Generalized Born Model Research Articles

IDENTIFICATION OF ANTIBACTERIAL AGENTS AGAINST KLEBSIELLA PNEUMONIAE TARGETING THE CTX-M-15 PROTEIN USING INTEGRATED STRUCTURE MODEL-BASED VIRTUAL SCREENING METHODS

The spread of carbapenem-resistant Gram-negative bacteria, particularly those having the bla CTX-M-15 gene, has stimulated a global challenge for β-lactam antimicrobial effectiveness. This study aims to identify commercially available compounds that demonstrate strong antibacterial properties with high efficacy and low toxicity to combat the increasing problem of antibiotic resistance. Virtual screening and molecular docking techniques were employed to generate a pharmacophore model based on the molecular structure. Eleven potential compounds were selected based on their IC50 values. Subsequently, a molecular docking approach was employed to select the best three small molecules for further comprehensive investigation. Moreover, absorption, distribution, metabolism, and excretion (ADME) and toxicity analysis ensured that it can be used as a drug without the effect of health. Finally, molecular dynamic (MD) simulations and generalized Born model and solvent accessibility (MMGB/SA) were carried out to evaluate the stability of these compounds against the receptor. Three selected compounds, specifically ZINCCID (ZINC94211493, ZINC20528448, and ZINC04331046), have exhibited strong binding affinities of -8.4, -8.1, and -7.7 kcal/mol, respectively. This has been predicted as a potential competitive inhibitor targeting CTX-M-15. However, further assessment through experimental lab studies is required to evaluate the efficacy of these compounds. Initially, a structure-based model was constructed, and subsequently, molecular docking, MD simulation, ADMET analysis, and MMGB/SA were performed. Through the A-to-Z virtual screening process, the top three natural compounds were identified as potential lead molecules in the development of novel drugs targeting CTX-M-15 for combating Klebsiella infections. Keywords: Klebsiella; Structure-based; Molecular docking; Pharmacophore modeling; CTX-M-15 protein; ADMET; MMGB/SA, MD simulation.

Implicit Solvent with Explicit Ions Generalized Born Model in Molecular Dynamics: Application to DNA.

The ion atmosphere surrounding highly charged biomolecules, such as nucleic acids, is crucial for their dynamics, structure, and interactions. Here, we develop an approach for the explicit treatment of ions within an implicit solvent framework suitable for atomistic simulations of biomolecules. The proposed implicit solvent/explicit ions model, GBION, is based on a modified generalized Born (GB) model; it includes separate, modified GB terms for solute-ion and ion-ion interactions. The model is implemented in the AMBER package (version 24), and its performance is thoroughly investigated in atomistic molecular dynamics (MD) simulations of double-stranded DNA on a microsecond time scale. The aggregate characteristics of monovalent (Na+ and K+) and trivalent (Cobalt Hexammine, CoHex3+) counterion distributions around double-stranded DNA predicted by the model are in reasonable agreement with the experiment (where available), all-atom explicit water MD simulations, and the expectation from the Manning condensation theory. The radial distributions of monovalent cations around DNA are reasonably close to the ones obtained using the explicit water model: expressed in units of energy, the maximum deviations of local ion concentrations from the explicit solvent reference are within 1 kBT, comparable to the corresponding deviations expected between different established explicit water models. The proposed GBION model is able to simulate DNA fragments in a large volume of solvent with explicit ions with little additional computational overhead compared with the fully implicit GB treatment of ions. Ions simulated using the developed model explore conformational space at least 2 orders of magnitude faster than in the explicit solvent. These advantages allowed us to observe and explore an unexpected "stacking" mode of DNA condensation in the presence of trivalent counterions (CoHex3+) that was revealed by recent experiments.

Inclusion of Water Multipoles into the Implicit Solvation Framework Leads to Accuracy Gains.

