Stochastic pairwise alignments and scoring methods for comparative protein structure modeling

Despite recent advances in fold recognition algorithms that identify template structures with distant homology to the target sequence, the quality of the target-template alignment can be a major problem for distantly related proteins in comparative modeling. Here we report for the first time on the use of ensembles of pairwise alignments obtained by stochastic backtracking as a means to improve three-dimensional comparative protein models. In every one of the 35 cases, the ensemble produced by the program probA resulted in alignments that were closer to the structural alignment than those obtained from the optimal alignment. In addition, we examined the lowest energy structure among these ensembles from four different structural assessment methods and compared these with the optimal and structural alignment model. The structural assessment methods consisted of the DFIRE, DOPE, and ProsaII statistical potential energies and the potential energy from the CHARMM protein force field coupled to a Generalized Born implicit solvent model. The results demonstrate that the generation of alignment ensembles through stochastic backtracking using probA combined with one of the statistical potentials for assessing three-dimensional structures can be used to improve comparative models.     

FLAME: a program to flexibly align molecules

Herein, we describe a method to flexibly align molecules (FLAME = FLexibly Align MolEcules). FLAME aligns two molecules by first finding maximum common pharmacophores between them using a genetic algorithm. The resulting alignments are then subjected to simultaneous optimizations of their internal energies and an alignment score. The utility of the method in pairwise alignment, multiple molecule flexible alignment, and database searching was examined. For pairwise alignment, two carboxypeptidase ligands (Protein Data Bank codes and ), two estrogen receptor ligands ( and ), and two thrombin ligands ( and ) were used as test sets. Alignments generated by FLAME starting from CONCORD structures compared very well to the X-ray structures (average root-mean-square deviation = 0.36 A) even without further minimization in the presence of the protein. For multiple flexible alignments, five structurally diverse D3 receptor ligands were used as a test set. The FLAME alignment automatically identified three common pharmacophores: a base, a hydrogen-bond acceptor, and a hydrophobe/aromatic ring. The best alignment was then used to search the MDDR database. The search results were compared to the results using atom pair and Daylight fingerprint similarity. A similar database search comparison was also performed using estrogen receptor modulators. In both cases, hits identified by FLAME were structurally more diverse compared to those from the atom pair and Daylight fingerprint methods.     

Efficient overlay of small organic molecules using 3D pharmacophores

Aligning and overlaying two or more bio-active molecules is one of the key tasks in computational drug discovery and bio-activity prediction. Especially chemical-functional molecule characteristics from the view point of a macromolecular target represented as a 3D pharmacophore are the most interesting similarity measure when describing and analyzing macromolecule-ligand interaction. In this study, a novel approach for aligning rigid three-dimensional molecules according to their chemical-functional pharmacophoric features is presented and compared to the overlay of experimentally determined poses in a comparable macromolecule coordinate frame. The presented approach identifies optimal chemical feature pairs using distance and density characteristics obtained by correlating pharmacophoric geometries and thus proves to be faster than existing combinatorial alignment methods and creates more reasonable alignments than pure atom-based methods. Examples will be provided to demonstrate the feasibility, speed and intuitiveness of this method.     

Fluorescein labeling of estrogen for application of fluorescence polarization binding assays for antibody and receptor

The fluorescence polarization binding assay (FPBA) using fluorescein-labeled estrogen tracer is a homogeneous assay applicable to both estrogen antibody and estrogen receptor-binding assays. Two estrogen-ethylendiamine fluoresceinthiobamyl (E-EDF) tracers were synthesized; estrogen-6-EDF (E-6-F) derived from 6-ketoestradiol 6-(o-carboxymethyl) oxime and estrogen-17-EDF (E-17-F) was from 17β-estradiol 17-hemisuccinate. In both FPBAs using antibody and receptor, E-6-F tracer (Rf_365nm=0.58) showed a better binding response than E-17-F (Rf_365nm=0.70) indicating that the 17-position of estrogen seems to play an essential role as a binding site for antibody or receptor. In the optimized conditions of FPBA for E₂ using E-6-F tracer, antibody binding (K_d=9.4×10⁻⁹ M) is 50 times sensitive than receptor binding (K_d=4.6×10⁻⁸ M). Binding responses of estrogen and its related chemicals by FPBA indicate that antibody binding assay is able to screen the structural similarity of estrogen showing some response with methyltestosterone (K_i=2.1×10⁻⁵ M). On the other hand, the receptor assay is able to screen for estrogenic chemicals such as tamoxifen (K_i=4.5×10⁻⁹ M) and diethylstilbesterol (K_i=8.1×10⁻⁷ M). Therefore, E-6-F tracer is useful as a tracer for FPBA that is able to screen for chemicals structurally similar to estrogen using antibody, and that is able to screen for chemicals functionally similar to estrogen using receptor binding assay.     

