changes as seen in bond geometry-specific (φ,ψ) steric maps at the residue level during simulations. (φ,ψ) regions that have steric clash at one timepoint can become steric clash-free at a different timepoint through minor adjustments to backbone bond lengths and angles. Also instances of (φ,ψ) transitions from the left to right half of the (φ,ψ) map in consecutive snapshots of the trajectory are seen. Although the two quadrants are separated by a steric clash-prone region, corresponding to a high-energy barrier, height of this barrier is lowered by adjusting the bond geometry such that a bridging region of steric clash-free, low-energy (φ,ψ) values is formed. We demonstrate the idea of dynamically varying nature of acceptable and accessible (φ,ψ) steric space in proteins, which has implications for protein folding; proteins could sample (φ,ψ) space which is originally considered to be inaccessible, during folding, through minor adjustments to their backbone bond geometry.

FEPrepare: A Web-Based Tool for Automating the Setup of Relative Binding Free Energy Calculations

Relative binding free energy calculations in drug design are becoming a useful tool in facilitating lead binding affinity optimization in a cost- and time-efficient manner. However, they have been limited by technical challenges such as the manual creation of large numbers of input files to set up, run, and analyze free energy simulations. In this Application Note, we describe FEPrepare, a novel web-based tool, which automates the setup procedure for relative binding FEP calculations for the dual-topology scheme of NAMD, one of the major MD engines, using OPLS-AA force field topology and parameter files. FEPrepare provides the user with all necessary files needed to run a FEP/MD simulation with NAMD. FEPrepare can be accessed and used at https://feprepare.vi-seem.eu/.

Probing the Suitability of Different Ca2+ Parameters for Long Simulations of Diisopropyl Fluorophosphatase

Organophosphate hydrolases are promising as potential biotherapeutic agents to treat poisoning with pesticides or nerve gases. However, these enzymes often need to be further engineered in order to become useful in practice. One example of such enhancement is the alteration of enantioselectivity of diisopropyl fluorophosphatase (DFPase). Molecular modeling techniques offer a unique opportunity to address this task rationally by providing a physical description of the substrate-binding process. However, DFPase is a metalloenzyme, and correct modeling of metal cations is a challenging task generally coming with a tradeoff between simulation speed and accuracy. Here, we probe several molecular mechanical parameter combinations for their ability to empower long simulations needed to achieve a quantitative description of substrate binding. We demonstrate that a combination of the Amber19sb force field with the recently developed 12-6 Ca2+ models allows us to both correctly model DFPase and obtain new insights into the DFP binding process.

Design of carboxylated single-walled carbon nanotubes as highly efficient inhibitors against Aβ40 fibrillation based on the HyBER mechanism

Misfolding and the subsequent self-assembly of amyloid-β protein (Aβ) is very important in the occurrence of Alzheimer's disease (AD). Thus, inhibition of Aβ aggregation is currently an effective method to alleviate and treat AD. Herein, a carboxylated single-walled carbon nanotube (SWCNT-COOH) was rationally designed based on the hydrophobic binding-electrostatic repulsion (HyBER) mechanism. The inhibitory effect of SWCNT-COOH on Aβ fibrillogenesis was first studied. Based on the results of thioflavin T fluorescence and atomic force microscopy imaging assays, it was shown that SWCNT-COOH can not only effectively inhibit Aβ aggregation, but also depolymerize the mature fibrils of Aβ. In addition, its inhibitory action will be affected by the content of carboxyl groups. Moreover, the influence of SWCNT-COOH on cytotoxicity induced by Aβ was investigated by the MTT method. It was found that SWCNT-COOH can produce an anti-Aβ neuroprotective effect in vitro. Molecular dynamics simulations showed that SWCNT-COOH significantly destroyed the overall and internal structural stability of an Aβ40 trimer. Moreover, SWCNT-COOH interacted strongly with the N-terminal region, turn region and C-terminal region of the Aβ40 trimer via hydrogen bonds, salt bridges and π-π interactions, which triggered a large structural disturbance of the Aβ40 trimer, reduced the β-sheet content of the Aβ40 trimer and led to more disorder in these regions. All the above data not only reveal the suppressive effect of SWCNT-COOH on Aβ aggregation, but also reveal its inhibitory mechanism, which provides a useful clue to exploit anti-Aβ drugs in the future.

Oxazinethione Derivatives as a Precursor to Pyrazolone and Pyrimidine Derivatives: Synthesis, Biological Activities, Molecular Modeling, ADME, and Molecular Dynamics Studies

In this study, we used oxazinethione as a perfect precursor to synthesize new pyrimidine and pyrazole derivatives with potent biological activities. Biological activities were determined for all compounds against A. flavus, E. coli, S. aureus, and F. moniliform. Compounds 3, 4a-b, and 5 exhibited higher activities toward A. flavus, E. coli, S. aureus, and F. moniliform; this was indicated through the MIC (minimum inhibitory concentration). At the same time, anticancer activities were determined through four cell lines, Ovcar-3, Hela, MCF-7, and LCC-MMk. The results obtained indicated that compound 5 was the most potent compound for both cell lines. Molecular docking was studied by the MOE (molecular operating environment). The in silico ADME of compounds 2 and 5 showed good pharmacokinetic properties. The present research strengthens the applicability of these compounds as encouraging anticancer and antibacterial drugs. Moreover, JAGUAR module MD simulations were carried out at about 100 ns. In addition, spectroscopic studies were carried out to establish the reactions of the synthesized structure derivatives.

Potent Inhibition of Nicotinamide N-Methyltransferase by Alkene-Linked Bisubstrate Mimics Bearing Electron Deficient Aromatics

Nicotinamide N-methyltransferase (NNMT) methylates nicotinamide (vitamin B3) to generate 1-methylnicotinamide (MNA). NNMT overexpression has been linked to a variety of diseases, most prominently human cancers, indicating its potential as a therapeutic target. The development of small-molecule NNMT inhibitors has gained interest in recent years, with the most potent inhibitors sharing structural features based on elements of the nicotinamide substrate and the S-adenosyl-l-methionine (SAM) cofactor. We here report the development of new bisubstrate inhibitors that include electron-deficient aromatic groups to mimic the nicotinamide moiety. In addition, a trans-alkene linker was found to be optimal for connecting the substrate and cofactor mimics in these inhibitors. The most potent NNMT inhibitor identified exhibits an IC₅₀ value of 3.7 nM, placing it among the most active NNMT inhibitors reported to date. Complementary analytical techniques, modeling studies, and cell-based assays provide insights into the binding mode, affinity, and selectivity of these inhibitors.

