Quantitative Structure-affinity Relationship Research Articles

Factor Analysis Scales of Generalized Amino Acid Information as Applied in Predicting Interactions between the Human Amphiphysin‐1 SH3 Domains and Their Peptide Ligands

Factor analysis scales of generalized amino acid information (FASGAI) involving hydrophobicity, alpha-helix and beta-turn propensities, bulky properties, compositional characteristics, local flexibility, and electronic properties, was proposed to represent the structures of the decapeptides binding the human amphiphysin-1 SH3 domains. Parameters being responsible for the binding affinities were selected by genetic algorithm, and a quantitative structure-affinity relationship (QSAR) model by partial least square was established to predict the peptide-SH3 domain interactions. Diversified properties of the residues between P(2) and P(-3) (including P(2) and P(-3)) of the decapeptide (P(4)P(3)P(2)P(1)P(0)P(-1)P(-2)P(-3)P(-4)P(-5)) may contribute remarkable effect to the interactions between the SH3 domain and the decapeptide. Particularly, electronic properties of P(2) may provide relatively large positive contributions to the interactions, and reversely, hydrophobicity of P(2) may be largely negative to the interactions. These results showed that FASGAI vectors can well represent the structural characteristics of the decapeptides. Furthermore, the model obtained, which showed low computational complexity, correlated FASGAI descriptors with the binding affinities as well as that FASGAI vectors may also be applied in QSAR studies of peptides.

Modeling and prediction of binding affinities between the human amphiphysin SH3 domain and its peptide ligands using genetic algorithm‐Gaussian processes

In this article, we discuss the application of the Gaussian process (GP) and other statistical methods (PLS, ANN, and SVM) for the modeling and prediction of binding affinities between the human amphiphysin SH3 domain and its peptide ligands. Divided physicochemical property scores of amino acids, involving significant hydrogen bond, electronic, hydrophobic, and steric properties, was used to characterize the peptide structures, and quantitative structure-affinity relationship models were then constructed by PLS, ANN, SVM, and GP coupled with genetic algorithm-variable selection. The results show that: (i) since the significant flexibility and high complexity possessed in polypeptide structures, linear PLS method was incapable of fulfilling a satisfying behavior on SH3 domain binding peptide dataset; (ii) the overfitting involved in training process has decreased the predictive power of ANN model to some extent; (iii) both SVM and GP have a good performance for SH3 domain binding peptide dataset. Moreover, by combining linear and nonlinear terms in the covariance function, the GP is capable of handling linear and nonlinear-hybrid relationship, and which thus obtained a more stable and predictable model than SVM. Analyses of GP models showed that diversified properties contribute remarkable effect to the interactions between the SH3 domain and the peptides. Particularly, steric property and hydrophobicity of P(2), electronic property of P(0), and electronic property and hydrogen bond property of P(-3) in decapeptide (P(4)P(3)P(2)P(1)P(0)P(-1)P(-2)P(-3)P(-4)P(-5)) significantly contribute to the binding affinities of SH3 domain-peptide interactions.

Prediction of Binding Affinities between the Human Amphiphysin-1 SH3 Domain and Its Peptide Ligands Using Homology Modeling, Molecular Dynamics and Molecular Field Analysis

The SH3 domain of the human protein amphiphysin-1, which plays important roles in clathrin-mediated endocytosis, actin function and signaling transduction, can recognize peptide motif PXRPXR (X is any amino acid) with high affinity and specificity. We have constructed a complex structure of the amphiphysin-1 SH3 domain and a high-affinity peptide ligand PLPRRPPRA using homology modeling and molecular docking, which was optimized by molecular dynamics (MD). Three-dimensional quantitative structure-affinity relationship (3D-QSAR) analyses on the 200 peptides with known binding affinities to the amphiphysin-1 SH3 domain was then performed using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best CoMSIA model showed promising predictive power, giving good predictions for about 95% of the peptides in the test set (absolute prediction errors less than 1.0). It was used to validate peptide-SH3 binding structure and provide insight into the structural requirements for binding of peptides to SH3 domains. Finally, MD simulations were performed to analyze the interaction between the SH3 domain and another peptide GFPRRPPPRG that contains with the PXRPXsR (s represents residues with small side chains) motif. MD simulations demonstrated that the binding conformation of GFPRRPPPRG is quite different from that of PLPRRPPRAA especially the four residues at the C terminal, which may explain why the CoMSIA model cannot give good predictions on the peptides of the PXRPXsR motif. Because of its efficiency and predictive power, the 3D-QSAR model can be used as a scoring filter for predicting peptide sequences bound to SH3 domains.

8‐Substituted‐9‐deazaxanthines as Adenosine Receptor Ligands: Design, Synthesis and Structure‐Affinity Relationships at A2B.

AbstractFor Abstract see ChemInform Abstract in Full Text.

8-Substituted-9-deazaxanthines as adenosine receptor ligands: design, synthesis and structure-affinity relationships at A 2B

A number of 8-substituted-9-deazaxanthine derivatives (1,3-dialkyl-6-substituted-1 H-pyrrolo[3,2- d]pyrimidine-2,4(3 H,5 H)-diones) were prepared and tested for their antagonistic activity at the recombinant human adenosine receptors, in particular at the A 2B and A 2A receptor subtypes. Compounds endowed with micromolar to nanomolar binding affinities, but with poor A 2B/A 2A selectivity, were obtained. Preliminary quantitative structure–affinity relationships suggested that the binding potency at the A 2B receptor is mainly modulated by the electronic and lipophilic properties of the ligands.

