Experimental Binding Affinities Research Articles

Identification of C-β-d-Glucopyranosyl Azole-Type Inhibitors of Glycogen Phosphorylase That Reduce Glycogenolysis in Hepatocytes: In Silico Design, Synthesis, in Vitro Kinetics, and ex Vivo Studies.

Several C-β-d-glucopyranosyl azoles have recently been uncovered as among the most potent glycogen phosphorylase (GP) catalytic site inhibitors discovered to date. Toward further exploring their translational potential, ex vivo experiments have been performed for their effectiveness in reduction of glycogenolysis in hepatocytes. New compounds for these experiments were predicted in silico where, for the first time, effective ranking of GP catalytic site inhibitor potencies using the molecular mechanics-generalized Born surface area (MM-GBSA) method has been demonstrated. For a congeneric training set of 27 ligands, excellent statistics in terms of Pearson (RP) and Spearman (RS) correlations (both 0.98), predictive index (PI = 0.99), and area under the receiver operating characteristic curve (AU-ROC = 0.99) for predicted versus experimental binding affinities were obtained, with ligand tautomeric/ionization states additionally considered using density functional theory (DFT). Seven 2-aryl-4(5)-(β-d-glucopyranosyl)-imidazoles and 2-aryl-4-(β-d-glucopyranosyl)-thiazoles were subsequently synthesized, and kinetics experiments against rabbit muscle GPb revealed new potent inhibitors with best Ki values in the low micromolar range (5c = 1.97 μM; 13b = 4.58 μM). Ten C-β-d-glucopyranosyl azoles were then tested ex vivo in mouse primary hepatocytes. Four of these (5a-c and 9d) demonstrated significant reduction of glucagon stimulated glycogenolysis (IC50 = 30-60 μM). Structural and predicted physicochemical properties associated with their effectiveness were analyzed with permeability related parameters identified as crucial factors. The most effective ligand series 5 contained an imidazole ring, and the calculated pKa (Epik: 6.2; Jaguar 5.5) for protonated imidazole suggests that cellular permeation through the neutral state is favored, while within the cell, there is predicted more favorable binding to GP in the protonated form.

Heterogeneous Dielectric Implicit Membrane Model for the Calculation of MMPBSA Binding Free Energies.

Membrane-bound protein receptors are a primary biological drug target, but the computational analysis of membrane proteins has been limited. In order to improve molecular mechanics Poisson-Boltzmann surface area (MMPBSA) binding free energy calculations for membrane protein-ligand systems, we have optimized a new heterogeneous dielectric implicit membrane model, with respect to free energy simulations in explicit membrane and explicit water, and implemented it into the Amber software suite. This new model supersedes our previous uniform, single dielectric implicit membrane model by allowing the dielectric constant to vary with depth within the membrane. We calculated MMPBSA binding free energies for the human purinergic platelet receptor (P2Y12R) and two of the muscarinic acetylcholine receptors (M2R and M3R) bound to various antagonist ligands using both membrane models, and we found that the heterogeneous dielectric membrane model has a stronger correlation with experimental binding affinities compared to the older model under otherwise identical conditions. This improved membrane model increases the utility of MMPBSA calculations for the rational design and improvement of future drug candidates.

Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing

The goal of Binding MOAD is to provide users with a data set focused on high-quality x-ray crystal structures that have been solved with biologically relevant ligands bound. Where available, experimental binding affinities (Ka, Kd, Ki, IC50) are provided from the primary literature of the crystal structure. The database has been updated regularly since 2005, and this most recent update has added nearly 7000 new structures (growth of 21%). MOAD currently contains 32,747 structures, composed of 9117 protein families and 16,044 unique ligands. The data are freely available on www.BindingMOAD.org. This paper outlines updates to the data in Binding MOAD as well as improvements made to both the website and its contents. The NGL viewer has been added to improve visualization of the ligands and protein structures. MarvinJS has been implemented, over the outdated MarvinView, to work with JChem for small molecule searching in the database. To add tools for predicting polypharmacology, we have added information about sequence, binding-site, and ligand similarity between entries in the database. A main premise behind polypharmacology is that similar binding sites will bind similar ligands. The large amount of protein–ligand information available in Binding MOAD allows us to compute pairwise ligand and binding-site similarities. Lists of similar ligands and similar binding sites have been added to allow users to identify potential polypharmacology pairs. To show the utility of the polypharmacology data, we detail a few examples from Binding MOAD of drug repurposing targets with their respective similarities.