The current practical "workhorses" of the atomistic implicit solvation─the Poisson-Boltzmann (PB) and generalized Born (GB) models─face fundamental accuracy limitations. Here, we propose a computationally efficient implicit solvation framework, the Implicit Water Multipole GB (IWM-GB) model, that systematically incorporates the effects of multipole moments of water molecules in the first hydration shell of a solute, beyond the dipole water polarization already present at the PB/GB level. The framework explicitly accounts for coupling between polar and nonpolar contributions to the total solvation energy, which is missing from many implicit solvation models. An implementation of the framework, utilizing the GAFF force field and AM1-BCC atomic partial charges model, is parametrized and tested against the experimental hydration free energies of small molecules from the FreeSolv database. The resulting accuracy on the test set (RMSE ∼ 0.9 kcal/mol) is 12% better than that of the explicit solvation (TIP3P) treatment, which is orders of magnitude slower. We also find that the coupling between polar and nonpolar parts of the solvation free energy is essential to ensuring that several features of the IWM-GB model are physically meaningful, including the sign of the nonpolar contributions.

Molecular dynamics simulations as a guide for modulating small molecule aggregation

Small colloidally aggregating molecules (SCAMs) can be problematic for biological assays in drug discovery campaigns. However, the self-associating properties of SCAMs have potential applications in drug delivery and analytical biochemistry. Consequently, the ability to predict the aggregation propensity of a small organic molecule is of considerable interest. Chemoinformatics-based filters such as ChemAGG and Aggregator Advisor offer rapid assessment but are limited by the assay quality and structural diversity of their training set data. Complementary to these tools, we explore here the ability of molecular dynamics (MD) simulations as a physics-based method capable of predicting the aggregation propensity of diverse chemical structures. For a set of 32 molecules, using simulations of 100 ns in explicit solvent, we find a success rate of 97% (one molecule misclassified) as opposed to 75% by Aggregator Advisor and 72% by ChemAGG. These short timescale MD simulations are representative of longer microsecond trajectories and yield an informative spectrum of aggregation propensities across the set of solutes, capturing the dynamic behaviour of weakly aggregating compounds. Implicit solvent simulations using the generalized Born model were less successful in predicting aggregation propensity. MD simulations were also performed to explore structure-aggregation relationships for selected molecules, identifying chemical modifications that reversed the predicted behaviour of a given aggregator/non-aggregator compound. While lower throughput than rapid cheminformatics-based SCAM filters, MD-based prediction of aggregation has potential to be deployed on the scale of focused subsets of moderate size, and, depending on the target application, provide guidance on removing or optimizing a compound’s aggregation propensity.Graphical

Tuning Potential Functions to Host-Guest Binding Data.

Software to more rapidly and accurately predict protein-ligand binding affinities is of high interest for early-stage drug discovery, and physics-based methods are among the most widely used technologies for this purpose. The accuracy of these methods depends critically on the accuracy of the potential functions that they use. Potential functions are typically trained against a combination of quantum chemical and experimental data. However, although binding affinities are among the most important quantities to predict, experimental binding affinities have not to date been integrated into the experimental data set used to train potential functions. In recent years, the use of host-guest complexes as simple and tractable models of binding thermodynamics has gained popularity due to their small size and simplicity, relative to protein-ligand systems. Host-guest complexes can also avoid ambiguities that arise in protein-ligand systems such as uncertain protonation states. Thus, experimental host-guest binding data are an appealing additional data type to integrate into the experimental data set used to optimize potential functions. Here, we report the extension of the Open Force Field Evaluator framework to enable the systematic calculation of host-guest binding free energies and their gradients with respect to force field parameters, coupled with the curation of 126 host-guest complexes with available experimental binding free energies. As an initial application of this novel infrastructure, we optimized generalized Born (GB) cavity radii for the OBC2 GB implicit solvent model against experimental data for 36 host-guest systems. This refitting led to a dramatic improvement in accuracy for both the training set and a separate test set with 90 additional host-guest systems. The optimized radii also showed encouraging transferability from host-guest systems to 59 protein-ligand systems. However, the new radii are significantly smaller than the baseline radii and lead to excessively favorable hydration free energies (HFEs). Thus, users of the OBC2 GB model currently may choose between GB cavity radii that yield more accurate binding affinities and GB cavity radii that yield more accurate HFEs. We suspect that achieving good accuracy on both will require more far-reaching adjustments to the GB model. We note that binding free-energy calculations using the OBC2 model in OpenMM gain about a 10× speedup relative to corresponding explicit solvent calculations, suggesting a future role for implicit solvent absolute binding free-energy (ABFE) calculations in virtual compound screening. This study proves the principle of using host-guest systems to train potential functions that are transferrable to protein-ligand systems and provides an infrastructure that enables a range of applications.

