Receptor-specific scoring functions derived from quantum chemical models improve affinity estimates for in-silico drug discovery

The adaptation of forcefield-based scoring function to specific receptors remains an important challenge for in-silico drug discovery. Here we compare binding energies of forcefield-based scoring functions with models that are reparameterized on the basis of large-scale quantum calculations of the receptor. We compute binding energies of eleven ligands to the human estrogen receptor subtype alpha (ERalpha) and four ligands to the human retinoic acid receptor of isotype gamma (RARgamma). Using the FlexScreen all-atom receptor-ligand docking approach, we compare docking simulations parameterized by quantum-mechanical calculation of a large protein fragment with purely forcefield-based models. The use of receptor flexibility in the FlexScreen permits the treatment of all ligands in the same receptor model. We find a high correlation between the classical binding energy obtained in the docking simulation and quantum mechanical binding energies and a good correlation with experimental affinities R=0.81 for ERalpha and R=0.95 for RARgamma using the quantum derived scoring functions. A significant part of this improvement is retained, when only the receptor is treated with quantum-based parameters, while the ligands are parameterized with a purely classical model.     

2-Amino-4,6-diarylpyridines as novel ligands for the estrogen receptor.

We have prepared a novel series of 2-amino-4,6-diarylpyridines that function as ligands of estrogen receptor alpha (ERalpha) and estrogen receptor beta (ERbeta). These compounds bind to both ERalpha and ERbeta with a modest selectivity for the alpha subtype. The most potent of these analogues, compound 19, has a K(i)=20nM at ERalpha. These molecules represent a novel template for designing potentially useful ligands for the estrogen receptor.     

Testing a flexible-receptor docking algorithm in a model binding site

Sampling receptor flexibility is challenging for database docking. We consider a method that treats multiple flexible regions of the binding site independently, recombining them to generate different discrete conformations. This algorithm scales linearly rather than exponentially with the receptor's degrees of freedom. The method was first evaluated for its ability to identify known ligands of a hydrophobic cavity mutant of T4 lysozyme (L99A). Some 200000 molecules of the Available Chemical Directory (ACD) were docked against an ensemble of cavity conformations. Surprisingly, the enrichment of known ligands from among a much larger number of decoys in the ACD was worse than simply docking to the apo conformation alone. Large decoys, accommodated in the larger cavity conformations sampled in the ensemble, were ranked better than known small ligands. The calculation was redone with an energy correction term that considered the cost of forming the larger cavity conformations. Enrichment improved, as did the balance between high-ranking large and small ligands. In a second retrospective test, the ACD was docked against a conformational ensemble of thymidylate synthase. Compared to docking against individual enzyme conformations, the flexible receptor docking approach improved enrichment of known ligands. Including a receptor conformational energy weighting term improved enrichment further. To test the method prospectively, the ACD database was docked against another cavity mutant of lysozyme (L99A/M102Q). A total of 18 new compounds predicted to bind this polar cavity and to change its conformation were tested experimentally; 14 were found to bind. The bound structures for seven ligands were determined by X-ray crystallography. The predicted geometries of these ligands all corresponded to the observed geometries to within 0.7A RMSD or better. Significant conformational changes of the cavity were observed in all seven complexes. In five structures, part of the observed accommodations were correctly predicted; in two structures, the receptor conformational changes were unanticipated and thus never sampled. These results suggest that although sampling receptor flexibility can lead to novel ligands that would have been missed when docking a rigid structure, it is also important to consider receptor conformational energy.     

Synthesis of a novel fluorescent probe for estrogen receptor

A novel estradiol-mimetic fluorescent probe 5 was synthesized from diethylstilbestrol (DES, 1), which is useful for probing estrogen receptor (ERalpha), a prognostic indicator of estrogen-dependent cancers, and for developing a homogeneous fluorescence polarization (FP) assay to identify the ligands of estrogen receptor.     