The Structural and Dynamical Properties of the Hydration of SNase Based on a Molecular Dynamics Simulation

The dynamics of protein–water fluctuations are of biological significance. Molecular dynamics simulations were performed in order to explore the hydration dynamics of staphylococcal nuclease (SNase) at different temperatures and mutation levels. A dynamical transition in hydration water (at ~210 K) can trigger larger-amplitude fluctuations of protein. The protein–water hydrogen bonds lost about 40% in the total change from 150 K to 210 K, while the Mean Square Displacement increased by little. The protein was activated when the hydration water in local had a comparable trend in making hydrogen bonds with protein– and other waters. The mutations changed the local chemical properties and the hydration exhibited a biphasic distribution, with two time scales. Hydrogen bonding relaxation governed the local protein fluctuations on the picosecond time scale, with the fastest time (24.9 ps) at the hydrophobic site and slowest time (40.4 ps) in the charged environment. The protein dynamic was related to the water’s translational diffusion via the relaxation of the protein–water’s H-bonding. The structural and dynamical properties of protein–water at the molecular level are fundamental to the physiological and functional mechanisms of SNase.

A Scalable Graph Neural Network Method for Developing an Accurate Force Field of Large Flexible Organic Molecules

An accurate force field is the key to the success of all molecular mechanics simulations on organic polymers and biomolecules. Accurate correlated wave function (CW) methods scale poorly with system size, so this poses a great challenge to the development of an extendible ab initio force field for large flexible organic molecules at the CW level of accuracy. In this work, we combine the physics-driven nonbonding potential with a data-driven subgraph neural network bonding model (named sGNN). Tests on polyethylene glycol, polyethene, and their block polymers show that our strategy is highly accurate and robust for molecules of different sizes and chemical compositions. Therefore, one can develop a parameter library of small molecular fragments (with sizes easily accessible to CW methods) and assemble them to predict the energy of large polymers, thus opening a new path to next-generation organic force fields.

Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks

Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce the training data. However, NN predictions are only reliable within well-learned training domains, and show volatile behavior when extrapolating. Uncertainty quantification methods can flag atomic configurations for which prediction confidence is low, but arriving at such uncertain regions requires expensive sampling of the NN phase space, often using atomistic simulations. Here, we exploit automatic differentiation to drive atomistic systems towards high-likelihood, high-uncertainty configurations without the need for molecular dynamics simulations. By performing adversarial attacks on an uncertainty metric, informative geometries that expand the training domain of NNs are sampled. When combined with an active learning loop, this approach bootstraps and improves NN potentials while decreasing the number of calls to the ground truth method. This efficiency is demonstrated on sampling of kinetic barriers, collective variables in molecules, and supramolecular chemistry in zeolite-molecule interactions, and can be extended to any NN potential architecture and materials system.

Design of next-generation therapeutic IgG4 with improved manufacturability and bioanalytical characteristics

Manufacturability of immunoglobulin G4 (IgG4) antibodies from the Chemistry, Manufacture, and Controls (CMC) perspective has received little attention during early drug discovery. Despite the success of protein engineering in improving antibody biophysical properties, a clear gap still exists between rational design of IgG4 candidates and their manufacturing suitability. Here, we illustrate that undesirable two-peak elution profiles in cation-exchange chromatography are attributed to the S228P mutation (in IgG4 core-hinge region) intentionally designed to prevent Fab-arm exchange. A new scaffolding platform for engineering IgG4 antibodies amenable to bioprocessing and bioanalysis is proposed by introducing an “IgG1-like” single-point mutation in the hinge or CH1 region of IgG4S228P. This work offers insight into the design, discovery, and development of innovative therapeutic antibodies that are well suited for robust biomanufacturing and quality control.

Optimization of Triarylpyridinone Inhibitors of the Main Protease of SARS-CoV-2 to Low-Nanomolar Antiviral Potency

Non-covalent inhibitors of the main protease (Mpro) of SARS-CoV-2 having a pyridinone core were previously reported with IC50 values as low as 0.018 μM for inhibition of enzymatic activity and EC50 values as low as 0.8 μM for inhibition of viral replication in Vero E6 cells. The series has now been further advanced by consideration of placement of substituted five-membered-ring heterocycles in the S4 pocket of Mpro and N-methylation of a uracil ring. Free energy perturbation calculations provided guidance on the choice of the heterocycles, and protein crystallography confirmed the desired S4 placement. Here we report inhibitors with EC50 values as low as 0.080 μM, while remdesivir yields values of 0.5-2 μM in side-by-side testing with infectious SARS-CoV-2. A key factor in the improvement is enhanced cell permeability, as reflected in PAMPA measurements. Compounds 19 and 21 are particularly promising as potential therapies for COVID-19, featuring IC50 values of 0.044-0.061 μM, EC50 values of ca. 0.1 μM, good aqueous solubility, and no cytotoxicity.

Preparing and Analyzing Polarizable Molecular Dynamics Simulations with the Classical Drude Oscillator Model

Molecular dynamics (MD) simulations performed with force fields that include explicit electronic polarization are becoming more prevalent in the field. The increasing emergence of these simulations is a result of continual refinement against a range of theoretical and empirical target data, optimization of software algorithms for higher performance, and availability of graphical processing unit hardware to further accelerate the simulations. Polarizable MD simulations are likely to be most impactful in biomolecular systems in which heterogeneous environments or unique microenvironments exist that would lead to inaccuracies in simulations performed with fixed-charge, nonpolarizable force fields. The further adoption of polarizable MD simulations will benefit from tutorial material that specifically addresses preparing and analyzing their unique features. In this chapter, we introduce common protocols for preparing routine biomolecular systems containing proteins, including both a globular protein in aqueous solvent and a transmembrane model peptide in a phospholipid bilayer. Details and example input files are provided for preparation of the simulation system using CHARMM, performing the simulations with OpenMM, and analyzing interesting dipole moment properties in CHARMM.

Comparison and Validation of Force Fields for Deep Eutectic Solvents in Combination with Water and Alcohol Dehydrogenase

Deep eutectic solvents (DESs) have become popular as environmental-friendly solvents for biocatalysis. Molecular dynamics (MD) simulations offer an in-depth analysis of enzymes in DESs, but their performance depends on the force field chosen. Here, we present a comprehensive validation of three biomolecular force fields (CHARMM, Amber, and OPLS) for simulations of alcohol dehydrogenase (ADH) in DESs composed of choline chloride and glycerol/ethylene glycol with varying water contents. Different properties (e.g., protein structure and flexibility, solvation layer, and H-bonds) were used for validation. For two properties (viscosity and water activity) also experiments were performed. The viscosity was calculated with the periodic perturbation method, whereby its parameter dependency is disclosed. A modification of Amber was identified as the best-performing model for low water contents, whereas CHARMM outperforms the other models at larger water concentrations. An analysis of ADH's structure and interactions with the DESs revealed similar predictions for Amber and CHARMM.