Quantum Computer Simulation of Protein Protonation.

In attempts to simulate the protonation of proteins, a major challenge is that the number of protonation states grows rapidly as a function (2N) of the number of protonation sites (N). Expression on the free energy of the protonation state as an N-site Ising model ─ using an empirical Generalized-Born model ─ allows a quantum computer to efficiently determine the important states at a given pH value and subsequently reconstruct the pH titration process at all sites. Compared with the exact results painstakingly obtained with classical computers, the results obtained using quantum computers show good agreement for staphylococcal nuclease and excellent agreement for α-lactalbumin. This work illustrates the effectiveness of quantum computers in sampling important physical states, which may be useful in attacking challenging biomolecular problems.

Molecular electrostatics and pKa shifts calculations with the Generalized Born model. A tutorial through examples with Bluues2

Biomolecular electrostatics is of key importance for biological function and recognition. The continuum electrostatic model based on the Poisson-Boltzmann (PB) equation has been widely used to study biomolecular electrostatics. The solution of the PB equation gives the electrostatic potential over the space enclosing the molecule(s) of interest, typically obtained at points of a suitable grid.The Generalized Born (GB) model has been used to provide a useful approximation to the solution obtained by solving numerically the PB equation. The main advantage of the GB approach is to express the electrostatic energy of the system as the sum of pairwise interactions. The latter depend on geometric parameters (the GB radii) which depend on all atoms, and their radii, of the system. In this work we present a tutorial through examples for a more efficient and general version of the program Bluues which is able to compute:1) the generalized Born radius of each atom;2) the electrostatic potential at the surface of the molecule mapped to solvent accessible atoms;3) the solvent accessible surface in a PDB formatted file;4) the electrostatic potential in the volume surrounding the molecule;5) the electrostatic free energy and different contributions to it;6) the pH-dependent properties of proteins (total charge and pH-dependent free energy of folding) in the pH range −4 to 18;7) the pKa shifts due to molecular structure of all ionizable groups. Program summaryProgram Title: bluues2CPC Library link to program files:https://doi.org/10.17632/bx98gcrzbg.1Developer's repository link:https://github.com/federico-fogolari/bluues2Code Ocean capsule:https://codeocean.com/capsule/9338710Licensing provisions: GPLv3Programming language: CNature of problem: Biomolecular electrostatics is of key importance for biological function and recognition. Computation of electrostatic effects and their dissection in atomic contributions is a general problem in molecular modeling.Solution method: The GBR6 version of the Generalized Born (GB) model is implemented through surface integrals and all common molecular electrostatic analyses are performed by a single command line program.Additional comments including restrictions and unusual features: The program requires, as input, files in pqr format, which may be generated by independent software (e.g. babel, pdb2pqr). For the optional generation of the solvent excluded surface, the software msms must be installed and present in the path.

The Intrinsic Radius as a Key Parameter in the Generalized Born Model to Adjust Protein-Protein Electrostatic Interaction.

The generalized Born (GB) model is an extension of the continuum dielectric theory of Born solvation energy and is a powerful method for accelerating the molecular dynamic (MD) simulations of charged biological molecules in water. While the effective dielectric constant of water that varies as a function of the separation distance between solute molecules is incorporated into the GB model, adjustment of the parameters is indispensable for accurate calculation of the Coulomb (electrostatic) energy. One of the key parameters is the lower limit of the spatial integral of the energy density of the electric field around a charged atom, known as the intrinsic radius ρ. Although ad hoc adjustment of ρ has been conducted to improve the Coulombic (ionic) bond stability, the physical mechanism by which ρ affects the Coulomb energy remains unclear. Via energetic analysis of three differently sized systems, here, we clarify that the Coulomb bond stability increases with increasing ρ and that the increased stability is caused by the interaction energy term, not by the self-energy (desolvation energy) term, as was supposed previously. Our results suggest that the use of larger values for the intrinsic radii of hydrogen and oxygen atoms, together with the use of a relatively small value for the spatial integration cutoff in the GB model, can better reproduce the Coulombic attraction between protein molecules.

Assessment of Different Parameters on the Accuracy of Computational Alanine Scanning of Protein-Protein Complexes with the Molecular Mechanics/Generalized Born Surface Area Method.