Exploring Gatekeeper Mutations in EGFR through Computer Simulations.

The emergence of resistance against drugs that inhibit a particular protein is a major problem in targeted therapy. There is a clear need for rigorous methods to predict the likelihood of specific drug-resistance mutations arising in response to the binding of a drug. In this work we attempt to develop a robust computational protocol for predicting drug resistant mutations at the gatekeeper position (T790) in EGFR. We explore how mutations at this site affects interactions with ATP and three drugs that are currently used in clinics. We found, as expected, that certain mutations are not tolerated structurally, while some other mutations interfere with the natural substrate and hence are unlikely to be selected for. However, we found five possible mutations that are well tolerated structurally and energetically. Two of these mutations were predicted to have increased affinity for the drugs over ATP, as has been reported earlier. By reproducing the trends in the experimental binding affinities of the data, the methods chosen here are able to correctly predict the effects of these mutations on the binding affinities of the drugs. However, the increased affinity does not always translate into increased efficacy, because the efficacy is affected by several other factors such as binding kinetics, competition with ATP, and residence times. The computational methods used in the current study are able to reproduce or predict the effects of mutations on the binding affinities. However, a different set of methods is required to predict the kinetics of drug binding.

Optimization and Evaluation of Site-Identification by Ligand Competitive Saturation (SILCS) as a Tool for Target-Based Ligand Optimization.

Chemical fragment cosolvent sampling techniques have become a versatile tool in ligand-protein binding prediction. Site-identification by ligand competitive saturation (SILCS) is one such method that maps the distribution of chemical fragments on a protein as free energy fields called FragMaps. Ligands are then simulated via Monte Carlo techniques in the field of the FragMaps (SILCS-MC) to predict their binding conformations and relative affinities for the target protein. Application of SILCS-MC using a number of different scoring schemes and MC sampling protocols against multiple protein targets was undertaken to evaluate and optimize the predictive capability of the method. Seven protein targets and 551 ligands with broad chemical variability were used to evaluate and optimize the model to maximize Pearson's correlation coefficient, Pearlman's predictive index, correct relative binding affinity, and root-mean-square error versus the absolute experimental binding affinities. Across the protein-ligand sets, the relative affinities of the ligands were predicted correctly an average of 69% of the time for the highest overall SILCS protocol. Training the FragMap weighting factors using a Bayesian machine learning (ML) algorithm led to an increase to an average 75% relative correct affinity predictions. Furthermore, once the optimal protocol is identified for a specific protein-ligand system average predictabilities of 76% are achieved. The ML algorithm is successful with small training sets of data (30 or more compounds) due to the use of physically correct FragMap weights as priors. Notably, the 76% correct relative prediction rate is similar to or better than free energy perturbation methods that are significantly computationally more expensive than SILCS. The results further support the utility of SILCS as a powerful and computationally accessible tool to support lead optimization and development in drug discovery.

Structural analysis and rational design of orthogonal stacking system in an E. coli DegP PDZ1–peptide complex

The DegP is essential for clearance of denatured or aggregated components from the inner-membrane and periplasmic space in Escherichia coli (E. coli). The enzyme contains two regulatory PDZ domains that have been shown to act as substrate specificity determinant by binding to the C-terminal hydrophobic stretch of substrate proteins. Here, the complex structure of E. coli DegP PDZ1 domain with a phage-displayed C3H1 pentapeptide is modeled and examined using peptide grafting, virtual mutagenesis, and QM/MM calculation. An orthogonal stacking system is identified at the domain–peptide complex interface, which consists of a T-shaped cation-π stacking (t-stacking) and a paralleled cation-π stacking (p-stacking) formed from domain cationic residue R325 to peptide aromatic residues Trp−1 and Phe−4, respectively. A synergistic effect between t-stacking and p-stacking is observed; π-electron conjugation is primarily responsible for the synergistic effect. Subsequently, the two peptide aromatic residues are systematically replaced by other aromatic amino acids as well as a non-aromatic alanine to optimize the synergistic effect, from which the binding affinities of wild-type C3H1 peptide and seven variants to E. coli DegP PDZ1 domain are determined at micromolar level using fluorescence-based assay. A good linear correlation between experimental binding affinities and calculated binding energies is derived, with a Pearson’s correlation coefficient rp = 0.928. The aromatic Phe−4Tyr substitution can considerably improve peptide binding potency by 8.7-fold, whereas the non-aromatic substitutions at each of t-stacking and p-stacking or both can largely impair the peptide affinity by 20.7-fold (Phe−4Ala), 82.1-fold (Trp−1Ala) and 110.7-fold (Trp−1Ala/Phe−4Ala).