In Vitro and In Silico Investigation of Water-Soluble Fullerenol C60(OH)24: Bioactivity and Biocompatibility

Light fullerenes, C₆₀ and C₇₀, have significant potential in biomedical applications due to their ability to absorb reactive oxygen species, inhibit the development of tumors, inactivate viruses and bacteria, and as the basis for developing systems for targeted drug delivery. However, the hydrophobicity of individual fullerenes complicates their practical use; therefore, creating water-soluble derivatives of fullerenes is increasingly important. Currently, the most studied soluble adducts of fullerenes are polyhydroxy fullerenes or fullerenols. Unfortunately, investigations of fullerenol biocompatibility are fragmental. They often lack reproducibility both in the synthesis of the compounds and their biological action. We here investigate the biocompatibility of a well-defined fullerenol C₆₀(OH)₂₄ obtained using methods that minimize the content of impurities and quantitatively characterize the product's composition. We carry out comprehensive biochemical and biophysical investigations of C₆₀(OH)₂₄ that include photodynamic properties, cyto- and genotoxicity, hemocompatibility (spontaneous and photo-induced hemolysis, platelet aggregation), and the thermodynamic characteristics of C₆₀(OH)₂₄ binding to human serum albumin and DNA. The performed studies show good biocompatibility of fullerenol C₆₀(OH)₂₄, which makes it a promising object for potential use in biomedicine.

The G protein-first activation mechanism of opioid receptors by Gi protein and agonists

We report the G protein-first mechanism for activation of G protein-coupled receptors (GPCR) for the three closest subtypes of the opioid receptors (OR), μOR, κOR and δOR. We find that they couple to the inactive Gi protein-bound guanosine diphosphate (GDP) prior to agonist binding. The inactive Gi protein forms anchors to the intracellular loops of the inactive apo-μOR, apo-κOR and apo-δOR, inducing opening of the cytoplasmic region to form a pre-activated state that holds Gi protein in place until agonist binds. Then, agonist binds to μOR, κOR and δOR already complexed with Gi protein, to trigger the Gαi to open up the tightly coupled GDP binding site, making GDP accessible for GTP exchange, an essential step for Gi signalling. We show that the agonist alone cannot open the intracellular region of μOR and κOR, requiring Gi protein to open the cytoplasmic region by itself. We consider that this G protein-first mechanism may apply to activation of other Class A GPCRs. However, for δOR, agonist binding can open up the intracellular region to encourage Gi protein recruitment. Thus, activation of Gi protein mediated by δOR favourably may proceed with either ligand-first or G protein-first activation mechanisms.

Understanding Gas Solubility of Pure Component and Binary Mixtures within Multivalent Ionic Liquids from Molecular Simulations

Understanding the molecular-level solubility of CO₂ and its mixtures is essential to the progress of gas-treating technologies. Herein, we use grand canonical Monte Carlo simulations to study the single-component gas absorption of SO₂, N₂, CH₄, and H₂ and binary mixtures of CO₂/SO₂, CO₂/N₂, CO₂/CH₄, and CO₂/H₂ of varying mole fractions within multivalent ionic liquids (ILs). Our results highlight the importance of the free volume effect and the anion effect when interpreting the absorption behavior of these mixtures, similar to the behavior of CO₂ found in our previous study (Phys. Chem. Chem. Phys. 2020, 22, 20618-20633). The deviation of gas solubility between the pure component absorption versus the binary absorption, as well as the solubility selectivity, highlights the importance of the relative affinity of gas species within a mixture to the different anions. The absorption selectivity within a specific IL system can be predicted based on the relative gas affinity to the anion.

Cancer-Associated Mutations Perturb the Disordered Ensemble and Interactions of the Intrinsically Disordered p53 Transactivation Domain

Intrinsically disordered proteins (IDPs) are key components of regulatory networks that control crucial aspects of cell decision making. The intrinsically disordered transactivation domain (TAD) of tumor suppressor p53 mediates its interactions with multiple regulatory pathways to control the p53 homeostasis during the cellular response to genotoxic stress. Many cancer-associated mutations have been discovered in p53-TAD, but their structural and functional consequences are poorly understood. Here, by combining atomistic simulations, NMR spectroscopy, and binding assays, we demonstrate that cancer-associated mutations can significantly perturb the balance of p53 interactions with key activation and degradation regulators. Importantly, the four mutations studied in this work do not all directly disrupt the known interaction interfaces. Instead, at least three of these mutations likely modulate the disordered state of p53-TAD to perturb its interactions with regulators. Specifically, NMR and simulation analysis together suggest that these mutations can modulate the level of conformational expansion as well as rigidity of the disordered state. Our work suggests that the disordered conformational ensemble of p53-TAD can serve as a central conduit in regulating the response to various cellular stimuli at the protein-protein interaction level. Understanding how the disordered state of IDPs may be modulated by regulatory signals and/or disease associated perturbations will be essential in the studies on the role of IDPs in biology and diseases.

Unimolecular Dissociation of C6H6-C6Cl6 Complex and Effect of Mode-Mode Coupling

The unimolecular dissociation dynamics of the C₆H₆-C₆Cl₆ (Bz-HCB) complex is studied with initial excitation of all vibrational modes for a temperature range of 1000-2000 K and with mode-specific excitations at 1500 K. The results are compared with those of the C₆H₆-C₆F₆ [Bz- HFB] complex. When all modes of Bz-HCB are initially excited, the rate of dissociation is slower with respect to Bz-HFB. However, the rate of dissociation is faster when simulations with nonrandom excitation of the specific vibrational modes are performed. The rate of dissociation of Bz-HCB is found to become slower when a few intramolecular modes are excited along with all inter-fragment modes compared to the simulation when only inter-fragment modes of the same complex are excited. Such an energy-transfer dynamics is absent if both intramolecular and inter-fragment modes are not initially excited. Thus, a "stimulated" resonance energy-transfer dynamics is observed in Bz-HCB dissociation dynamics.