Computational alanine scanning with the molecular mechanics generalized Born surface area (MM/GBSA) method constitutes a widely used approach for identifying critical residues at protein-protein interfaces. Despite its popularity, the MM/GBSA method still has certain drawbacks due to its dependence on many factors. Here, we performed a systematical study on the impact of four different parameters, namely, the internal dielectric constant, the generalized Born model, the entropic term, and the inclusion of structural waters on the accuracy of computational alanine scanning calculations with the MM/GBSA method. Our results show that the internal dielectric constant is the most critical parameter for getting accurate predictions. The introduction of entropy and interfacial water molecules decreased the quality of the predictions, while the generalized Born model had little to no effect. Considering the significance of the internal dielectric value, we proposed a methodology based on the energetic predominance of a particular set of amino acids at the protein-protein interface for selecting an appropriate value for this variable. We hope that these results serve as a guideline for future studies of protein-protein complexes using the MM/GBSA method.

CHARMM-GUI Implicit Solvent Modeler for Various Generalized Born Models in Different Simulation Programs

Implicit solvent models are widely used because they are advantageous to speed up simulations by drastically decreasing the number of solvent degrees of freedom, which allows one to achieve long simulation time scales for large system sizes. CHARMM-GUI, a web-based platform, has been developed to support the setup of complex multicomponent molecular systems and prepare input files. This study describes an Implicit Solvent Modeler (ISM) in CHARMM-GUI for various generalized Born (GB) implicit solvent simulations in different molecular dynamics programs such as AMBER, CHARMM, GENESIS, NAMD, OpenMM, and Tinker. The GB models available in ISM include GB-HCT, GB-OBC, GB-neck, GBMV, and GBSW with the CHARMM and Amber force fields for protein, DNA, RNA, glycan, and ligand systems. Using the system and input files generated by ISM, implicit solvent simulations of protein, DNA, and RNA systems produce similar results for different simulation packages with the same input information. Protein-ligand systems are also considered to further validate the systems and input files generated by ISM. Simple ligand root-mean-square deviation (RMSD) and molecular mechanics generalized Born surface area (MM/GBSA) calculations show that the performance of implicit simulations is better than docking and can be used for early stage ligand screening. These reasonable results indicate that ISM is a useful and reliable tool to provide various implicit solvent simulation applications.

Computational prediction of potential drug-like compounds from Cannabis sativa leaf extracts targeted towards Alzheimer therapy

• Phytochemicals isolated from Cannabis sativa can improve behavioral symptoms associated with neurological diseases including AD. • Morphinan-6-one demonstrated inhibitory actions against AChE and DDC, suggesting that it might have dual functions in AD. • Morphinan-6-one is hypothesized to be a modulatory molecule for acetylcholinesterase. This study was aimed at evaluating the inhibitory effects of the phytochemicals from the Cannabis sativa (Cannabis) leaf extracts against Alzheimer’s disease (AD) protein targets. Twelve compounds derived from the Cannabis sativa leaf extracts were evaluated as potential inhibitors of acetylcholinesterase (AChE), dopa decarboxylase (DDC), serotonin receptor 2C (HTR2C) and monoamine oxidase (MAO). Ligand-based and receptor-ligand complex were used to derive the pharmacophore hypothesis. In silico study through molecular docking simulation method was adopted to analyze the inhibitory activity of the compounds in question. Molecular dynamic simulation (MDs) was performed to assess the stability of the top-ranked phytochemicals. The binding energies of these compounds to the four targets were investigated by the Molecular Mechanics for the Generalized Born Model and Solvent Accessibility method (MM-GBSA). The binding-free energy suggests that cannabinol, cannabichromene, linoelaidic acid and morphinan-6-one can be utilized as lead compounds in drug discovery and development of AD to inhibit activity of AChE, DCC gene, MAO and HTR2C. The MDs indicated that AChE-Cannabinol, DCC-Cannabicoumaronone, MAO-Linoelaidic acid, and HTR2C-morphinan-6-one were stable over the entire course of 100 ns suggesting their role in the regulation of the diseases in which their respective receptors are implicated.

Generalized Born Implicit Solvent Models Do Not Reproduce Secondary Structures of De Novo Designed Glu/Lys Peptides.