Structure, stability and specificity of the binding of ssDNA and ssRNA with proteins.

Recognition of single-stranded DNA (ssDNA) or single-stranded RNA (ssRNA) is important for many fundamental cellular functions. A variety of single-stranded DNA-binding proteins (ssDBPs) and single-stranded RNA-binding proteins (ssRBPs) have evolved that bind ssDNA and ssRNA, respectively, with varying degree of affinities and specificities to form complexes. Structural studies of these complexes provide key insights into their recognition mechanism. However, computational modeling of the specific recognition process and to predict the structure of the complex is challenging, primarily due to the heterogeneity of their binding energy landscape and the greater flexibility of ssDNA or ssRNA compared with double-stranded nucleic acids. Consequently, considerably fewer computational studies have explored interactions between proteins and single-stranded nucleic acids compared with protein interactions with double-stranded nucleic acids. Here, we report a newly developed energy-based coarse-grained model to predict the structure of ssDNA–ssDBP and ssRNA–ssRBP complexes and to assess their sequence-specific interactions and stabilities. We tuned two factors that can modulate specific recognition: base–aromatic stacking strength and the flexibility of the single-stranded nucleic acid. The model was successfully applied to predict the binding conformations of 12 distinct ssDBP and ssRBP structures with their cognate ssDNA and ssRNA partners having various sequences. Estimated binding energies agreed well with the corresponding experimental binding affinities. Bound conformations from the simulation showed a funnel-shaped binding energy distribution where the native-like conformations corresponded to the energy minima. The various ssDNA–protein and ssRNA–protein complexes differed in the balance of electrostatic and aromatic energies. The lower affinity of the ssRNA–ssRBP complexes compared with the ssDNA–ssDBP complexes stems from lower flexibility of ssRNA compared to ssDNA, which results in higher rate constants for the dissociation of the complex (koff) for complexes involving the former.

In Silico Discovery and Pharmacological Analysis of Potential Ligands Targeting the Serotonin Transporter

The serotonin transporter (SERT) protein is responsible for terminating serotonergic signaling by actively removing serotonin from the synapse through a sodium‐ and chloride‐dependent uptake mechanism into the neuron. The SERT is the primary target of the widely prescribed antidepressant drugs known as the Selective Serotonin Reuptake Inhibitors (SSRIs), which include Citalopram, Fluoxetine, and Paroxetine. Consequently, interest in SERT as a target for drug discovery is substantial due to the limited efficacy and side effects of current antidepressants. Computational approaches have previously been used with homology models of SERT with success, and the recent publication of the human SERT crystal structures has provided an opportunity to revisit virtual screening. For this study, our approach was to modify the SERT structure (PDB id. 5I71), which had 13 mutations introduced to crystallize it, back to its wildtype structure by replacing the 13 mutations with the wildtype amino acids using the Molecular Operating Environment (MOE) software. Also, the backbone of the protein was relaxed using molecular dynamics with ACEMD software. The S1 binding pocket was identified using the Site Finder module in MOE, and the Maybridge Hit Discoverer library, which contained 45,000 compounds, was virtually screened through the Dock module that incorporated a pharmacophore filter on aspartic acid 98. The screen scored and ranked 33,000 compounds of which 450 were selected as the top hits based on predicted affinity (S‐score under −17). The 450 compounds were then narrowed down to 44 by filtering the dataset with the Lipinski rule of 5, and predicted affinity (S‐score < −18). The 44 compounds were subjected to fingerprinting and chemical similarity analysis using R, Python, and Pearl scripts so the compounds could be placed into various groups. Of the 44 compounds, 10 were chosen for further pharmacological analysis based on the available chemical space. These compounds represent the most unique structures found within the dataset as measured by their Tanimoto coefficient in relationship to each compound utilizing scripts in the MayaChemTools software collection. The identified compounds had characteristics ranging from 6 to 10 hydrogen bond acceptors, 1 to 4 hydrogen bond donors, and a −6 to 4.87 cLogP. A stably transfected SERT cell line was used to pharmacologically evaluate the compounds using in vitro assays. Radioligand competition assays were used to determine experimental binding affinity to the SERT. Efficacy as an inhibitor was determined using an uptake inhibition assay using radioactive serotonin.In conclusion, computational methods were utilized to identify potential ligands targeted at the serotonin transporter. From this virtual screen, 10 compounds were selected based on their chemical uniqueness to be evaluated pharmacologically.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Effect of set up protocols on the accuracy of alchemical free energy calculation over a set of ACK1 inhibitors.