PyAutoFEP: An Automated Free Energy Perturbation Workflow for GROMACS Integrating Enhanced Sampling Methods

Free energy perturbation (FEP) calculations are now routinely used in drug discovery to estimate the relative FEB (RFEB) of small molecules to a biomolecular target of interest. Using enhanced sampling can improve the correlation between predictions and experimental data, especially in systems with conformational changes. Due to the large number of perturbations required in drug discovery campaigns, the manual setup of FEP calculations is no longer viable. Here, we introduce PyAutoFEP, a flexible and open-source tool to aid the setup of RFEB FEP. PyAutoFEP is written in Python3, and automates the generation of perturbation maps, dual topologies, system building and molecular dynamics (MD), and analysis. PyAutoFEP supports multiple force fields, incorporates replica exchange with solute tempering (REST) and replica exchange with solute scaling (REST2) enhanced sampling methods, and allows flexible λ values along perturbation windows. To validate PyAutoFEP, it was applied to a set of 14 Farnesoid X receptor ligands, a system included in the drug design data resource grand challenge 2. An 88% mean correct sign prediction was achieved, and 75% of the predictions had an error below 1.5 kcal/mol. Results using Amber03/GAFF, CHARMM36m/CGenFF, and OPLS-AA/M/LigParGen had Pearson's r values of 0.71 ± 0.13, 0.30 ± 0.27, and 0.66 ± 0.20, respectively. The Amber03/GAFF and OPLS-AA/M/LigParGen results were on par with the top grand challenge 2 submissions. Applying REST2 improved the results using CHARMM36m/CGenFF (Pearson's r = 0.43 ± 0.21) but had little impact on the other force fields. CHARMM36-YF and CHARMM36-WYF modifications did not yield improved predictions compared to CHARMM36m. Finally, we estimated the probability of finding a molecule 1 pK_i better than a lead when using PyAutoFEP to screen 10 or 100 analogues. The probabilities, when compared to random sampling, increased up to sevenfold when 100 molecules were to be screened, suggesting that PyAutoFEP would likely be useful for lead optimization. PyAutoFEP is available on GitHub at https://github.com/lmmpf/PyAutoFEP.

Accuracy of Alternate Nonpolarizable Force Fields for the Determination of Protein-Ligand Binding Affinities Dominated by Cation-π Interactions

Modifying pair-specific Lennard-Jones parameters through the nonbonded FIX (NBFIX) feature of the CHARMM36 force field has proven cost-effective for improving the description of cation-π interactions in biological objects by means of pairwise additive potential energy functions. Here, two sets of newly optimized CHARMM36 force-field parameters including NBFIX corrections, coined CHARMM36m-NBF and CHARMM36-WYF, and the original force fields, namely CHARMM36m and Amber ff14SB, are used to determine the standard binding free energies of seven protein-ligand complexes containing cation-π interactions. Compared with precise experimental measurements, our results indicate that the uncorrected, original force fields significantly underestimate the binding free energies, with a mean error of 5.3 kcal/mol, while the mean errors of CHARMM36m-NBF and CHARMM36-WYF amount to 0.8 and 2.1 kcal/mol, respectively. The present study cogently demonstrates that the use of modified parameters jointly with NBFIX corrections dramatically increases the accuracy of the standard binding free energy of protein-ligand complexes dominated by cation-π interactions, most notably with CHARMM36m-NBF.

Q-Force: Quantum Mechanically Augmented Molecular Force Fields

The quality of molecular dynamics simulations strongly depends on the accuracy of the underlying force fields (FFs) that determine all intra- and intermolecular interactions of the system. Commonly, transferable FF parameters are determined based on a representative set of small molecules. However, such an approach sacrifices accuracy in favor of generality. In this work, an open-source and automated toolkit named Q-Force is presented, which augments these transferable FFs with molecule-specific bonded parameters and atomic charges that are derived from quantum mechanical (QM) calculations. The molecular fragmentation procedure allows treatment of large molecules (>200 atoms) with a low computational cost. The generated Q-Force FFs can be used at the same computational cost as transferable FFs, but with improved accuracy: We demonstrate this for the vibrational properties on a set of small molecules and for the potential energy surface on a complex molecule (186 atoms) with photovoltaic applications. Overall, the accuracy, user-friendliness, and minimal computational overhead of the Q-Force protocol make it widely applicable for atomistic molecular dynamics simulations.

Design, synthesis, HER2 inhibition and anticancer evaluation of new substituted 1,5-dihydro-4,1-benzoxazepines

A series of 11 new substituted 1,5-dihydro-4,1-benzoxazepine derivatives was synthesised to study the influence of the methyl group in the 1-(benzenesulphonyl) moiety, the replacement of the purine by the benzotriazole bioisosteric analogue, and the introduction of a bulky substituent at position 6 of the purine, on the biological effects. Their inhibition against isolated HER2 was studied and the structure–activity relationships have been confirmed by molecular modelling studies. The most potent compound against isolated HER2 is 9a with an IC₅₀ of 7.31 µM. We have investigated the effects of the target compounds on cell proliferation. The most active compound (7c) against all the tumour cell lines studied (IC₅₀ 0.42–0.86 µM) does not produce any modification in the expression of pro-caspase 3, but increases the caspase 1 expression, and promotes pyroptosis.

Identification of V6.51L as a selectivity hotspot in stereoselective A2B adenosine receptor antagonist recognition

The four adenosine receptors (ARs) A₁AR, A_2AAR, A_2BAR_, and A₃AR are G protein-coupled receptors (GPCRs) for which an exceptional amount of experimental and structural data is available. Still, limited success has been achieved in getting new chemical modulators on the market. As such, there is a clear interest in the design of novel selective chemical entities for this family of receptors. In this work, we investigate the selective recognition of ISAM-140, a recently reported A_2BAR reference antagonist. A combination of semipreparative chiral HPLC, circular dichroism and X-ray crystallography was used to separate and unequivocally assign the configuration of each enantiomer. Subsequently affinity evaluation for both A_2A and A_2B receptors demonstrate the stereospecific and selective recognition of (S)-ISAM140 to the A_2BAR. The molecular modeling suggested that the structural determinants of this selectivity profile would be residue V250^6.51 in A_2BAR, which is a leucine in all other ARs including the closely related A_2AAR. This was herein confirmed by radioligand binding assays and rigorous free energy perturbation (FEP) calculations performed on the L249V^6.51 mutant A_2AAR receptor. Taken together, this study provides further insights in the binding mode of these A_2BAR antagonists, paving the way for future ligand optimization.