Ligands differentially modulate the protein interactions of the human estrogen receptors alpha and beta

The interactions of human estrogen receptor subtypes ERalpha and ERbeta with DNA and a 210 amino acid residue fragment of the coactivator protein SRC-1 bearing three nuclear receptor interaction motifs were investigated quantitatively using fluorescence anisotropy in the presence of agonist and antagonist ligands. ERalpha and ERbeta were found to bind in a similar manner to DNA, and both salt and temperature affected the affinity and/or stoichiometry of these interactions. The agonist ligands estradiol, estrone and estriol did not modify the binding of ERalpha to the fluorescein-labeled target estrogen response element. However, in the case of ERbeta, these ligands led to the formation of some higher-order protein-DNA complexes and a small decrease in affinity. The partial agonist 4-hydroxytamoxifen had little effect on either ER subtype, whereas the pure antagonist ICI 182,780 led to the cooperative formation of protein-DNA complexes of higher order than dimer, as further demonstrated by competition experiments and gel mobility-shift assays. In addition to DNA binding, the interaction of both ER subtypes with the Alexa488-labeled SRC-1 coactivator fragment was investigated by fluorescence anisotropy. The agonist ligands estrone, estradiol, estriol, genistein and ethynyl estradiol exhibited distinct capacities for inducing the recruitment of SRC-1 that were not correlated with their affinity for the receptor. Moreover, estrone and genistein exhibited subtype specificity in that they induced SRC-1 recruitment to ERbeta with much higher efficiency than in the case of ERalpha. The differential coactivator recruitment capacities of the ER agonists and their receptor subtype coactivator recruitment specificity may be linked to the molecular structure of the agonists with respect to their interactions with a specific histidine residue located at the back of the ligand-binding pocket. Altogether, these quantitative in vitro studies of ER interactions reveal the complex energetic and stoichiometric consequences of changes in the chemical structures of these proteins and their ligands.     

Fluorous tolerance of the estrogen receptor alpha as probed by 11-polyfluoroalkylestradiol derivatives

The concern of this work was to try to delineate factors, inherent to fluorination, susceptible to influence estradiol binding to the estrogen receptor alpha (ERalpha). For this purpose, fluorinated chains were linked at 11beta position of the steroid (i.e., C(6)F(13), CH(2)CH(2)C(4)F(9), CH(2)CH(2)C(8)F(17)). Relative binding affinity (RBA) for ERalpha of these compounds and of other related fluorinated derivatives was compared to those of non-fluorinated analogs. Despite being relatively well accepted by the receptor, investigated compounds exhibited lower RBA values at 0 degrees C than their non-fluorinated counterparts. Nevertheless, heavily fluorinated chains were tolerated in so far as they are not too long (C-4) and insulated from the steroidal core by a two methylene spacer unit. Increase of the temperature of our binding assay (25 degrees C) failed to change the RBA values of two selected polyfluorohexyl derivatives while it drastically enhanced the value of the corresponding non-fluorinated analogs. Rigidity of the chain induced by fluorination as well as the oleophilic (fluorophobic) nature of the estradiol binding cavity of ERalpha is proposed to explain these properties.     