Hit-to-lead virtual screening frequently relies on a cascade of computational methods that starts with rapid calculations applied to a large number of compounds and ends with more expensive computations restricted to a subset of compounds that passed initial filters. This work focuses on set up protocols for alchemical free energy (AFE) scoring in the context of a Docking–MM/PBSA–AFE cascade. A dataset of 15 congeneric inhibitors of the ACK1 protein was used to evaluate the performance of AFE set up protocols that varied in the steps taken to prepare input files (using previously docked and best scored poses, manual selection of poses, manual placement of binding site water molecules). The main finding is that use of knowledge derived from X-ray structures to model binding modes, together with the manual placement of a bridging water molecule, improves the R2 from 0.45 ± 0.06 to 0.76 ± 0.02 and decreases the mean unsigned error from 2.11 ± 0.08 to 1.24 ± 0.04 kcal mol-1. By contrast a brute force automated protocol that increased the sampling time ten-fold lead to little improvements in accuracy. Besides, it is shown that for the present dataset hysteresis can be used to flag poses that need further attention even without prior knowledge of experimental binding affinities.

Molecular Dynamics Simulations of Wild Type and Mutants of SAPAP in Complexed with Shank3.

Specific interactions between scaffold protein SH3 and multiple ankyrin repeat domains protein 3 (Shank3) and synapse-associated protein 90/postsynaptic density-95–associated protein (SAPAP) are essential for excitatory synapse development and plasticity. In a bunch of human neurological diseases, mutations on Shank3 or SAPAP are detected. To investigate the dynamical and thermodynamic properties of the specific binding between the N-terminal extended PDZ (Post-synaptic density-95/Discs large/Zonaoccludens-1) domain (N-PDZ) of Shank3 and the extended PDZ binding motif (E-PBM) of SAPAP, molecular dynamics simulation approaches were used to study the complex of N-PDZ with wild type and mutated E-PBM peptides. To compare with the experimental data, 974QTRL977 and 966IEIYI970 of E-PBM peptide were mutated to prolines to obtain the M4P and M5P system, respectively. Conformational analysis shows that the canonical PDZ domain is stable while the βN extension presents high flexibility in all systems, especially for M5P. The high flexibility of βN extension seems to set up a barrier for the non-specific binding in this area and provide the basis for specific molecular recognition between Shank3 and SAPAP. The wild type E-PBM tightly binds to N-PDZ during the simulation while loss of binding is observed in different segments of the mutated E-PBM peptides. Energy decomposition and hydrogen bonds analysis show that M4P mutations only disrupt the interactions with canonical PDZ domain, but the interactions with βN1′ remain. In M5P system, although the interactions with βN1′ are abolished, the binding between peptide and the canonical PDZ domain is not affected. The results indicate that the interactions in the two-binding site, the canonical PDZ domain and the βN1′ extension, contribute to the binding between E-PBM and N-PDZ independently. The binding free energies calculated by MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) are in agreement with the experimental binding affinities. Most of the residues on E-PBM contribute considerably favorable energies to the binding except A963 and D964 in the N-terminal. The study provides information to understand the molecular basis of specific binding between Shank3 and SAPAP, as well as clues for design of peptide inhibitors.