Lysozyme and Human Serum Albumin Proteins as Potential Nitric Oxide Cardiovascular Drug Carriers: Theoretical and Experimental Investigation

Nitric oxide-containing drugs present a critical remedy for cardiovascular diseases. Nitroglycerin (NG, O-NO) and S-nitrosoglutathione (SNG, S-NO) are the most common nitric oxide drugs for cardiovascular diseases. Insights regarding the binding affinity of NO drugs with lysozyme and human serum albumin (HSA) proteins and their dissociation mechanism will provide inquisitive information regarding the potential of the proteins as drug carriers. For the first time, the binding interactions and affinities are investigated using molecular docking, conventional molecular dynamics, steered molecular dynamics, and umbrella sampling to explore the ability of both proteins to act as nitric oxide drug carriers. The molecular dynamics simulation results showed higher stability of lysozyme-drug complexes compared to HSA. For lysozyme, cardiovascular drugs were bound in the protein cavity mainly by the electrostatic and hydrogen bond interactions with residues ASP53, GLN58, ILE59, ARG62, TRP64, ASP102, and TRP109. For HSA, key binding residues were ARG410, TYR411, LYS414, ARG485, GLU450, ARG486, and SER489. The free energy profiles produced from umbrella sampling also suggest that lysozyme-drug complexes had better binding affinity than HSA-drug. Binding characteristics of nitric oxide-containing drugs NG and SNG to lysozyme and HSA proteins were studied using fluorescence and UV-vis absorption spectroscopy. The relative change in the fluorescence intensity as a function of drug concentrations was analyzed using Stern-Volmer calculations. This was also confirmed by the change in the UV-vis spectra. Fluorescence quenching results of both proteins with the drugs, based on the binding constant values, demonstrated significantly weak binding affinity to NG and strong binding affinity to SNG. Both computational and experimental studies provided important data for understanding protein-drug interactions and will aid in developing potential drug carrier systems in cardiovascular diseases.

Immunoproteasome and Non-Covalent Inhibition: Exploration by Advanced Molecular Dynamics and Docking Methods

The selective inhibition of immunoproteasome is a valuable strategy to treat autoimmune, inflammatory diseases, and hematologic malignancies. Recently, a new series of amide derivatives as non-covalent inhibitors of the β1i subunit with K_i values in the low/submicromolar ranges have been identified. Here, we investigated the binding mechanism of the most potent and selective inhibitor, N-benzyl-2-(2-oxopyridin-1(2H)-yl)propanamide (1), to elucidate the steps from the ligand entrance into the binding pocket to the ligand-induced conformational changes. We carried out a total of 400 ns of MD-binding analyses, followed by 200 ns of plain MD. The trajectories clustering allowed identifying three representative poses evidencing new key interactions with Phe31 and Lys33 together in a flipped orientation of a representative pose. Further, Binding Pose MetaDynamics (BPMD) studies were performed to evaluate the binding stability, comparing 1 with four other inhibitors of the β1i subunit: N-benzyl-2-(2-oxopyridin-1(2H)-yl)acetamide (2), N-cyclohexyl-3-(2-oxopyridin-1(2H)-yl)propenamide (3), N-butyl-3-(2-oxopyridin-1(2H)-yl)propanamide (4), and (S)-2-(2-oxopyridin-1(2H)-yl)-N,4-diphenylbutanamide (5). The obtained results in terms of free binding energy were consistent with the experimental values of inhibition, confirming 1 as a lead compound of this series. The adopted methods provided a full dynamic description of the binding events, and the information obtained could be exploited for the rational design of new and more active inhibitors.

Soluble State of Villin Headpiece Protein as a Tool in the Assessment of MD Force Fields

Protein self-assembly plays an important role in cellular processes. Whereas molecular dynamics (MD) represents a powerful tool in studying assembly mechanisms, its predictions depend on the accuracy of underlying force fields, which are known to overly promote protein assembly. We here examine villin headpiece domain, HP36, which remains soluble at concentrations amenable to MD studies. The experimental characterization of soluble HP36 at concentrations of 0.05 to 1 mM reveals concentration-independent 90% monomeric and 10% dimeric populations. Extensive all-atom MD simulations at two protein concentrations, 0.9 and 8.5 mM, probe the HP36 dimer population, stability, and kinetics of dimer formation within two MD force fields, Amber ff14SB and CHARMM36m. MD results demonstrate that whereas CHARMM36m captures experimental HP36 monomer populations at the lower concentration, both force fields overly promote HP36 association at the higher concentration. Moreover, contacts stabilizing HP36 dimers are force-field-dependent. CHARMM36m produces consistently higher HP36 monomer populations, lower association rates, and weaker dependence of these quantities on the protein concentration than Amber ff14SB. Nonetheless, the highest monomer populations and dissociation constants are observed when the TIP3P water model in Amber ff14SB is replaced by TIP4P/2005, showcasing the critical role of the water model in addressing the protein solubility problem in MD.

Force Field Benchmark of Amino Acids. 3. Hydration with Scaled Lennard-Jones Interactions

Classical protein force fields were reported with too weak protein-water interactions relative to protein-protein interactions, leading to more compact structures and artificial protein aggregation. Here we investigated the impacts of scaled Lennard-Jones (LJ) interactions on the hydration of amino acids and the simulation of folded and intrinsically disordered proteins (IDPs). The obtained optimal scaling parameters reproduce accurately hydration free energies of neutral amino acid side chain analogues and do not affect the compactness and structural stability of folded proteins significantly. The scaling leads to less compact IDPs and varies from case to case. Strengthening the interactions between protein and water oxygen or hydrogen atoms by increasing the interacting LJ well depth (ε) appears more effective than weakening protein-protein interactions by reducing the interacting dispersion coefficients (C₆). We demonstrate that weakening water-water interactions is a solution as well to obtaining more favorable protein-water interactions in an indirect way, although modern force fields like Amber ff19SB and a99SB-disp tend to use water models with strong water-water interactions. This is likely a compromise between strong protein-protein interactions and strong water-water interactions. Independent optimization of protein force fields and water models is therefore needed to make both interactions more close to reality, leading to good accuracy without bias or scaling.

Computational approach, scanning electron and fluorescence microscopies revealed insights into the action mechanisms of anticandidal peptide Mo-CBP3-PepIII

AIMS

The Candida genus is composed of opportunistic pathogens that threaten public health. Given the increase in resistance to current drugs, it is necessary to develop new drugs to treat infections by these pathogens. Antimicrobial peptides are promising alternative molecules with low cost, broad action spectrum and low resistance induction. This study aimed to clarify the action mechanisms of synthetic peptides against Candida albicans.

