Assessment and acceleration of binding energy calculations for protein–ligand complexes by the fragment molecular orbital method

In the field of drug discovery, it is important to accurately predict the binding affinities between target proteins and drug applicant molecules. Many of the computational methods available for evaluating binding affinities have adopted molecular mechanics‐based force fields, although they cannot fully describe protein–ligand interactions. A noteworthy computational method in development involves large‐scale electronic structure calculations. Fragment molecular orbital (FMO) method, which is one of such large‐scale calculation techniques, is applied in this study for calculating the binding energies between proteins and ligands. By testing the effects of specific FMO calculation conditions (including fragmentation size, basis sets, electron correlation, exchange‐correlation functionals, and solvation effects) on the binding energies of the FK506‐binding protein and 10 ligand complex molecule, we have found that the standard FMO calculation condition, FMO2‐MP2/6‐31G(d), is suitable for evaluating the protein–ligand interactions. The correlation coefficient between the binding energies calculated with this FMO calculation condition and experimental values is determined to be R = 0.77. Based on these results, we also propose a practical scheme for predicting binding affinities by combining the FMO method with the quantitative structure–activity relationship (QSAR) model. The results of this combined method can be directly compared with experimental binding affinities. The FMO and QSAR combined scheme shows a higher correlation with experimental data (R = 0.91). Furthermore, we propose an acceleration scheme for the binding energy calculations using a multilayer FMO method focusing on the protein–ligand interaction distance. Our acceleration scheme, which uses FMO2‐HF/STO‐3G:MP2/6‐31G(d) at R_int = 7.0 Å, reduces computational costs, while maintaining accuracy in the evaluation of binding energy. © 2015 Wiley Periodicals, Inc.     

Ligand‐Induced Conformational Changes in HSP90 Monitored Time Resolved and Label Free—Towards a Conformational Activity Screening for Drug Discovery

Investigation of protein–ligand interactions is crucial during early drug‐discovery processes. ATR‐FTIR spectroscopy can detect label‐free protein–ligand interactions with high spatiotemporal resolution. Here we immobilized, as an example, the heat shock protein HSP90 on an ATR crystal. This protein is an important molecular target for drugs against several diseases including cancer. With our novel approach we investigated a ligand‐induced secondary structural change. Two specific binding modes of 19 drug‐like compounds were analyzed. Different binding modes can lead to different efficacy and specificity of different drugs. In addition, the k_obs values of ligand dissociation were obtained. The results were validated by X‐ray crystallography for the structural change and by SPR experiments for the dissociation kinetics, but our method yields all data in a single and simple experiment.     

Ligand–Receptor Binding Affinities from Saturation Transfer Difference (STD) NMR Spectroscopy: The Binding Isotherm of STD Initial Growth Rates

The direct evaluation of dissociation constants (K_D) from the variation of saturation transfer difference (STD) NMR spectroscopy values with the receptor–ligand ratio is not feasible due to the complex dependence of STD intensities on the spectral properties of the observed signals. Indirect evaluation, by competition experiments, allows the determination of K_D, as long as a ligand of known affinity is available for the protein under study. Herein, we present a novel protocol based on STD NMR spectroscopy for the direct measurements of receptor–ligand dissociation constants (K_D) from single‐ligand titration experiments. The influence of several experimental factors on STD values has been studied in detail, confirming the marked impact on standard determinations of protein–ligand affinities by STD NMR spectroscopy. These factors, namely, STD saturation time, ligand residence time in the complex, and the intensity of the signal, affect the accumulation of saturation in the free ligand by processes closely related to fast protein–ligand rebinding and longitudinal relaxation of the ligand signals. The proposed method avoids the dependence of the magnitudes of ligand STD signals at a given saturation time on spurious factors by constructing the binding isotherms using the initial growth rates of the STD amplification factors, in a similar way to the use of NOE growing rates to estimate cross relaxation rates for distance evaluations. Herein, it is demonstrated that the effects of these factors are cancelled out by analyzing the protein–ligand association curve using STD values at the limit of zero saturation time, when virtually no ligand rebinding or relaxation takes place. The approach is validated for two well‐studied protein–ligand systems: the binding of the saccharides GlcNAc and GlcNAcβ1,4GlcNAc (chitobiose) to the wheat germ agglutinin (WGA) lectin, and the interaction of the amino acid L ‐tryptophan to bovine serum albumin (BSA). In all cases, the experimental K_D measured under different experimental conditions converged to the thermodynamic values. The proposed protocol allows accurate determinations of protein–ligand dissociation constants, extending the applicability of the STD NMR spectroscopy for affinity measurements, which is of particular relevance for those proteins for which a ligand of known affinity is not available.     