We test a range of standard generalized Born (GB) models and protein force fields for a set of five experimentally characterized, designed peptides comprising alternating blocks of glutamate and lysine, which have been shown to differ significantly in α-helical content. Sixty-five combinations of force fields and GB models are evaluated in >800 μs of molecular dynamics simulations. GB models generally do not reproduce the experimentally observed α-helical content, and none perform well for all five peptides. These results illustrate that these models are not usefully predictive in this context. These peptides provide a useful test set for simulation methods.

Conformational Preferences of an N-Glycan in Aqueous Solution: A Case Study for Evaluating the Accuracy of Generalized Born Model

Conformational Preferences of an N-Glycan in Aqueous Solution: A Case Study for Evaluating the Accuracy of Generalized Born Model

Design of novel hybrid secondary metabolite targets to diguanylate cyclase of Acinetobacter baumannii.

Biofilm formation in bacteria is a resistance determinant and is positively regulated by cyclic diguanylate signaling. This signaling is a near universal signaling, and c-di-GMP produced by diguanylate cyclase (DGC) in this signaling is involved in different bacterial behaviors. The present study aims to find a plant-based novel hybrid therapeutic agent that can target the DGC of Acinetobacter baumannii. In this study, we have tried to design a hybrid molecule from the anti-biofilm plant secondary metabolites and screened its binding with the DGC of A. baumannii. The modeled and validated DGC was used to identify the active site and docking grid. Designed hybrid compounds were analysed for their interaction with the active site residues of DGC of A. baumannii. Further, the binding free energies of the docked complexes obtained from the Generalized Born model and Solvent Accessibility (MMGBSA) were analysed. The results indicated that VR-QEg-180 has a predicted high binding affinity with enzyme DGC as compared to other hybrids, parent secondary metabolites and positive control. Molecular dynamics simulation (MDS) analysis confirmed the interaction of VR-QEg-180 with DGC of the A. baumannii. The designed lead has favorable ADMET properties, has no human off-targets and has no predicted cytotoxicity in cell lines. Therefore, the designed hybrid molecule (VR-QEg-180) targeting the DGC of A. baumannii may play a very significant role in controlling this pathogen.

Dipeptidyl peptidase-IV inhibitory action of Calebin A: An in silico and in vitro analysis

BackgroundDipeptidyl peptidase-IV (DPP-IV) inhibitors, the enhancers of incretin are used for the treatment of diabetes. The non-glycaemic actions of these drugs (under developmental stage) also proved that repurposing of these molecules may be advantageous for other few complicated disorders like cardiovascular diseases, Parkinson's disease, Alzheimer's disease, etc. ObjectiveThe present study was aimed to investigate the DPP-IV inhibitory potential of Calebin-A, one of the constituents of Curcuma longa. Material and methodsThe phytoconstituent was subjected for various in silico studies (using Schrödinger Suite) like, Docking analysis, molecular mechanics combined with generalized Born model and solvent accessibility method (MMGBSA) and Induced fit docking (IFD) after validating the protein using Ramachandran plot. Further, the protein-ligand complex was subjected to molecular dynamic simulation studies for 50 nanoseconds. And finally, the results were confirmed through enzyme inhibition study. ResultsInsilico results revealed possible inhibitory binding interactions in the catalytic pocket (importantly Glu205, Glu206 and Tyr 662 etc.) and binding affinity in terms of glide g-score and MMGBSA dG bind values were found to be −6.2 kcal/mol and −98.721 kcal/mol. Further, the inhibitory action towards the enzyme was confirmed by an enzyme inhibition assay, in which it showed dose-dependent inhibition, with maximum % inhibition of 55.9 at 26.3 μM. From molecular dynamic studies (50 nanoseconds), it was understood that Calebin A was found to be stable for about 30 nanoseconds in maintaining inhibitory interactions. ConclusionFrom the in silico and in vitro analysis, the current research emphasizes the consideration of Calebin A to be as a promising or lead compound for the treatment of several ailments where DPP-IV action is culprit.

Synthesis, molecular docking, DFT study, and in vitro antimicrobial activity of some 4-(biphenyl-4-yl)-1,4-dihydropyridine and 4-(biphenyl-4-yl)pyridine derivatives.