MAIN METHODS

The mode of action of the anticandidal peptides Mo-CBP₃-PepIII were analyzed through molecular dynamics and quantum biochemistry methods against Exo-β-1,3-glucanase (EXG), vital to cell wall metabolism. Furthermore, scanning electron (SEM) and fluorescence (FM) microscopies were employed to corroborate the in silico data.

KEY FINDINGS

Mo-CBP₃-PepIII strongly interacted with EXG (-122.2 kcal mol^-1) at the active site, higher than the commercial inhibitor pepstatin. Also, molecular dynamics revealed the insertion of Mo-CBP₃-PepIII into the yeast membrane. SEM analyses revealed that Mo-CBP₃-PepIII induced cracks and scars of the cell wall and FM analyses confirmed the pore formation on the Candida membrane.

SIGNIFICANCE

Mo-CBP₃-PepIII has strong potential as a new drug with a broad spectrum of action, given its different mode of action compared to conventional drugs.

Normal mode calculation and infrared spectroscopy of proteins in water solution: Relationship between amide I transition dipole strength and secondary structure

Dipole Strength (DS) of the amides has gained a renewed interest in chemical physics since it provides an important tool to disclose the on-site vibrational energy distributions. Apart from earlier experimental efforts on polypeptides, little is still known about DS in complex proteins. We accurately measured the Fourier Transform Infrared absorption spectra of nine proteins in water solution obtaining their Molar Extinction Coefficient in the amide I and II spectral region. Our results show that the amide I DS value depends on the protein secondary structure, being that of the α-rich and unstructured proteins lower by a factor of 2 than that of the β-rich proteins. The average DS for amino acids in α and β secondary structures confirms this finding. Normal Mode calculation and Molecular Dynamics were performed and used as tools for data analysis and interpretation. The present outcomes corroborate the hypothesis that antiparallel β-sheet environment is more prone to delocalize the on-site CO stretching vibration through coupling mechanisms between carbonyl groups, whereas α-helix structures are energetically less stable to permit vibrational mode delocalization.

Ultra-strong bio-glue from genetically engineered polypeptides

The development of biomedical glues is an important, yet challenging task as seemingly mutually exclusive properties need to be combined in one material, i.e. strong adhesion and adaption to remodeling processes in healing tissue. Here, we report a biocompatible and biodegradable protein-based adhesive with high adhesion strengths. The maximum strength reaches 16.5 ± 2.2 MPa on hard substrates, which is comparable to that of commercial cyanoacrylate superglue and higher than other protein-based adhesives by at least one order of magnitude. Moreover, the strong adhesion on soft tissues qualifies the adhesive as biomedical glue outperforming some commercial products. Robust mechanical properties are realized without covalent bond formation during the adhesion process. A complex consisting of cationic supercharged polypeptides and anionic aromatic surfactants with lysine to surfactant molar ratio of 1:0.9 is driven by multiple supramolecular interactions enabling such strong adhesion. We demonstrate the glue's robust performance in vitro and in vivo for cosmetic and hemostasis applications and accelerated wound healing by comparison to surgical wound closures.

Generalizing the Discrete Gibbs Sampler-Based λ-Dynamics Approach for Multisite Sampling of Many Ligands

In this work, the discrete λ variant of the Gibbs sampler-based λ-dynamics (d-GSλD) method is developed to enable multiple functional group perturbations to be investigated at one or more sites of substitution off a common ligand core. The theoretical framework and special considerations for constructing discrete λ states for multisite d-GSλD are presented. The precision and accuracy of the d-GSλD method is evaluated with three test cases of increasing complexity. Specifically, methyl → methyl symmetric perturbations in water, 1,4-benzene hydration free energies and protein-ligand binding affinities for an example HIV-1 reverse transcriptase inhibitor series are computed with d-GSλD. Complementary MSλD calculations were also performed to compare with d-GSλD's performance. Excellent agreement between d-GSλD and MSλD is observed, with mean unsigned errors of 0.12 and 0.22 kcal/mol for computed hydration and binding free energy test cases, respectively. Good agreement with experiment is also observed, with errors of 0.5-0.7 kcal/mol. These findings support the applicability of the d-GSλD free energy method for a variety of molecular design problems, including structure-based drug design. Finally, a discussion of d-GSλD versus MSλD approaches is presented to compare and contrast features of both methods.

Developing and Assessing Nonbonded Dummy Models of Magnesium Ion with Different Hydration Free Energy References

A large diversity in the targeted hydration free energies (HFEs) during model parameterization of metal ions was reported in the literature with a difference by dozens of kcal/mol. Here, we developed a series of nonbonded dummy models of the Mg²⁺ ion targeting different HFE references in TIP3P water, followed by assessments of the designed models in the simulations of MgCl₂ solution and biological systems. Together with the comparison of existing models, we conclude that the difference in the targeted HFEs has a limited influence on the model performance, while the usability of these models differs from case to case. The feasibility of reproducing more properties of Mg²⁺ such as diffusion constants and water exchange rates using a nonbonded dummy model is demonstrated. Underestimated activity derivative and osmotic coefficient of MgCl₂ solutions in high concentration reveal a necessity for further optimization of ion-pair interactions. The developed dummy models are applicable to metal coordination with Asp, Glu, and His residues in metalloenzymes, and the performance in predicting monodentate or bidentate binding modes of Asp/Glu residues depends on the complexity of metal centers and the choice of protein force fields. When both the binding modes coexist, the nonbonded dummy models outperform point charge models, probably in need of considering polarization of metal-binding residues by, for instance, charge calibration in classical force fields. This work is valuable for the use and further development of magnesium ion models for simulations of metal-containing systems with good accuracy.

Mining the Mycobacterium tuberculosis proteome for identification of potential T-cell epitope based vaccine candidates

Identification of protective antigens for designing a high-efficacy tuberculosis vaccine is the need of the hour. Till date only 7% of the Mycobacterium tuberculosis proteome has been explored for discovering antigens capable of activating T-cell responses. Therefore, it becomes crucial to screen the remaining Mycobacterium tuberculosis proteome for more immunodominant T-cell epitopes. An extensive knowledge of the epitopes recognized by our immune system can aid this process of finding potential T cell antigens for development of a better TB vaccine. In the present in-silico study, 237 proteins belonging to the 'virulence, detoxification, and adaptation' category of Mycobacterium tuberculosis proteome were targeted for T-cell epitope screening. 50825 MHC Class I and 49357 MHC Class II epitopes were generated using NetMHC3.4 and IEDB servers respectively and tested for their antigenicity and cytokine stimulation. The highest antigenic epitopes were analyzed for their world population coverage and epitope conservancy. Molecular docking and molecular dynamics simulation studies were performed to corroborate the binding affinities and structural stability of the peptide-MHC complexes. We predicted a total of 3 MHC Class I (ILLKMCWPA, FAVGMNVYV, and SLAGNSAKV) and 7 MHC Class II (DLTIGFFLHIPFPPV, RPDLTIGFFLHIPFP, LTIGFFLHIPFPPVE, VLVFALVVALVYLQF, LVFALVVALVYLQFR, PNLVAARFIQLTPVY, and LVLVFALVVALVYLQ) epitopes that can be promising vaccine candidates. These predicted epitopes belong to 6 distinct proteins: Rv0169 (mce1a), Rv3490 (ostA), Rv3496 (mce4D), Rv1085c, Rv0563 (HtpX), Rv3497c (mce4C). All these proteins are expressed at different stages in the life cycle of Mycobacterium tuberculosis and thus, the predicted epitopes could be employed as candidates for designing a multistage-multiepitopic vaccine.