How to deal with weak interactions in noncovalent complexes analyzed by electrospray mass spectrometry: Cyclopeptidic inhibitors of the nuclear receptor coactivator 1-STAT6

Mass spectrometry, and especially electrospray ionization, is now an efficient tool to study noncovalent interactions between proteins and inhibitors. It is used here to study the interaction of some weak inhibitors with the NCoA-1/STAT6 protein with K _D values in the μM range. High signal intensities corresponding to some nonspecific electrostatic interactions between NCoA-1 and the oppositely charged inhibitors were observed by nanoelectrospray mass spectrometry, due to the use of high ligand concentrations. Diverse strategies have already been developed to deal with nonspecific interactions, such as controlled dissociation in the gas phase, mathematical modeling, or the use of a reference protein to monitor the appearance of nonspecific complexes. We demonstrate here that this last methodology, validated only in the case of neutral sugar-protein interactions, i.e., where dipole-dipole interactions are crucial, is not relevant in the case of strong electrostatic interactions. Thus, we developed a novel strategy based on half-maximal inhibitory concentration (IC₅₀) measurements in a competitive assay with readout by nanoelectrospray mass spectrometry. IC₅₀ values determined by MS were finally converted into dissociation constants that showed very good agreement with values determined in the liquid phase using a fluorescence polarization assay.     

Mapping Protein–Ligand Interactions in the Gas Phase Using a Functional Group Replacement Strategy. Comparison of CID and BIRD Activation Methods

Intermolecular interactions in the gaseous ions of two protein–ligand complexes, a single chain antibody (scFv) and its trisaccharide ligand (α-D-Galp-(1→2)-[α-D-Abep-(1→3)]-α-Manp-OCH_3, L1) and streptavidin homotetramer (S₄) and biotin (B), were investigated using a collision-induced dissociation (CID)-functional group replacement (FGR) strategy. CID was performed on protonated ions of a series of structurally related complexes based on the (scFv + L1) and (S₄?+?4B) complexes, at the +10 and +13 charge states, respectively. Intermolecular interactions were identified from decreases in the collision energy required to dissociate 50 % of the reactant ion (Ec₅₀) upon modification of protein residues or ligand functional groups. For the (scFv + L1)¹⁰⁺ ion, it was found that deoxygenation of L1 (at Gal C3 and C6 and Man C4 and C6) or mutation of His101 (to Ala) resulted in a decrease in Ec₅₀ values. These results suggest that the four hydroxyl groups and His101 participate in intermolecular H-bonds. These findings agree with those obtained using the blackbody infrared radiative dissociation (BIRD)-FGR method. However, the CID-FGR method failed to reveal the relative strengths of the intermolecular interactions or establish Man C4 OH and His101 as an H-bond donor/acceptor pair. The CID-FGR method correctly identified Tyr43, but not Ser27, Trp79, and Trp120, as a stabilizing contact in the (S₄?+?4B)¹³⁺ ion. In fact, mutation of Trp79 and Trp120 led to an increase in the Ec₅₀ value. Taken together, these results suggest that the CID-FGR method, as implemented here, does not represent a reliable approach for identifying interactions in the gaseous protein–ligand complexes.      