Docking with AutoDock4.

AutoDock is one of the most popular receptor-ligand docking simulation programs. It was first released in the early 1990s and is in continuous development and adapted to specific protein targets. AutoDock has been applied to a wide range of biological systems. It has been used not only for protein-ligand docking simulation but also for the prediction of binding affinity with good correlation with experimental binding affinity for several protein systems. The latest version makes use of a semi-empirical force field to evaluate protein-ligand binding affinity and for selecting the lowest energy pose in docking simulation. AutoDock4.2.6 has an arsenal of four search algorithms to carry out docking simulation including simulated annealing, genetic algorithm, and Lamarckian algorithm. In this chapter, we describe a tutorial about how to perform docking with AutoDock4. We focus our simulations on the protein target cyclin-dependent kinase 2.

Ligand Binding Free Energies with Adaptive Water Networks: Two-Dimensional Grand Canonical Alchemical Perturbations.

Computational methods to calculate ligand binding affinities are increasing in popularity, due to improvements in simulation algorithms, computational resources, and easy-to-use software. However, issues can arise in relative ligand binding free energy simulations if the ligands considered have different active site water networks, as simulations are typically performed with a predetermined number of water molecules (fixed N ensembles) in preassigned locations. If an alchemical perturbation is attempted where the change should result in a different active site water network, the water molecules may not be able to adapt appropriately within the time scales of the simulations—particularly if the active site is occluded. By combining the grand canonical ensemble (μVT) to sample active site water molecules, with conventional alchemical free energy methods, the water network is able to dynamically adapt to the changing ligand. We refer to this approach as grand canonical alchemical perturbation (GCAP). In this work we demonstrate GCAP for two systems; Scytalone Dehydratase (SD) and Adenosine A2A receptor. For both systems, GCAP is shown to perform well at reproducing experimental binding affinities. Calculating the relative binding affinities with a naïve, conventional attempt to solvate the active site illustrates how poor results can be if proper consideration of water molecules in occluded pockets is neglected. GCAP results are shown to be consistent with time-consuming double decoupling simulations. In addition, by obtaining the free energy surface for ligand perturbations, as a function of both the free energy coupling parameter and water chemical potential, it is possible to directly deconvolute the binding energetics in terms of protein–ligand direct interactions and protein binding site hydration.

Accurate Calculation of Relative Binding Free Energies between Ligands with Different Net Charges.

In drug discovery programs, modifications that change the net charge of the ligands are often considered to improve the binding potency and solubility, or to address other ADME/Tox problems. Accurate calculation of the binding free-energy changes associated with charge-changing perturbations remains a great challenge of central importance in computational drug discovery. The finite size effects associated with periodic boundary condition and lattice summation employed in common molecular dynamics simulations introduce artifacts in the electrostatic potential energy calculations, which need to be carefully handled for accurate free-energy calculations between systems with different net charges. The salts in the buffer solution of experimental binding affinity assays also have a strong effect on the binding free energies between charged species, which further complicates the modeling of the charge-changing perturbations. Here, we extend our free-energy perturbation (FEP) algorithm, which has been extensively applied to many drug discovery programs for relative binding free-energy calculations between ligands with the same net charge (charge-conserving perturbation), to enable charge-changing perturbations. We have investigated three different approaches to correct the finite size effects and tested them on 10 protein targets and 31 charge-changing perturbations. We have found that all three methods are able to successfully eliminate the box-size dependence of calculated binding free energies associated with brute force FEP. Moreover, inclusion of salts matching the ionic strength of experimental buffer solution significantly improves the calculated binding free energies. For ligands with multiple possible protonation states, we applied the p Ka correction to account for the ionization equilibrium of the ligands and the results are significantly improved. Finally, the calculated binding free energies from these methods agree with each other, and also agree well with the experimental results. The root-mean-square error between the calculated binding free energies and experimental data is 1.1 kcal/mol, which is on par with the accuracy of charge-conserving perturbations. We anticipate that the outstanding accuracy demonstrated here across a broad range of target classes may have significant implications for drug discovery projects, where charge-changing modifications must be considered.