Structural characterization of cassava linamarase-linamarin enzyme complex: an integrated computational approach

Cassava linamarase is a hydrolyzing enzyme that belongs to a glycoside hydrolase family 1 (GH1). It is responsible for breaking down linamarin to toxic cyanide. The enzyme provides a defensive mechanism for plants against herbivores and has various applications in many fields. Understanding the structure of linamarase at the molecular level is a key to avail its reaction mechanism. In this study, the three-dimensional (3D) structure of linamarase was built for the first time using homology modelling and used to study its interaction with linamarin. Molecular docking calculations established the binding and orientation nature of linamarin, while molecular dynamics (MD) simulation established protein-ligand complexes' stability. Binding-free energy based on MM/PBSA was further used to rescore the docking results. An ensemble structure was found to be relatively stable compared to the modelled structure. This study sheds light on the exploration of linamarase towards understanding its reaction mechanisms.Communicated by Ramaswamy H. Sarma.

Target SARS-CoV-2: theoretical exploration on clinical suitability of certain drugs

We propose a unique theoretical methodology because of the global high priority rating to search for the repurposed drugs that outfit clinical suitability to SARS-CoV-2. The approach is based on the exploration of structural analysis, computation of biothermodynamics, interactions and the prediction of entropy sign successively via molecular dynamics. We tested this methodology for Favipiravir/Dolutegravir drugs on the apo form of SARS-CoV-2 main protease. This theoretical exploration not only suggested the presence of strong interactions between (SARS-CoV-2 + Favipiravir/Dolutegravir) but also emphasized the clinical suitability of Favipiravir over Dolutegravir to treat SARS-CoV-2 main protease. The supremacy of Favipiravir over Doultegravir is well supported by the results of global clinical trials on SARS-CoV-2 infection. Thus, this work will pave the way for incremental advancement towards future design and development of more specific inhibitors to treat SARS-CoV-2 infection in humans.Communicated by Ramaswamy H. Sarma.

Dissecting the Conformational Free Energy of a Small Peptide in Solution

The free energy surface of a small peptide was analyzed based on an unbiased microsecond molecular dynamics simulation. The peptide sampled disordered conformational ensembles of distinct compactness, and its free energy was decomposed into separate contributions from the intramolecular potential energy, conformational entropy, and solvation free energy. The latter was further broken down into enthalpic and entropic contributions due to peptide-water and water-water interactions. This decomposition was enabled by a generalized linear response relation between the peptide-water interaction energy and the solvation free energy, which was empirically parametrized by explicit solvation free energy calculations for representative peptide conformations. This full dissection of the peptide free energy identifies individual contributions that stabilize and destabilize compact and extended peptide conformational ensembles and reveals the origin of a free energy barrier associated with transitions between them.

Quantum biochemistry, molecular docking, and dynamics simulation revealed synthetic peptides induced conformational changes affecting the topology of the catalytic site of SARS-CoV-2 main protease

The recent outbreak caused by SARS-CoV-2 continues to threat and take many lives all over the world. The lack of an efficient pharmacological treatments are serious problems to be faced by scientists and medical staffs worldwide. In this work, an in silico approach based on the combination of molecular docking, dynamics simulations, and quantum biochemistry revealed that the synthetic peptides RcAlb-PepI, PepGAT, and PepKAA, strongly interact with the main protease (Mpro) a pivotal protein for SARS-CoV-2 replication. Although not binding to the proteolytic site of SARS-CoV-2 Mpro, RcAlb-PepI, PepGAT, and PepKAA interact with other protein domain and allosterically altered the protease topology. Indeed, such peptide-SARS-CoV-2 Mpro complexes provoked dramatic alterations in the three-dimensional structure of Mpro leading to area and volume shrinkage of the proteolytic site, which could affect the protease activity and thus the virus replication. Based on these findings, it is suggested that RcAlb-PepI, PepGAT, and PepKAA could interfere with SARS-CoV-2 Mpro role in vivo. Also, unlike other antiviral drugs, these peptides have no toxicity to human cells. This pioneering in silico investigation opens up opportunity for further in vivo research on these peptides, towards discovering new drugs and entirely new perspectives to treat COVID-19.Communicated by Ramaswamy H. Sarma.

Resolving Entangled JH-H-Coupling Patterns for Steroidal Structure Determinations by NMR Spectroscopy

For decades, high-resolution ¹H NMR spectroscopy has been routinely utilized to analyze both naturally occurring steroid hormones and synthetic steroids, which play important roles in regulating physiological functions in humans. Because the ¹H signals are inevitably superimposed and entangled with various J_H–H splitting patterns, such that the individual ¹H chemical shift and associated J_H–H coupling identities are hardly resolved. Given this, applications of thess information for elucidating steroidal molecular structures and steroid/ligand interactions at the atomic level were largely restricted. To overcome, we devoted to unraveling the entangled J_H–H splitting patterns of two similar steroidal compounds having fully unsaturated protons, i.e., androstanolone and epiandrosterone (denoted as 1 and 2, respectively), in which only hydroxyl and ketone substituents attached to C3 and C17 were interchanged. Here we demonstrated that the J_H–H values deduced from 1 and 2 are universal and applicable to other steroids, such as testosterone, 3β, 21-dihydroxygregna-5-en-20-one, prednisolone, and estradiol. On the other hand, the ¹H chemical shifts may deviate substantially from sample to sample. In this communication, we propose a simple but novel scheme for resolving the complicate J_H–H splitting patterns and ¹H chemical shifts, aiming for steroidal structure determinations.