Molecular docking and 3D-QSAR studies on the glucocorticoid receptor antagonistic activity of hydroxylated polychlorinated biphenyls

The glucocorticoid receptor (GR) antagonistic activities of hydroxylated polychlorinated biphenyls (HO-PCBs) were recently characterised. To further explore the interactions between HO-PCBs and the GR, and to elucidate structural characteristics that influence the GR antagonistic activity of HO-PCBs, molecular docking and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed. Comparative molecular similarity indices analysis (CoMSIA) was performed using both ligand- and receptor-based alignment schemes. Results generated from the receptor-based model were found to be more satisfactory, with q² of 0.632 and r² of 0.931 compared with those from the ligand-based model. Some internal validation strategies (e.g. cross-validation analysis, bootstrapping analysis and Y-randomisation) and an external validation method were used respectively to further assess the stability and predictive ability of the derived model. Graphical interpretation of the model provided some insights into the structural features that affected the GR antagonistic activity of HO-PCBs. Molecular docking studies revealed that some key residues were critical for ligand–receptor interactions by forming hydrogen bonds (Glu540) and hydrophobic interactions with ligands (Ile539, Val543 and Trp577). Although CoMSIA sometimes depends on the alignment of the molecules, the information provided is beneficial for predicting the GR antagonistic activities of HO-PCB homologues and is helpful for understanding the binding mechanisms of HO-PCBs to GR.     

Detecting conserved secondary structures in RNA molecules using constrained structural alignment

Constrained sequence alignment has been studied extensively in the past. Different forms of constraints have been investigated, where a constraint can be a subsequence, a regular expression, or a probability matrix of symbols and positions. However, constrained structural alignment has been investigated to a much lesser extent. In this paper, we present an efficient method for constrained structural alignment and apply the method to detecting conserved secondary structures, or structural motifs, in a set of RNA molecules. The proposed method combines both sequence and structural information of RNAs to find an optimal local alignment between two RNA secondary structures, one of which is a query and the other is a subject structure in the given set. The method allows a biologist to annotate conserved regions, or constraints, in the query RNA structure and incorporates these regions into the alignment process to obtain biologically more meaningful alignment scores. A statistical measure is developed to assess the significance of the scores. Experimental results based on detecting internal ribosome entry sites in the RNA molecules of hepatitis C virus and Trypanosoma brucei demonstrate the effectiveness of the proposed method and its superiority over existing techniques.     

3D-QSAR Studies on 3-Substituted N-Benzhydryl-nortropane Analogs as Nociception Receptor Agonists

The nociceptin receptor(NOP) has been involved in multiple biological functions, including pain, anxiety, cough, substance abuse, cardiovascular control, and immunity. Thus, selective NOP agonists might have clinical potential for the treatment of related diseases. In the present work, three-dimensional quantitative structure-activity relationship(3D-QSAR) studies were performed on a series of 3-substituted N-benzhydryl-nortropane analogs as NOP agonists using comparative molecular field analysis(Co MFA) and comparative molecular similarity indices analysis(CoM SIA) techniques. The statistically significant models were obtained with 54 compounds in training set by ligand-based atom-by-atom matching alignment. The CoM FA model gave cross-validated coefficient(q2) value of 0.530 using 6 components, non-cross-validated(r2) value of 0.921 with estimated F value of 93.668, and standard error of estimate(SEE) of 0.185. The best Co MSIA model resulted in q2 = 0.592, r2 = 0.945, N = 10, SEE = 0.162, and F = 75.654, based on steric, electrostatic, hydrophobic and hydrogen bond acceptor fields. The predictive ability of the Co MFA and CoM SIA models was further validated using a test set of 18 molecules that were not included in the training set, which resulted in predictive correlation coefficients(r2pred) of 0.551 and 0.637, respectively. Moreover, the CoM FA and CoM SIA contour maps identified the features important for exhibiting potent binding affinities on NOP, and can thus serve as a useful guide for the design of potential NOP agonists.     