Fast,metadynamics‐based method for prediction of the stereochemistry‐dependent relative free energies of ligand–receptor interactions

The computational approach applicable for the molecular dynamics (MD)‐based techniques is proposed to predict the ligand–protein binding affinities dependent on the ligand stereochemistry. All possible stereoconfigurations are expressed in terms of one set of force‐field parameters [stereoconfiguration‐independent potential (SIP)], which allows for calculating all relative free energies by only single simulation. SIP can be used for studying diverse, stereoconfiguration‐dependent phenomena by means of various computational techniques of enhanced sampling. The method has been successfully tested on the β₂‐adrenergic receptor (β₂‐AR) binding the four fenoterol stereoisomers by both metadynamics simulations and replica‐exchange MD. Both the methods gave very similar results, fully confirming the presence of stereoselective effects in the fenoterol‐β₂‐AR interactions. However, the metadynamics‐based approach offered much better efficiency of sampling which allows for significant reduction of the unphysical region in SIP. © 2014 Wiley Periodicals, Inc.     

Carbohydrate–Lipid Interactions: Affinities of Methylmannose Polysaccharides for Lipids in Aqueous Solution

The interactions between 3‐O‐methyl‐mannose polysaccharides (MMPs), extracted from Mycobacterium smegmatis (consisting of a mixture of MMP‐10, ‐11, ‐12 and ‐13) or obtained by chemical synthesis (MMP‐5_s, ‐8_s, ‐11_s and ‐14_s), and linear saturated and unsaturated fatty acids (FAs), and a commercial mixture of naphthenic acids (NAs) in aqueous solution at 25 °C and pH 8.5 were quantified by electrospray ionization mass spectrometry (ESI‐MS). Association constants (K_a) for MMP binding to four FAs (myristic acid, palmitic acid, stearic acid and trans‐parinaric acid) were measured by using an indirect ESI‐MS assay, the “proxy protein” method. The K_a values are in the 10⁴–10⁵ M ^?1 range and, based on results obtained for the binding of the synthetic MMPs with palmitic acid, increase with the size of the carbohydrate. Notably, the measured affinity of the extracted MMPs for trans‐parinaric acid is two orders of magnitude smaller than the reported value, which was determined by using a fluorescence assay. Using a newly developed competitive binding assay, referred to as the “proxy protein/proxy ligand” ESI‐MS method, it was shown that MMPs bind specifically to NAs in aqueous solution, with apparent affinities of approximately (5×10⁴) M ^?1 for the mixture of NAs tested. This represents the first demonstration that MMPs can bind to hydrophobic species more complex than those containing linear alkyl/alkenyl chains. Moreover, the approach developed here represents a novel method for probing carbohydrate–lipid interactions.     

Mechanistic Insight into Nanomolar Binding of Multivalent Neoglycopeptides to Wheat Germ Agglutinin

Multivalent carbohydrate–protein interactions are frequently involved in essential biological recognition processes. Accordingly, multivalency is often also exploited for the design of high‐affinity lectin ligands aimed at the inhibition of such processes. In a previous study (D. Schwefel et al., J. Am. Chem. Soc. 2010 , 132, 8704–8719) we identified a tetravalent cyclopeptide‐based ligand with nanomolar affinity to the model lectin wheat germ agglutinin (WGA). To unravel the structural features of this ligand required for high‐affinity binding to WGA, we synthesized a series of cyclic and linear neoglycopeptides that differ in their conformational freedom as well as the number of GlcNAc residues. Combined evidence from isothermal titration calorimetry (ITC), enzyme‐linked lectin assays (ELLA), and dynamic light scattering (DLS) revealed different binding modes of tetra‐ and divalent ligands and that conformational preorganization of the ligands by cyclization is not a prerequisite for achieving high binding affinities. The high affinities of the tetravalent ligands rather stem from their ability to form crosslinks between several WGA molecules. The results illustrate that binding affinities and mechanisms are strongly dependent on the used multivalent system which offers opportunities to tune and control binding processes.     