NAT2 polymorphisms associated with the development of hepatotoxicity after first-line tuberculosis treatment in Mexican patients: From genotype to molecular structure characterization

BACKGROUND AND AIMS

To assess the relevance of the slow acetylator phenotype based on NAT2 genotypes, among patients with pulmonary tuberculosis (PTB) that developed hepatotoxicity after first-line tuberculosis treatment in a Northeastern Mexican population.

METHODS

Ninety one PTB patients were included, 7 of them developed hepatotoxicity. NAT2 SNPs (rs1801279, rs1041983, rs1801280, rs1799929, rs1799930, rs1208, and rs1799931) were genotyped by TaqMan allelic discrimination assay. Statistical analyses were performed using Epi Info statistical software 7.0 and SHEsisPlus for haplotype reconstruction. The NAT2 slow non-synonymous SNP were studied by molecular dynamic analysis (MDA).

RESULTS

The frequency of the haplotype associated with slow acetylation status for PTB was 58%, and for with hepatotoxicity (PTB-H) represented 42.6%. Three haplotypes, NAT2*5Q, NAT2*5U, NAT2*5Va were exclusively present in seven PTB-H patients, (P = 0.01, P = 0.0006, P = 0.01, respectively). These haplotypes include the combination of two SNPs (I114T + R197Q or I114T + G286E). The effect of the SNPs on protein structure is to disrupt the CoA binding site affecting acetylation activity.

CONCLUSION

Our study provides insight into slow acetylation NAT2 haplotypes associated with hepatotoxicity after first-line tuberculosis treatment, for first time, in a Mexican population. The molecular mechanism acts at the CoA binding site.

Computational Identification of Potential Anti-Inflammatory Natural Compounds Targeting the p38 Mitogen-Activated Protein Kinase (MAPK): Implications for COVID-19-Induced Cytokine Storm

Severely ill coronavirus disease 2019 (COVID-19) patients show elevated concentrations of pro-inflammatory cytokines, a situation commonly known as a cytokine storm. The p38 MAPK receptor is considered a plausible therapeutic target because of its involvement in the platelet activation processes leading to inflammation. This study aimed to identify potential natural product-derived inhibitory molecules against the p38α MAPK receptor to mitigate the eliciting of pro-inflammatory cytokines using computational techniques. The 3D X-ray structure of the receptor with PDB ID 3ZS5 was energy minimized using GROMACS and used for molecular docking via AutoDock Vina. The molecular docking was validated with an acceptable area under the curve (AUC) of 0.704, which was computed from the receiver operating characteristic (ROC) curve. A compendium of 38,271 natural products originating from Africa and China together with eleven known p38 MAPK inhibitors were screened against the receptor. Four potential lead compounds ZINC1691180, ZINC5519433, ZINC4520996 and ZINC5733756 were identified. The compounds formed strong intermolecular bonds with critical residues Val38, Ala51, Lys53, Thr106, Leu108, Met109 and Phe169. Additionally, they exhibited appreciably low binding energies which were corroborated via molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations. The compounds were also predicted to have plausible pharmacological profiles with insignificant toxicity. The molecules were also predicted to be anti-inflammatory, kinase inhibitors, antiviral, platelet aggregation inhibitors, and immunosuppressive, with probable activity (Pa) greater than probable inactivity (Pi). ZINC5733756 is structurally similar to estradiol with a Tanimoto coefficient value of 0.73, which exhibits anti-inflammatory activity by targeting the activation of Nrf2. Similarly, ZINC1691180 has been reported to elicit anti-inflammatory activity in vitro. The compounds may serve as scaffolds for the design of potential biotherapeutic molecules against the cytokine storm associated with COVID-19.

BFEE2: Automated, Streamlined, and Accurate Absolute Binding Free-Energy Calculations

Accurate absolute binding free-energy estimation in silico, following either an alchemical or a geometrical route, involves several subprocesses and requires the introduction of geometric restraints. Human intervention, for instance, to define the necessary collective variables, prepare the input files, monitor the simulation, and perform post-treatments is, however, tedious, cumbersome, and prone to errors. With the aim of automating and streamlining free-energy calculations, especially for nonexperts, version 2.0 of the binding free energy estimator (BFEE2) provides both standardized alchemical and geometrical workflows and obviates the need for extensive human intervention to guarantee complete reproducibility of the results. To achieve the largest gamut of protein-ligand and, more generally, of host-guest complexes, BFEE2 supports most academic force fields, such as CHARMM, Amber, OPLS, and GROMOS. Configurational files are generated in the NAMD and Gromacs formats, and all the post-treatments are performed in an automated fashion. Moreover, convergence of the free-energy calculation can be monitored from the intermediate files generated during the simulation. All in all, BFEE2 is a foolproof, versatile tool for accurate absolute binding free-energy calculations, assisting the end-user over a broad range of applications.

Implementation of the QUBE Force Field in SOMD for High-Throughput Alchemical Free-Energy Calculations

The quantum mechanical bespoke (QUBE) force-field approach has been developed to facilitate the automated derivation of potential energy function parameters for modeling protein-ligand binding. To date, the approach has been validated in the context of Monte Carlo simulations of protein-ligand complexes. We describe here the implementation of the QUBE force field in the alchemical free-energy calculation molecular dynamics simulation package SOMD. The implementation is validated by demonstrating the reproducibility of absolute hydration free energies computed with the QUBE force field across the SOMD and GROMACS software packages. We further demonstrate, by way of a case study involving two series of non-nucleoside inhibitors of HIV-1 reverse transcriptase, that the availability of QUBE in a modern simulation package that makes efficient use of graphics processing unit acceleration will facilitate high-throughput alchemical free-energy calculations.

Docking and molecular dynamics studies of potential new leads against DBL3x derived from chondroitin sulfate A (CSA): a new approach for the treatment of malaria

In this work the DBL3x domain of the erythrocyte membrane protein from Plasmodium Falciparum (PfEMP1), was revisited as a potential molecular target for the development of new drugs against malaria. This protein interacts with chondroitin sulfate A (CSA), a glycosaminoglycan present in the substance fundamental for connective tissues of vertebrates and is implicated in malaria complications in pregnant women. We performed molecular docking and molecular dynamic studies of DBL3x complexed with CSA and five analogues, where the sulfate group was replaced by phosphate, in order to evaluate if the better electrostatic interactions provided by phosphate groups could afford better binders capable of preventing the binding of CSA to DBL3x. Results suggest that all proposed compounds have high affinity towards DBL3x and could bind better to the DBL3x domain of PfEMP1 than CSA, qualifying as potential inhibitors of this protein and, therefore, new potential leads for the drug design against malaria.Communicated by Ramaswamy H. Sarma.