Receptor-based 3D QSAR analysis of estrogen receptor ligands – merging the accuracy of receptor-based alignments with the computational efficiency of ligand-based methods

One of the major challenges in computational approaches to drug design is the accurate prediction of binding affinity of biomolecules. In the present study several prediction methods for a published set of estrogen receptor ligands are investigated and compared. The binding modes of 30 ligands were determined using the docking program AutoDock and were compared with available X-ray structures of estrogen receptor-ligand complexes. On the basis of the docking results an interaction energy-based model, which uses the information of the whole ligand-receptor complex, was generated. Several parameters were modified in order to analyze their influence onto the correlation between binding affinities and calculated ligand-receptor interaction energies. The highest correlation coefficient (r ² = 0.617, q ² _LOO = 0.570) was obtained considering protein flexibility during the interaction energy evaluation. The second prediction method uses a combination of receptor-based and 3D quantitative structure-activity relationships (3D QSAR) methods. The ligand alignment obtained from the docking simulations was taken as basis for a comparative field analysis applying the GRID/GOLPE program. Using the interaction field derived with a water probe and applying the smart region definition (SRD) variable selection, a significant and robust model was obtained (r ² = 0.991, q ² _LOO = 0.921). The predictive ability of the established model was further evaluated by using a test set of six additional compounds. The comparison with the generated interaction energy-based model and with a traditional CoMFA model obtained using a ligand-based alignment (r ² = 0.951, q ² _LOO = 0.796) indicates that the combination of receptor-based and 3D QSAR methods is able to improve the quality of the underlying model.     

An efficient molecular docking using conformational space annealing

Molecular docking falls into the general category of global optimization problems because its main purpose is to find the most stable complex consisting of a receptor and its ligand. Conformational space annealing (CSA), a powerful global optimization method, is incorporated with the Tinker molecular modeling package to perform molecular docking simulations of six receptor-ligand complexes (3PTB, 1ULB, 2CPP, 1STP, 3CPA, and 1PPH) from the Protein Data Bank. In parallel, Monte Carlo with the minimization (MCM) method is also incorporated into the Tinker package for comparison. The energy function, consisting of electrostatic interactions, van der Waals interactions, and torsional energy terms, is calculated using the AMBER94 all-atom empirical force field. Rigid docking simulations for all six complexes and flexible docking simulations for three complexes (1STP, 3CPA, and 1PPH) are carried out using the CSA and the MCM methods. The simulation results show that the docking procedures using the CSA method generally find the most stable complexes as well as the native-like complexes more efficiently and accurately than those using the MCM, demonstrating that CSA is a promising search method for molecular docking problems.     

Bimodal Complexations of Steroids with Cyclodextrins by a Flexible Docking Algorithm

A flexible docking algorithm was developed for studying the inclusion complexes of cyclodextrins with steroids in aqueous solution by an optimization method and an empirical function. The function is used to estimate the binding free energy including intermolecular interaction energy, the conformational energy change, and the solvation energy. The bimodal complexations of twelve steroids in β- and γ-CD cavities were studied by the algorithm. For the two orientations of the guests in the cavity, the possible binding regions were investigated, and the lowest energies for the inclusion complexes in the binding regions were obtained. The stability constant for each orientation was estimated from the optimized energy components by a quantitative model. Therefore, the preferential orientations of the guests were found out from the results finally.This revised version was published online in July 2005 with a corrected issue number.     

Application of automated docking to the binding of naphthalenes to βCD in water: correlation with spectrofluorimetric data

Automated semi-rigid docking has been explored as an alternative approach for the theoretical study of the inclusion complexes with cyclodextrins. To this purpose we have chosen as a model for the binding to βCD some naphthalene derivatives (naphthalene, 2-ethylnaphthalene, 2-acetylnaphthalene, 1-naphthyl acetate, 2-naphthyl acetate and 1-naphthol). For comparison purposes, the binding constants in water and the associated thermodynamic parameters have been obtained under the same experimental conditions by steady-state fluorescence spectroscopy. The calculations of the automated docking regarding the topology of the guest inside the cavity produce a cluster of structures that qualitatively agrees with fluorescence results and literature data. However, the predicted values of the free energy of binding are lower than the experimental ones by ca. −10 kJ mol⁻¹, and very close to the experimental enthalpy of binding deduced from the temperature dependence of the association constants. The differences are ascribed mainly to the assumption of rigidity of the CD into the auto-docking scheme.     