Development of ANFIS technique for estimation of CO2 solubility in amino acids and study on impact of input parameters

ANFIS (Adaptive neuro fuzzy inference system) modeling of CO₂ capture using chemical absorbent was carried out in this study to correlate the solubility of CO₂ to the solvent and operational parameters. In the ANFIS model, the input parameters including temperature, pressure, and physio-chemical properties of the solvent were considered, while the loading of CO₂ in the absorbent was considered as the sole target output to be predicted by the model. Indeed, we developed a machine learning based model for predicting the CO₂ loading capacity in amino acid salt solutions as the chemical absorbent of carbon dioxide. This model uses a metaheuristic optimized ANFIS based on a wide range of amino acids. This study's novel part is the use of Differential Evolution (DE) and Firefly Algorithm (FA) metaheuristics in order to solve hyper-parameter tuning of ANFIS as an optimization problem based on differential evolution. Accordingly, the optimized ANFIS model has an R² score of 0.9520 for the test data and a score of 0.9841 for the training data. This indicates that the proposed model is both general and accurate in terms of its predictions for CO₂ loading in amino acid salt solutions. The MAPE and RMSE error rates are also 1.17E-01, respectively, while the MAPE error rate is 1.14E-01.     

Improved pose and affinity predictions using different protocols tailored on the basis of data availability

The D3R 2015 grand drug design challenge provided a set of blinded challenges for evaluating the applicability of our protocols for pose and affinity prediction. In the present study, we report the application of two different strategies for the two D3R protein targets HSP90 and MAP4K4. HSP90 is a well-studied target system with numerous co-crystal structures and SAR data. Furthermore the D3R HSP90 test compounds showed high structural similarity to existing HSP90 inhibitors in BindingDB. Thus, we adopted an integrated docking and scoring approach involving a combination of both pharmacophoric and heavy atom similarity alignments, local minimization and quantitative structure activity relationships modeling, resulting in the reasonable prediction of pose [with the root mean square deviation (RMSD) values of 1.75 Å for mean pose 1, 1.417 Å for the mean best pose and 1.85 Å for the mean all poses] and affinity (ROC AUC = 0.702 at 7.5 pIC50 cut-off and R = 0.45 for 180 compounds). The second protein, MAP4K4, represents a novel system with limited SAR and co-crystal structure data and little structural similarity of the D3R MAP4K4 test compounds to known MAP4K4 ligands. For this system, we implemented an exhaustive pose and affinity prediction protocol involving docking and scoring using the PLANTS software which considers side chain flexibility together with protein–ligand fingerprints analysis assisting in pose prioritization. This protocol through fares poorly in pose prediction (with the RMSD values of 4.346 Å for mean pose 1, 4.69 Å for mean best pose and 4.75 Å for mean all poses) and produced reasonable affinity prediction (AUC = 0.728 at 7.5 pIC50 cut-off and R = 0.67 for 18 compounds, ranked 1st among 80 submissions).     

Virtual screening for R-groups, including predicted pIC50 contributions, within large structural databases, using Topomer CoMFA

Multiple R-groups (monovalent fragments) are implicitly accessible within most of the molecular structures that populate large structural databases. R-group searching would desirably consider pIC50 contribution forecasts as well as ligand similarities or docking scores. However, R-group searching, with or without pIC50 forecasts, is currently not practical. The most prevalent and reliable source of pIC50 predictions, existing 3D-QSAR approaches, is also difficult and somewhat subjective. Yet in 25 of 25 trials on data sets on which a field-based 3D-QSAR treatment had already succeeded, substitution of objective (canonically generated) topomer poses for the original structure-guided manual alignments produced acceptable 3D-QSAR models, on average having almost equivalent statistical quality to the published models, and with negligible effort. Their overall pIC50 prediction error is 0.805, calculated as the average over these 25 topomer CoMFA models in the standard deviations of pIC50 predictions, derived from the 1109 possible "leave-out-one-R-group" (LOORG) pIC50 contributions. (This novel LOORG protocol provides a more realistic and stringent test of prediction accuracy than the customary "leave-out-one-compound" LOO approach.) The associated average predictive r(2) of 0.495 indicates a pIC50 prediction accuracy roughly halfway between perfect and useless. To assess the ability of topomer-CoMFA based virtual screening to identify "highly active" R-groups, a Receiver Operating Curve (ROC) approach was adopted. Using, as the binary criterion for a "highly active" R-group, a predicted pIC50 greater than the top 25% of the observed pIC50 range, the ROC area averaged across the 25 topomer CoMFA models is 0.729. Conventionally interpreted, the odds that a "highly active" R-group will indeed confer such a high pIC50 are 0.729/(1-0.729) or almost 3 to 1. To confirm that virtual screening within large collections of realized structures would provide a useful quantity and variety of R-group suggestions, combining shape similarity with the "highly active" pIC50, the 50 searches provided by these 25 models were applied to 2.2 million structurally distinct R-group candidates among 2.0 million structures within a ZINC database, identifying an average of 5705 R-groups per search, with the highest predicted pIC50 combination averaging 1.6 log units greater than the highest reported pIC50s.     