MARS: Computing Three-Dimensional Alignments for Multiple Ligands Using Pairwise Similarities

The three-dimensional (3D) superimposition of molecules of one biological target reflecting their relative bioactive orientation is key for several ligand-based drug design studies (e.g., QSAR studies, pharmacophore modeling). However, with the lack of sufficient ligand-protein complex structures, an experimental alignment is difficult or often impossible to obtain. Several computational 3D alignment tools have been developed by academic or commercial groups to address this challenge. Here, we present a new approach, MARS (Multiple Alignments by ROCS-based Similarity), that is based on the pairwise alignment of all molecules within the data set using the tool ROCS (Rapid Overlay of Chemical Structures). Each pairwise alignment is scored, and the results are captured in a score matrix. The ideal superimposition of the compounds in the set is then identified by the analysis of the score matrix building stepwise a superimposition of all molecules. The algorithm exploits similarities among all molecules in the data set to compute an optimal 3D alignment. This alignment tool presented here can be used for several applications, including pharmacophore model generation, 3D QSAR modeling, 3D clustering, identification of structural outliers, and addition of compounds to an already existing alignment. Case studies are shown, validating the 3D alignments for six different data sets.     

Ab initio molecular dynamics study of methanol adsorption on copper clusters

The preferential structures of small copper clusters Cun (n=2-9) and the adsorption of methanol molecules on these clusters are examined with first principles, molecular dynamics simulations. The results show that the copper clusters undergo systematic changes in bond length and bond order associated with altering their preferential structures from one-dimensional structures, to two-dimensional and three-dimensional structures. The results also indicate that low coordination number sites on the copper clusters are both the most favorable for methanol adsorption and have the greatest localization of electronic charge. The simulations predict that charge transfer between the neutral copper clusters and the incident methanol molecules is a key process by which adsorption is stabilized. Importantly, the changes in the dimensionality of the copper clusters do not significantly influence methanol adsorption.     

A reactivity pattern for discrimination of ER agonism and antagonism based on 3-D molecular attributes

Various models have been developed to predict the relative binding affinity (RBA) of chemicals to estrogen receptors (ER). These models can be used to prioritize chemicals for further tiered biological testing to assess the potential for endocrine disruption. One shortcoming of models predicting RBA has been the inability to distinguish potential receptor antagonism from agonism, and hence in vivo response. It has been suggested that steroid receptor antagonists are less compact than agonists; thus, ER binding of antagonists may prohibit proper alignment of receptor protein helices preventing subsequent transactivation. The current study tests the theory of chemical bulk as a defining parameter of antagonism by employing a 3-D structural approach for development of reactivity patterns for ER antagonists and agonists. Using a dataset of 23 potent ER ligands (16 agonists, 7 antagonists), molecular parameters previously found to be associated with ER binding affinity, namely global (E(HOMO)) and local (donor delocalizabilities and charges) electron donating ability of electronegative sites and steric distances between those sites, were found insufficient to discriminate ER antagonists from agonists. However, parameters related to molecular bulk, including solvent accessible surface and negatively charged Van der Waal's surface, provided reactivity patterns that were 100% successful in discriminating antagonists from agonists in the limited data set tested. The model also shows potential to discriminate pure antagonists from partial agonist/antagonist structures. Using this exploratory model it is possible to predict additional chemicals for their ability to bind but inactivate the ER, providing a further tool for hypothesis testing to elucidate chemical structural characteristics associated with estrogenicity and anti-estrogenicity.     