Docking macromolecules with flexible segments

We address a major obstacle to macromolecular docking algorithms by presenting a new method that takes into account the induced conformational adjustment of flexible loops situated at a protein/macromolecule interface. The method, MC2, is based on a multiple copy representation of the loops, coupled with a Monte Carlo conformational search of the relative position of the macromolecules and their side chain conformations. The selection of optimal loop conformations takes place during Monte Carlo cycling by the iterative adjustment of the weight of each copy. We describe here the parameterization of the method and trials on a protein-DNA complex of known 3-D structure, involving the Drosophila prd paired domain protein and its target oligonucleotide Wenqing, X. et al., Cell 1995, 80, 639. We demonstrate that our algorithm can correctly configure and position this protein, despite its relatively complex interactions with both grooves of DNA.     

Two Key Amino Acids Variant of α-l-arabinofuranosidase from Bacillus subtilis Str. 168 with Altered Activity for Selective Conversion Ginsenoside Rc to Rd

α-l-arabinofuranosidase is a subfamily of glycosidases involved in the hydrolysis of l-arabinofuranosidic bonds, especially in those of the terminal non-reducing arabinofuranosyl residues of glycosides, from which efficient glycoside hydrolases can be screened for the transformation of ginsenosides. In this study, the ginsenoside Rc-hydrolyzing α-l-arabinofuranosidase gene, BsAbfA, was cloned from Bacilus subtilis, and its codons were optimized for efficient expression in E. coli BL21 (DE3). The recombinant protein BsAbfA fused with an N-terminal His-tag was overexpressed and purified, and then subjected to enzymatic characterization. Site-directed mutagenesis of BsAbfA was performed to verify the catalytic site, and the molecular mechanism of BsAbfA catalyzing ginsenoside Rc was analyzed by molecular docking, using the homology model of sequence alignment with other β-glycosidases. The results show that the purified BsAbfA had a specific activity of 32.6 U/mg. Under optimal conditions (pH 5, 40 °C), the kinetic parameters K_m of BsAbfA for pNP-α-Araf and ginsenoside Rc were 0.6 mM and 0.4 mM, while the K_cat/K_m were 181.5 s⁻¹ mM⁻¹ and 197.8 s⁻¹ mM⁻¹, respectively. More than 90% of ginsenoside Rc could be transformed by 12 U/mL purified BsAbfA at 40 °C and pH 5 in 24 h. The results of molecular docking and site-directed mutagenesis suggested that the E173 and E292 variants for BsAbfA are important in recognizing ginsenoside Rc effectively, and to make it enter the active pocket to hydrolyze the outer arabinofuranosyl moieties at C₂₀ position. These remarkable properties and the catalytic mechanism of BsAbfA provide a good alternative for the effective biotransformation of the major ginsenoside Rc into Rd.     

Adsorption of carbon monoxide and oxygen on transition metal surfaces: A comparative study of the results from electron spectroscopy and theoretical calculations

Results of investigations on the adsorption of CO andO ₂ on transition metal surfaces by employinguv and x-ray photoelectron spectroscopy and electron energy loss spectroscopy (eels) are presented. Results of molecular orbital calculations on adsorbed CO and O₂ are also discussed. Some of the interesting aspects discussed are, satellites in the O(ls) region due to adsorbed CO, vibrationaleels of adsorbed O₂ and dissociation energy profiles of adsorbed O₂ on clean surfaces as well as surfaces covered with potassium or presorbed atomic oxygen. Contribution No 245 from the Solid State and Structural Chemistry Unit.     

Specific empirical free energy function for automated docking of carbohydrates to proteins

We present an automated docking protocol specifically optimized to predict the structure and affinity of a protein-carbohydrate complex. A scoring function was developed based on a training set of 30 protein-carbohydrate complexes of known structure and affinity. Combinations of several models for hydrogen bonding, torsional entropy loss, and solvation were tested for their ability to fit the training set data, and the best model was used with AutoDock. The electrostatic empirical coefficient is larger than in a previously obtained model using a training set comprised of various types of protein-ligand complexes, indicating that electrostatic interactions play a more important role in determining the affinity between a carbohydrate and a protein. The differences in the relative weighting of the empirical coefficients in the model yields predicted free energies for the training set with a standard error of 1.403 kcal/mol. The new scoring function was tested on 17 Aspergillus niger glucoamylase inhibitors for which binding energies had been determined experimentally. Free energies of complex formation were predicted with a residual standard error of 1.101 kcal/mol. The new scoring function therefore provides a robust method for predicting free energies of formation and optimal conformations of carbohydrate-protein complexes.