Complex-type-dependent scoring functions in protein-protein docking

A major challenge in the field of protein-protein docking is to discriminate between the many wrong and few near-native conformations, i.e. scoring. Here, we introduce combinatorial complex-type-dependent scoring functions for different types of protein-protein complexes, protease/inhibitor, antibody/antigen, enzyme/inhibitor and others. The scoring functions incorporate both physical and knowledge-based potentials, i.e. atomic contact energy (ACE), the residue pair potential (RP), electrostatic and van der Waals' interactions. For different type complexes, the weights of the scoring functions were optimized by the multiple linear regression method, in which only top 300 structures with ligand root mean square deviation (L_RMSD) less than 20 A from the bound (co-crystallized) docking of 57 complexes were used to construct a training set. We employed the bound docking studies to examine the quality of the scoring function, and also extend to the unbound (separately crystallized) docking studies and extra 8 protein-protein complexes. In bound docking of the 57 cases, the first hits of protease/inhibitor cases are all ranked in the top 5. For the cases of antibody/antigen, enzyme/inhibitor and others, there are 17/19, 5/6 and 13/15 cases with the first hits ranked in the top 10, respectively. In unbound docking studies, the first hits of 9/17 protease/inhibitor, 6/19 antibody/antigen, 1/6 enzyme/inhibitor and 6/15 others' complexes are ranked in the top 10. Additionally, for the extra 8 cases, the first hits of the two protease/inhibitor cases are ranked in the top for the bound and unbound test. For the two enzyme/inhibitor cases, the first hits are ranked 1st for bound test, and the 119th and 17th for the unbound test. For the others, the ranks of the first hits are the 1st for the bound test and the 12th for the 1WQ1 unbound test. To some extent, the results validated our divide-and-conquer strategy in the docking study, which might hopefully shed light on the prediction of protein-protein interactions.     

Evaluation of protein docking predictions using Hex 3.1 in CAPRI rounds 1 and 2

This article describes and reviews our efforts using Hex 3.1 to predict the docking modes of the seven target protein-protein complexes presented in the CAPRI (Critical Assessment of Predicted Interactions) blind docking trial. For each target, the structure of at least one of the docking partners was given in its unbound form, and several of the targets involved large multimeric structures (e.g., Lactobacillus HPr kinase, hemagglutinin, bovine rotavirus VP6). Here we describe several enhancements to our original spherical polar Fourier docking correlation algorithm. For example, a novel surface sphere smothering algorithm is introduced to generate multiple local coordinate systems around the surface of a large receptor molecule, which may be used to define a small number of initial ligand-docking orientations distributed over the receptor surface. High-resolution spherical polar docking correlations are performed over the resulting receptor surface patches, and candidate docking solutions are refined by using a novel soft molecular mechanics energy minimization procedure. Overall, this approach identified two good solutions at rank 5 or less for two of the seven CAPRI complexes. Subsequent analysis of our results shows that Hex 3.1 is able to place good solutions within a list of 相似文献   

Exploration of a binding mode of indole amide analogues as potent histone deacetylase inhibitors and 3D-QSAR analyses

Docking simulations and three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses were conducted on a series of indole amide analogues as potent histone deacetylase inhibitors. The studies include comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Selected ligands were docked into the active site of human HDAC1. Based on the docking results, a novel binding mode of indole amide analogues in the human HDAC1 catalytic core is presented, and enzyme/inhibitor interactions are discussed. The indole amide group is located in the open pocket, and anchored to the protein through a pair of hydrogen bonds with Asp99 O-atom and amide NH group on ligand. Based on the binding mode, predictive 3D-QSAR models were established, which had conventional r2 and cross-validated coefficient values (r(cv)2) up to 0.982 and 0.601 for CoMFA and 0.954 and 0.598 for CoMSIA, respectively. A comparison of the 3D-QSAR field contributions with the structural features of the binding site showed good correlation between the two analyses. The results of 3D-QSAR and docking studies validate each other and provided insight into the structural requirements for activity of this class of molecules as HDAC inhibitors. The CoMFA and CoMSIA PLS contour maps and MOLCAD-generated active site electrostatic, lipophilicity, and hydrogen-bonding potential surface maps, as well as the docking studies, provided good insights into inhibitor-HDAC interactions at the molecular level. Based on these results, novel molecules with improved activity can be designed.     