The evolution of microbial resistance necessitates the development of new antimicrobial drugs that are more effective than those currently on the market. To address this problem, we have prepared a series of novel 4-(biphenyl-4-yl)-1,4-dihydropyridine and 4-(biphenyl-4-yl)pyridine derivatives via Hantzsch reaction using nine different compounds containing active methylene group. IR, NMR, and mass spectra were used to determine the structures. Using ampicillin and griseofulvin as standards, the titled compounds were investigated for their antibacterial activity against different bacteria and fungi. Compounds 1f, 1g, 2f, and 2g have the best antibacterial activity against Gram-negative bacteria (minimum inhibitory concentration = 50 μg/ml), while 1f, 1h, 2g, and 2h have high antifungal activity against Candida albicans (minimum inhibitory concentration = 100 μg/ml). To gain a better understanding of the binding process and affinity for the bacterial Staphylococcus epidermidis protein, researchers used molecular docking and molecular mechanics, as well as the generalized Born model and solvent accessibility-based binding free energy. The active compounds 1g, 1h, and 2f have good docking scores of -5.575, -5.949, and -5.234, respectively, whereas compound 2c has the greatest docking score (-6.23). The HOMO-LUMO energy gap and molecular electrostatic potential were used to evaluate the reactivity of promising compounds, which were then associated with antibacterial efficacy.

Combining SARS-CoV-2 Proofreading Exonuclease and RNA-Dependent RNA Polymerase Inhibitors as a Strategy to Combat COVID-19: A High-Throughput in silico Screening.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected millions of people worldwide. Currently, many clinical trials in search of effective COVID-19 drugs are underway. Viral RNA-dependent RNA polymerase (RdRp) remains the target of choice for prophylactic or curative treatment of COVID-19. Nucleoside analogs are the most promising RdRp inhibitors and have shown effectiveness in vitro, as well as in clinical settings. One limitation of such RdRp inhibitors is the removal of incorporated nucleoside analogs by SARS-CoV-2 exonuclease (ExoN). Thus, ExoN proofreading activity accomplishes resistance to many of the RdRp inhibitors. We hypothesize that in the absence of highly efficient antivirals to treat COVID-19, combinatorial drug therapy with RdRp and ExoN inhibitors will be a promising strategy to combat the disease. To repurpose drugs for COVID-19 treatment, 10,397 conformers of 2,240 approved drugs were screened against the ExoN domain of nsp14 using AutoDock VINA. The molecular docking approach and detailed study of interactions helped us to identify dexamethasone metasulfobenzoate, conivaptan, hesperidin, and glycyrrhizic acid as potential inhibitors of ExoN activity. The results were further confirmed using molecular dynamics (MD) simulations and molecular mechanics combined with generalized Born model and solvent accessibility method (MM-GBSA) calculations. Furthermore, the binding free energy of conivaptan and hesperidin, estimated using MM-GBSA, was −85.86 ± 0.68 and 119.07 ± 0.69 kcal/mol, respectively. Based on docking, MD simulations and known antiviral activities, and conivaptan and hesperidin were identified as potential SARS-CoV-2 ExoN inhibitors. We recommend further investigation of this combinational therapy using RdRp inhibitors with a repurposed ExoN inhibitor as a potential COVID-19 treatment.

GalaxyWater-wKGB: Prediction of Water Positions on Protein Structure Using wKGB Statistical Potential.

Proteins fold and function in water, and protein-water interactions play important roles in protein structure and function. In computational studies on protein structure and interaction, the effect of water is considered either implicitly or explicitly. Implicit water models are frequently used in protein structure prediction and docking because they are computationally much more efficient than explicit water models, which are often employed in molecular dynamics (MD) simulations. However, implicit water models that treat water as a continuous solvent medium cannot account for specific atomistic protein-water interactions that are critical for structure formation and interactions with other molecules. Various methods for predicting water molecules that form specific atomistic interactions with proteins have been developed. Methods involving MD simulations or the integral equation theory tend to produce more accurate results at a higher computational cost than simple geometry- or energy-based methods. Here, we present a novel method for predicting water positions on a protein surface called GalaxyWater-wKGB, which is based on a statistical potential, a water knowledge-based potential based on the generalized Born model (wKGB). This method is accurate and rapid because it does not require conformational sampling or iterative computation owing to the effective statistical treatment employed to derive the potential. The statistical potential describes specific protein atom-water interactions more accurately than conventional potentials by considering the dependence on the degree of solvent accessibility of protein atoms as well as on protein atom-water distances and orientations. The introduction of solvent accessibility allows effective consideration of competing nonspecific protein-water and intraprotein interactions. When tested on high-resolution protein crystal structures, this method could recover similar or larger fractions of crystallographic water 180 times faster than the sophisticated integral equation theory, 3D-RISM. A web service of this water prediction method is freely available at http://galaxy.seoklab.org/wkgb.