In vitro and in silico perspectives on estrogenicity of tanshinones from Salvia miltiorrhiza

This work aims to investigate the structure-activity relationship for binding and activation of human estrogen receptor α ligand binding domain (hERα-LBD) with tanshinones by a combination of in vitro and in silico approaches. The recombinant hERα-LBD was expressed in E. coli strain. The direct binding interactions of tanshinones with hERα-LBD and their ERα agonistic potency were investigated by fluorescence polarization (FP) and reporter gene assays, respectively. FP assay suggested that the tested tanshinones can bind to hERα-LBD as affinity ligands. Tanshinones acted as agonists of hERα as demonstrated by transactivation of estrogen response element (ERE) in transiently transfected MCF-7 cells and by molecular docking of these compounds into the hydrophobic binding pocket of hERα-LBD. Interestingly, comparison of the calculated binding energies versus Connolly solvent-excluded volume and experimental binding affinities showed a good correlation. This work may provide insight into chemical and pharmacological characterization of novel bioactive compounds from Salvia miltiorrhiza.

Thermodynamic Insight into the Effects of Water Displacement and Rearrangement upon Ligand Modifications using Molecular Dynamics Simulations.

Computational methods, namely molecular dynamics (MD) simulations in combination with inhomogeneous fluid solvation theory (IFST) were used to retrospectively investigate various cases of ligand structure modifications that led to the displacement of binding site water molecules. Our findings are that water displacement per se is energetically unfavorable in the discussed examples, and that it is merely the fine balance between change in protein-ligand interaction energy, ligand solvation free energies, and binding site solvation free energies that determine if water displacement is favorable or not. We furthermore evaluated if we can reproduce experimental binding affinities by a computational approach combining changes in solvation free energies with changes in protein-ligand interaction energies and entropies. In two of the seven cases, this estimation led to large errors, implying that accurate predictions of relative binding free energies based on solvent thermodynamics is challenging. Nevertheless, MD simulations can provide insight regarding which water molecules can be targeted for displacement.

Predicting opioid receptor binding affinity of pharmacologically unclassified designer substances using molecular docking.

Opioids represent a highly-abused and highly potent class of drugs that have become a significant threat to public safety. Often there are little to no pharmacological and toxicological data available for new, illicitly used and abused opioids, and this has resulted in a growing number of serious adverse events, including death. The large influx of new synthetic opioids permeating the street-drug market, including fentanyl and fentanyl analogs, has generated the need for a fast and effective method to evaluate the risk a substance poses to public safety. In response, the US FDA’s Center for Drug Evaluation and Research (CDER) has developed a rapidly-deployable, multi-pronged computational approach to assess a drug’s risk to public health. A key component of this approach is a molecular docking model to predict the binding affinity of biologically uncharacterized fentanyl analogs to the mu opioid receptor. The model was validated by correlating the docking scores of structurally diverse opioids with experimentally determined binding affinities. Fentanyl derivatives with sub-nanomolar binding affinity at the mu receptor (e.g. carfentanil and lofentanil) have significantly lower binding scores, while less potent fentanyl derivatives have increased binding scores. The strong correlation between the binding scores and the experimental binding affinities suggests that this approach can be used to accurately predict the binding strength of newly identified fentanyl analogs at the mu receptor in the absence of in vitro data and may assist in the temporary scheduling of those substances that pose a risk to public safety.

Use of the Multilayer Fragment Molecular Orbital Method to Predict the Rank Order of Protein-Ligand Binding Affinities: A Case Study Using Tankyrase 2 Inhibitors.