A general approach for developing system-specific functions to score protein-ligand docked complexes using support vector inductive logic programming

Despite the increased recent use of protein-ligand and protein-protein docking in the drug discovery process due to the increases in computational power, the difficulty of accurately ranking the binding affinities of a series of ligands or a series of proteins docked to a protein receptor remains largely unsolved. This problem is of major concern in lead optimization procedures and has lead to the development of scoring functions tailored to rank the binding affinities of a series of ligands to a specific system. However, such methods can take a long time to develop and their transferability to other systems remains open to question. Here we demonstrate that given a suitable amount of background information a new approach using support vector inductive logic programming (SVILP) can be used to produce system-specific scoring functions. Inductive logic programming (ILP) learns logic-based rules for a given dataset that can be used to describe properties of each member of the set in a qualitative manner. By combining ILP with support vector machine regression, a quantitative set of rules can be obtained. SVILP has previously been used in a biological context to examine datasets containing a series of singular molecular structures and properties. Here we describe the use of SVILP to produce binding affinity predictions of a series of ligands to a particular protein. We also for the first time examine the applicability of SVILP techniques to datasets consisting of protein-ligand complexes. Our results show that SVILP performs comparably with other state-of-the-art methods on five protein-ligand systems as judged by similar cross-validated squares of their correlation coefficients. A McNemar test comparing SVILP to CoMFA and CoMSIA across the five systems indicates our method to be significantly better on one occasion. The ability to graphically display and understand the SVILP-produced rules is demonstrated and this feature of ILP can be used to derive hypothesis for future ligand design in lead optimization procedures. The approach can readily be extended to evaluate the binding affinities of a series of protein-protein complexes.     

A yeast sensor of ligand binding.

We describe a biosensor that reports the binding of small-molecule ligands to proteins as changes in growth of temperature-sensitive yeast. The yeast strains lack dihydrofolate reductase (DHFR) and are complemented by mouse DHFR containing a ligand-binding domain inserted in a flexible loop. Yeast strains expressing two ligand-binding domain fusions, FKBP12-DHFR and estrogen receptor-alpha (ERalpha)-DHFR, show increased growth in the presence of their corresponding ligands. We used this sensor to identify mutations in residues of ERalpha important for ligand binding, as well as mutations generally affecting protein activity or expression. We also tested the sensor against a chemical array to identify ligands that bind to FKBP12 or ERalpha. The ERalpha sensor was able to discriminate among estrogen analogs, showing different degrees of growth for the analogs that correlated with their relative binding affinities (RBAs). This growth assay provides a simple and inexpensive method to select novel ligands and ligand-binding domains.     

Virtual screening against metalloenzymes for inhibitors and substrates

Molecular docking uses the three-dimensional structure of a receptor to screen databases of small molecules for potential ligands, often based on energetic complementarity. For many docking scoring functions, which calculate nonbonded interactions, metalloenzymes are challenging because of the partial covalent nature of metal-ligand interactions. To investigate how well molecular docking can identify potential ligands of metalloenzymes using a "standard" scoring function, we have docked the MDL Drug Data Report (MDDR), a functionally annotated database of 95,000 small molecules, against the X-ray crystal structures of five metalloenzymes. These enzymes included three zinc proteases, the nickel analogue of an iron enzyme, and a molybdenum metalloenzyme. The ability of the docking program to retrospectively enrich the annotated ligands as high-scoring hits for each enzyme and to calculate proper geometries was evaluated. In all five systems, the annotated ligands within the MDDR were enriched at least 20 times over random. To test the approach prospectively, a sixth target, the zinc beta-lactamase from Bacteroides fragilis, was screened against the fragment-like subset of the ZINC database. We purchased and tested 15 compounds from among the top 50 top-ranked ligands from docking, and found 5 inhibitors with apparent K(i) values less than 120 microM, the best of which was 2 microM. A more ambitious test still was predicting actual substrates for a seventh target, a Zn-dependent phosphotriesterase from Pseudomonas diminuta. Screening the Available Chemicals Directory (ACD) identified 25 thiophosphate esters as potential substrates within the top 100 ranked compounds. Eight of these, all previously uncharacterized for this enzyme, were acquired and tested, and all were confirmed experimentally as substrates. These results suggest that a simple, noncovalent scoring function may be used to identify inhibitors of at least some metalloenzymes.