A reactivity pattern for discrimination of ER agonism and antagonism based on 3-D molecular attributes

Various models have been developed to predict the relative binding affinity (RBA) of chemicals to estrogen receptors (ER). These models can be used to prioritize chemicals for further tiered biological testing to assess the potential for endocrine disruption. One shortcoming of models predicting RBA has been the inability to distinguish potential receptor antagonism from agonism, and hence in vivo response. It has been suggested that steroid receptor antagonists are less compact than agonists; thus, ER binding of antagonists may prohibit proper alignment of receptor protein helices preventing subsequent transactivation. The current study tests the theory of chemical bulk as a defining parameter of antagonism by employing a 3-D structural approach for development of reactivity patterns for ER antagonists and agonists. Using a dataset of 23 potent ER ligands (16 agonists, 7 antagonists), molecular parameters previously found to be associated with ER binding affinity, namely global ( E HOMO ) and local (donor delocalizabilities and charges) electron donating ability of electronegative sites and steric distances between those sites, were found insufficient to discriminate ER antagonists from agonists. However, parameters related to molecular bulk, including solvent accessible surface and negatively charged Van der Waal's surface, provided reactivity patterns that were 100% successful in discriminating antagonists from agonists in the limited data set tested. The model also shows potential to discriminate pure antagonists from partial agonist/antagonist structures. Using this exploratory model it is possible to predict additional chemicals for their ability to bind but inactivate the ER, providing a further tool for hypothesis testing to elucidate chemical structural characteristics associated with estrogenicity and anti-estrogenicity.     

A novel method for superimposing molecules and receptor mapping

Superposition of molecules on a three-dimensional computer graphic display is an efficient means to compare three-dimensional molecular structures. Biologically active molecules, which are presumed to bind to the same receptor site, are thought to have common structural features. But, it is the physical and chemical properties arranged spatially through chemical structures that are important for specific binding to a receptor. Therefore, for the purpose of studies on biological activities, molecules should be superposed to those properties, not to the atomic positions as in the traditional methods. We have developed a program system for realizing this new concept. The concept stands on the general perceptions of organic chemists about hydrogen bondings and chemical isosterisms. The 'goodness of fit' values, which are estimated in realtime on the basis of spatial similarity of those properties between molecules, are displayed and updated throughout the superposing process. This program can construct a receptor cavity model and provide with the cavity size and shape, surface electrostatic potentials, hydrogen bonding sites and so on, by using all information supplied by the superposed molecules. This model can be modified by further superposing of another molecule. These constructed models would be of help for rational drug design, when the receptor structures are not yet known.     

A structure-based approach to understanding somatostatin receptor-4 agonism (sst4)

It has been reported that somatostatin receptor subtypes 4 and 5 would be high-impact templates for homology modeling if their 3D structures became available. We have generated a homology model of the somatostatin receptor subtype 4 (sst4), using the newest active state β(2) adrenoreceptor crystal structure, and subsequently docked a variety of agonists into the model-built receptor to elucidate the binding modes of reported agonists. Using experimental restraints, we were able to explain observed activity profiles. We propose two binding modes that can consistently explain findings for high-affinity agonists and reason why certain structures display low affinities for the receptor.     

Computational studies of the x-ray scattering properties of laser aligned stilbene

The enhancement of the x-ray scattering signal from partially aligned molecular samples is investigated. The alignment properties of the studied molecular system are modeled based on the method of laser alignment. With the advances in the area of laser alignment of molecules, the application of this sample manipulation technique promises a great potential for x-ray scattering measurements. Preferential alignment of molecules in an otherwise amorphous sample leads to constructive interference and thus increases the scattering intensity. This enhances the structural information encoded in the scattering images and enables improved resolution in studies of reaction dynamics, as in this work is shown for the example of the photo-isomerization of stilbene. We demonstrate that the scattering signal is strongly influenced by the alignment axis. Even the most basic one-dimensional alignment offers significant improvement compared to the structural information provided by a randomly oriented sample. Although the signal is sensitive to the uncertainty in the alignment angle, it offers encouraging results even at realistic alignment uncertainties.