An Effective MM/GBSA Protocol for Absolute Binding Free Energy Calculations: A Case Study on SARS-CoV-2 Spike Protein and the Human ACE2 Receptor.

The binding free energy calculation of protein–ligand complexes is necessary for research into virus–host interactions and the relevant applications in drug discovery. However, many current computational methods of such calculations are either inefficient or inaccurate in practice. Utilizing implicit solvent models in the molecular mechanics generalized Born surface area (MM/GBSA) framework allows for efficient calculations without significant loss of accuracy. Here, GBNSR6, a new flavor of the generalized Born model, is employed in the MM/GBSA framework for measuring the binding affinity between SARS-CoV-2 spike protein and the human ACE2 receptor. A computational protocol is developed based on the widely studied Ras–Raf complex, which has similar binding free energy to SARS-CoV-2/ACE2. Two options for representing the dielectric boundary of the complexes are evaluated: one based on the standard Bondi radii and the other based on a newly developed set of atomic radii (OPT1), optimized specifically for protein–ligand binding. Predictions based on the two radii sets provide upper and lower bounds on the experimental references: kcal/mol. The consensus estimates of the two bounds show quantitative agreement with the experiment values. This work also presents a novel truncation method and computational strategies for efficient entropy calculations with normal mode analysis. Interestingly, it is observed that a significant decrease in the number of snapshots does not affect the accuracy of entropy calculation, while it does lower computation time appreciably. The proposed MM/GBSA protocol can be used to study the binding mechanism of new variants of SARS-CoV-2, as well as other relevant structures.

A Multifunctional Peptide From Bacillus Fermented Soybean for Effective Inhibition of SARS-CoV-2 S1 Receptor Binding Domain and Modulation of Toll Like Receptor 4: A Molecular Docking Study.

Fermented soybean products are traditionally consumed and popular in many Asian countries and the northeastern part of India. To search for potential agents for the interruption of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Spike glycoprotein 1 (S1) and human angiotensin-converting enzyme 2 (ACE2) receptor interactions, the in silico antiviral prospective of peptides identified from the proteome of kinema was investigated. Soybean was fermented using Bacillus licheniformis KN1G, Bacillus amyloliquefaciens KN2G and two different strains of Bacillus subtilis (KN2B and KN2M). The peptides were screened in silico for possible antiviral activity using two different web servers (AVPpred and meta-iAVP), and binding interactions of selected 44 peptides were further explored against the receptor-binding domain (RBD) of the S1 protein (PDB ID: 6M0J) by molecular docking using ZDOCK. The results showed that a peptide ALPEEVIQHTFNLKSQ (P13) belonging to B. licheniformis KN1G fermented kinema was able to make contacts with the binding motif of RBD by blocking specific residues designated as critical (GLN493, ASN501) in the binding of human angiotensin-converting enzyme 2 (ACE2) cell receptor. The selected peptide was also observed to have a significant affinity towards human toll like receptor 4 (TLR4)/Myeloid Differentiation factor 2 (MD2) (PDB ID: 3FXI) complex known for its essential role in cytokine storm. The energy properties of the docked complexes were analyzed through the Generalized Born model and Solvent Accessibility method (MM/GBSA) using HawkDock server. The results showed peptidyl amino acids GLU5, GLN8, PHE11, and LEU13 contributed most to P13-RBD binding. Similarly, ARG90, PHE121, LEU61, PHE126, and ILE94 were appeared to be significant in P13-TLR4/MD2 complex. The findings of the study suggest that the peptides from fermented soy prepared using B. licheniformis KN1G have better potential to be used as antiviral agents. The specific peptide ALPEEVIQHTFNLKSQ could be synthesized and used in combination with experimental studies to validate its effect on SARS-CoV-2-hACE2 interaction and modulation of TLR4 activity. Subsequently, the protein hydrolysate comprising these peptides could be used as prophylaxis against viral diseases, including COVID-19.