Weak Attractive Ligand–Polymer and Related Interactions in Catalysis and Reactivity: Impact,Applications, and Modeling

The notion of weak attractive ligand–polymer interactions is introduced, and its potential application, importance, and conceptual links with “cooperative” ligand–substrate interactions are discussed. Synthetic models of weak attractive ligand–polymer interactions are described, in which intramolecular weak C? H???F? C interactions (the existence of which remains contentious) have been detected by NMR spectroscopy and neutron and X‐ray diffraction experiments. These C? H???F? C interactions carry important implications for the design of catalysts for olefin polymerization, because they provide support for the practical feasibility of ortho‐F???H_β ligand–polymer contacts proposed for living Group 4 fluorinated phenoxyimine catalysts. The notion of weak attractive noncovalent interactions between an “active” ligand and the growing polymer chain is a novel concept in polyolefin catalysis.     

A Bayesian statistical approach of improving knowledge‐based scoring functions for protein–ligand interactions

Knowledge‐based scoring functions are widely used for assessing putative complexes in protein–ligand and protein–protein docking and for structure prediction. Even with large training sets, knowledge‐based scoring functions face the inevitable problem of sparse data. Here, we have developed a novel approach for handling the sparse data problem that is based on estimating the inaccuracies in knowledge‐based scoring functions. This inaccuracy estimation is used to automatically weight the knowledge‐based scoring function with an alternative, force‐field‐based potential (FFP) that does not rely on training data and can, therefore, provide an improved approximation of the interactions between rare chemical groups. The current version of STScore, a protein–ligand scoring function using our method, achieves a binding mode prediction success rate of 91% on the set of 100 complexes by Wang et al., and a binding affinity correlation of 0.514 with the experimentally determined affinities in PDBbind. The method presented here may be used with other FFPs and other knowledge‐based scoring functions and can also be applied to protein–protein docking and protein structure prediction. © 2014 Wiley Periodicals, Inc.     

Deep Learning and Structure-Based Virtual Screening for Drug Discovery against NEK7: A Novel Target for the Treatment of Cancer

NIMA-related kinase7 (NEK7) plays a multifunctional role in cell division and NLRP3 inflammasone activation. A typical expression or any mutation in the genetic makeup of NEK7 leads to the development of cancer malignancies and fatal inflammatory disease, i.e., breast cancer, non-small cell lung cancer, gout, rheumatoid arthritis, and liver cirrhosis. Therefore, NEK7 is a promising target for drug development against various cancer malignancies. The combination of drug repurposing and structure-based virtual screening of large libraries of compounds has dramatically improved the development of anticancer drugs. The current study focused on the virtual screening of 1200 benzene sulphonamide derivatives retrieved from the PubChem database by selecting and docking validation of the crystal structure of NEK7 protein (PDB ID: 2WQN). The compounds library was subjected to virtual screening using Auto Dock Vina. The binding energies of screened compounds were compared to standard Dabrafenib. In particular, compound 762 exhibited excellent binding energy of −42.67 kJ/mol, better than Dabrafenib (−33.89 kJ/mol). Selected drug candidates showed a reactive profile that was comparable to standard Dabrafenib. To characterize the stability of protein–ligand complexes, molecular dynamic simulations were performed, providing insight into the molecular interactions. The NEK7–Dabrafenib complex showed stability throughout the simulated trajectory. In addition, binding affinities, pIC50, and ADMET profiles of drug candidates were predicted using deep learning models. Deep learning models predicted the binding affinity of compound 762 best among all derivatives, which supports the findings of virtual screening. These findings suggest that top hits can serve as potential inhibitors of NEK7. Moreover, it is recommended to explore the inhibitory potential of identified hits compounds through in-vitro and in-vivo approaches.     