In computational drug discovery, ranking a series of compound analogues in the order that is consistent with the experimental binding affinities remains a challenge. Many of the computational methods available for evaluating binding affinities have adopted molecular mechanics (MM)-based force fields, although they cannot completely describe protein–ligand interactions. By contrast, quantum mechanics (QM) calculations play an important role in understanding the protein–ligand interactions; however, their huge computational costs hinder their application in drug discovery. In this study, we have evaluated the ability to rank the binding affinities of tankyrase 2 ligands by combining both MM and QM calculations. Our computational approach uses the protein–ligand binding energies obtained from a cost-effective multilayer fragment molecular orbital (MFMO) method combined with the solvation energy obtained from the MM-Poisson–Boltzmann/surface area (MM-PB/SA) method to predict the binding affinity. This approach enabled us to rank tankyrase 2 inhibitor analogues, outperforming several MM-based methods, including rescoring by molecular docking and the MM-PB/SA method alone. Our results show that this computational approach using the MFMO method is a promising tool for predicting the rank order of the binding affinities of inhibitor analogues.

Influence of various force fields in estimating the binding affinity of acetylcholinesterase inhibitors using fast pulling of ligand scheme

Acetylcholinesterase (AChE) is considered as one of the most favored drug targets for Alzheimer’s disease. The effects of different force fields (FFs) on ranking affinity of acetylcholinesterase inhibitors were obtained using the fast pulling of ligand (FPL) method in steered-molecular dynamics (SMD) simulations. GROMOS, AMBER, CHARMM, and OPLS-AA FFs were investigated in this work. The pulling work derived with GROMOS FF has the strongest correlation and smallest error compared with experimental binding affinity. Moreover, the CPU consumption in the calculations using GROMOS FF is the lowest, which could allow us to rank affinity of a large number of AChE ligands.

Dissecting the Innate Immune Recognition of Opioid Inactive Isomer (+)-Naltrexone Derived Toll-like Receptor 4 (TLR4) Antagonists.

The opioid inactive isomer (+)-naltrexone is one of the rare Toll-like receptor 4 (TLR4) antagonists with good blood-brain barrier (BBB) permeability, which is a lead with promising potential for treating neuropathic pain and drug addiction. (+)-Naltrexone targets the lipopolysaccharides (LPS) binding pocket of myeloid differentiation protein 2 (MD-2) and blocks innate immune TLR4 signaling. However, the details of the molecular interactions of (+)-naltrexone and its derivatives with MD-2 are not fully understood, which hinders the ligand-based drug discovery. Herein, in silico and in vitro assays were performed to elucidate the innate immune recognition of the opioid inactive (+)-isomers. The results showed that the conserved LPS binding pocket of MD-2 accommodated these opioid inactive (+)-isomers. The calculated binding free energies of (+)-naltrexone and its derivatives in complex with MD-2 correlated well with their experimental binding affinities and TLR4 antagonistic activities. Hydrophobic residues in the MD-2 cavity interacted directly with these (+)-naltrexone based TLR4 antagonists and principally participated in ligand binding. Increasing the hydrophobicity of substituted group at N-17 improved its TLR4 antagonistic activity, while charged groups disfavored the binding with MD-2. Molecular dynamics (MD) simulations showed the binding of (+)-naltrexone or its derivatives to MD-2 stabilized the "collapsed" conformation of MD-2, consequently blocking the binding and signaling of TLR4. Thermodynamics and dynamic analysis showed the topology of substituted group at N-17 of (+)-naltrexone affected the binding with MD-2 and TLR4 antagonistic activity. This study provides a molecular insight into the innate immune recognition of opioid inactive (+)-isomers, which would be of great help for the development of next-generation of (+)-opioid based TLR4 antagonists.

Binding stability of peptides on major histocompatibility complex class I proteins: role of entropy and dynamics

Prediction of peptide binding on specific human leukocyte antigens (HLA) has long been studied with successful results. We herein describe the effects of entropy and dynamics by investigating the binding stabilities of 10 nanopeptides on various HLA Class I alleles using a theoretical model based on molecular dynamics simulations. The fluctuational entropies of the peptides are estimated over a temperature range of 310–460 K. The estimated entropies correlate well with experimental binding affinities of the peptides: peptides that have higher binding affinities have lower entropies compared to non-binders, which have significantly larger entropies. The computation of the entropies is based on a simple model that requires short molecular dynamics trajectories and allows for approximate but rapid determination. The paper draws attention to the long neglected dynamic aspects of peptide binding, and provides a fast computation scheme that allows for rapid scanning of large numbers of peptides on selected HLA antigens, which may be useful in defining the right peptides for personal immunotherapy.