Evaluation and comparison of n‐alkyl chain and polar ligand bonded stationary phases for protein separation in reversed‐phase liquid chromatography

Protein retention is very sensitive to the change of solvent composition in reversed‐phase liquid chromatography for so called “on–off” mechanism, leading to difficulty in mobile phase optimization. In this study, a novel 3‐chloropropyl trichlorosilane ligand bonded column was prepared for protein separation. The differences in retention characteristics between the 3‐chloropropyl trichlorosilane ligand bonded column and n‐alkyl chain modified (C₂, C₄, C₈) stationary phases were elucidated by the retention equation . Retention parameters (a and c) of nine standard proteins with different molecular weights were calculated by using homemade software. Results showed that retention times of nine proteins were similar on four columns, but the 3‐chloropropyl trichlorosilane ligand bonded column obtained the lowest retention parameter values of larger proteins. It meant that their retention behavior affected by acetonitrile concentration would be different due to lower |c| values. More specifically, protein elution windows were broader, and retentions were less sensitive to the change of acetonitrile concentration on the 3‐chloropropyl trichlorosilane ligand bonded column than that on other columns. Meanwhile, the 3‐chloropropyl trichlorosilane ligand bonded column displayed distinctive selectivity for some proteins. Our results indicated that stationary phase with polar ligand provided potential solutions to the “on–off” problem and optimization in protein separation.     

Indolyl Linked Meta‐Substituted Benzylidenes as Novel Ligands: Synthesis,Biological Evaluation,and Molecular Docking Studies

Synthesis of indolyl linked benzylidene based meta‐substituted phenyl containing thiazolidinediones ( 4a – b ), rhodanine ( 5a – b ), and 1,3‐dicarbonyl based acyclic analogs of isoxazolidinediones ( 6a – 7b ) in an effort to develop novel α‐glucosidase inhibitors in the management of hyperglycemia for the treatment of type 2 diabetes is reported. The structure of all the novel synthesized compounds was confirmed through the spectral studies (LC–MS, ¹H‐NMR, ¹³C‐NMR, and FTIR). Comparative evaluation of these compounds revealed that the compound 5b showed maximum inhibitory potential against α‐amylase and α‐glucosidase giving an IC₅₀ value of 0.28 ± 0.01 μM. Furthermore, binding affinities in terms of G score values and hydrogen bond interactions between all the synthesized compounds and the AA residues in the active site of the protein (PDB code: 3TOP) to that of Acarbose (standard drug) were explored with the help of molecular docking studies. Compound 5b was considered as promising candidate of this series.     

Identifying Carbohydrate Ligands of a Norovirus P Particle using a Catch and Release Electrospray Ionization Mass Spectrometry Assay

Noroviruses (NoVs), the major cause of epidemic acute gastroenteritis, recognize human histo-blood group antigens (HBGAs), which are present as free oligosaccharides in bodily fluid or glycolipids and glycoproteins on the surfaces of cells. The subviral P particle formed by the protruding (P) domain of the NoV capsid protein serves as a useful model for the study NoV–HBGA interactions. Here, we demonstrate the application of a catch-and-release electrospray ionization mass spectrometry (CaR-ESI-MS) assay for screening carbohydrate libraries against the P particle to rapidly identify NoV ligands and potential inhibitors. Carbohydrate libraries of 50 and 146 compounds, which included 18 and 24 analogs of HBGA receptors, respectively, were screened against the P particle of VA387, a member of the predominant GII.4 NoVs. Deprotonated ions corresponding to the P particle bound to carbohydrates were isolated and subjected to collision-induced dissociation to release the ligands in their deprotonated forms. The released ligands were identified by ion mobility separation followed by mass analysis. All 13 and 16 HBGA ligands with intrinsic affinities >500 M^–1 were identified in the 50 and the 146 compound libraries, respectively. Furthermore, screening revealed interactions with a series of oligosaccharides with structures found in the cell wall of mycobacteria and human milk. The affinities of these newly discovered ligands are comparable to those of the HBGA receptors, as estimated from the relative abundance of released ligand ions.      

PLIMSTEX: a novel mass spectrometric method for the quantification of protein–ligand interactions in solution

Protein–ligand interactions by mass spectrometry, titration, and H/D exchange (PLIMSTEX) is a new mass spectrometric method for determining association constants and binding stoichiometry for interactions of proteins with various ligands, as well as for quantifying the conformational changes associated with ligand binding to proteins. The association constants determined with PLIMSTEX agree with literature values within a factor of six, establishing its validity for protein interactions involving metal ions, small organic molecules, peptides, and proteins. PLIMSTEX provides solution, not gas-phase, properties by taking advantage of ESI and MALDI mass spectrometry to measure accurately the mass of a protein as it undergoes amide H/D exchange. The approach sidesteps the problem of relating gas-phase abundances of the protein or protein–ligand complex ions to their solution concentrations. With on-column concentration and desalting, high picomole quantities of proteins are sufficient for reproducible mass detection, and the concentration of the protein can be as low as 10⁻⁸ M. It is amenable to different protein/ligand systems in physiologically relevant media. No specially labeled protein or ligand is needed. PLIMSTEX offers minimal perturbation of the binding equilibrium because it uses no denaturants, no additional spectroscopy or reaction probes, and no physical separation of ligand and protein during binding.     

Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2

Computational protein binding affinity prediction can play an important role in drug research but performing efficient and accurate binding free energy calculations is still challenging. In the context of phase 2 of the Drug Design Data Resource (D3R) Grand Challenge 2 we used our automated eTOX ALLIES approach to apply the (iterative) linear interaction energy (LIE) method and we evaluated its performance in predicting binding affinities for farnesoid X receptor (FXR) agonists. Efficiency was obtained by our pre-calibrated LIE models and molecular dynamics (MD) simulations at the nanosecond scale, while predictive accuracy was obtained for a small subset of compounds. Using our recently introduced reliability estimation metrics, we could classify predictions with higher confidence by featuring an applicability domain (AD) analysis in combination with protein–ligand interaction profiling. The outcomes of and agreement between our AD and interaction-profile analyses to distinguish and rationalize the performance of our predictions highlighted the relevance of sufficiently exploring protein–ligand interactions during training and it demonstrated the possibility to quantitatively and efficiently evaluate if this is achieved by using simulation data only.     

Host–Guest Interactions of Phosphorescent Molecular Tweezers Based on an Alkynylplatinum(II) Terpyridine System with Polyaromatic Hydrocarbons

Host–guest interactions of a molecular tweezer complex 1 with various planar organic molecules including polyaromatic hydrocarbons (PAHs) were investigated by 1D and 2D ¹H NMR spectroscopy, UV/Vis absorption and emission titration studies. 2D and DOSY NMR spectroscopies support the sandwiched binding mode based on 1:1 host–guest interactions. The binding constants (K_S) of complex 1 for various PAHs were determined by NMR titration studies and the values were found to span up to an order of 10⁴ M ^?1 for coronene to no observable interaction for benzene, indicating that the π‐surface area is important for such host–guest interactions. The substituent effect on the host–guest interaction based on the guest series of 9‐substituted anthracenes was also studied. In general, a stronger interaction was observed for the anthracene guest with electron‐donating groups, although steric and π‐conjugation factors cannot be completely excluded. The photophysical responses of complex 1 upon addition of various PAHs were measured by UV/Vis and emission titration studies. The UV/Vis absorption spectra were found to show a drop in absorbance of the metal‐to‐ligand charge‐transfer (MLCT) and ligand‐to‐ligand charge‐transfer (LLCT) admixture band upon addition of various guest molecules to 1 , whereas the emission behavior was found to change differently depending on the guest molecules, showing emission enhancement and/or quenching. It was found that emission quenching occurred either via energy transfer or electron transfer pathway or both, while emission enhancement was caused by the increase in rigidity of complex 1 as a result of host–guest interaction.