Toward the Quantitative Prediction of T-Cell Epitopes: QSAR Studies on Peptides Having Affinity with the Class I MHC Molecular HLA-A*0201

It would be useful for vaccine development to develop a method of rapidly identifying peptide epitopes. In this paper, the empirical three-dimensional quantitative structure-affinity relationship (3D-QSAR) methods were used to study the relationship between the three dimensional structural parameters (the isotropic surface area, ISA, and the electronic charge index, ECI) of the HLA-A*0201 binding peptide and the HLA-A*0201/peptide binding affinities. A set of 102 peptides having affinity with the class I MHC HLA-A*0201 molecule was used as training set. A test set of 40 peptides was used to determine the predictive value of the models. The 3D-QSAR models yielded a q2 = 0.5724 and a high rpred2 = 0.6955. The standard regression coefficients indicated that the hydrophobic interactions played an important role in peptide-MHC molecule binding and predicted the specific amino acid residue essential at a certain position of the peptide. The approach tested in the current paper is highly complementary to many of the methods described in references and possesses good predictability. It is a rapid and convenient method to detect high affinity peptide epitopes.

Toward the Quantitative Prediction of T-Cell Epitopes: QSAR Studies on Peptides Having Affinity with the Class I MHC Molecular HLA-A*0201

It would be useful for vaccine development to develop a method of rapidly identifying peptide epitopes. In this paper, the empirical three-dimensional quantitative structure-affinity relationship (3D-QSAR) methods were used to study the relationship between the three dimensional structural parameters (the isotropic surface area, ISA, and the electronic charge index, ECI) of the HLA-A*0201 binding peptide and the HLA-A*0201/peptide binding affinities. A set of 102 peptides having affinity with the class I MHC HLA-A*0201 molecule was used as training set. A test set of 40 peptides was used to determine the predictive value of the models. The 3D-QSAR models yielded a q2 = 0.5724 and a high r2pred = 0.6955. The standard regression coefficients indicated that the hydrophobic interactions played an important role in peptide-MHC molecule binding and predicted the specific amino acid residue essential at a certain position of the peptide. The approach tested in the current paper is highly complementary to many of the methods described in references and possesses good predictability. It is a rapid and convenient method to detect high affinity peptide epitopes.

Coupling in silico and in vitro analysis of peptide-MHC binding: a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes.

The ability to define and manipulate the interaction of peptides with MHC molecules has immense immunological utility, with applications in epitope identification, vaccine design, and immunomodulation. However, the methods currently available for prediction of peptide-MHC binding are far from ideal. We recently described the application of a bioinformatic prediction method based on quantitative structure-affinity relationship methods to peptide-MHC binding. In this study we demonstrate the predictivity and utility of this approach. We determined the binding affinities of a set of 90 nonamer peptides for the MHC class I allele HLA-A*0201 using an in-house, FACS-based, MHC stabilization assay, and from these data we derived an additive quantitative structure-affinity relationship model for peptide interaction with the HLA-A*0201 molecule. Using this model we then designed a series of high affinity HLA-A2-binding peptides. Experimental analysis revealed that all these peptides showed high binding affinities to the HLA-A*0201 molecule, significantly higher than the highest previously recorded. In addition, by the use of systematic substitution at principal anchor positions 2 and 9, we showed that high binding peptides are tolerant to a wide range of nonpreferred amino acids. Our results support a model in which the affinity of peptide binding to MHC is determined by the interactions of amino acids at multiple positions with the MHC molecule and may be enhanced by enthalpic cooperativity between these component interactions.

Neuronal nicotinic acetylcholine receptor agonists: pharmacophores, evolutionary QSAR and 3D-QSAR models.

Neuronal nicotinic acetylcholine ion channel receptors (nAChRs) exist as several subtypes and are involved in a variety of functions and disorders of the central nervous system (CNS), such as Alzheimer's and Parkinson's diseases. The lack of reliable information on the 3D structure of nAChRs prompted us to focus efforts on pharmacophore and structure-affinity relationships (SAFIRs). The use of DISCO (DIStance COmparison) and Catalyst/HipHop led to the formulation of a pharmacophore that is made of three geometrically unrelated features: (i) an ammonium head involved in coulombic and/or H-bond interactions, (ii) a lone pair of a pyridine nitrogen or a carbonyl oxygen, as H-bond acceptor site, and (iii) a hydrophobic molecular region generally constituted by aliphatic cycles. The quantitative SAFIR (QSAFIR) study was carried out on about three hundred nicotinoid agonists, and coherent results were obtained from classical Hansch-type approach, 3D QSAFIRs, based on Comparative Molecular Field Analysis (CoMFA), and trade-off models generated by Multi-objective Genetic QSAR (MoQSAR), a novel evolutionary software that makes use of Genetic Programming (GP) and multi-objective optimization (MO). Within each congeneric series, Hansch-type equations revealed detrimental steric effects as the major factors modulating the receptor affinity, whereas CoMFA allowed us to merge progressively single-class models in a more global one, whose robustness was supported by crossvalidation, high prediction statistics and satisfactory predictions of the affinity data of a true external ligand set (r(2)(pred) = 0.796). Next, MoQSAR was used to analyze a data set of 58 highly active nicotinoids characterized by 56 descriptors, that are log P, MR and 54 low inter-correlated WHIM (Weighted Holistic Invariant Molecular) indices. Equivalent QSAFIR models, that represent different compromises between structural model complexity, fitting and internal model complexity, were found. Our attention was mostly engaged by a number of nonlinear QSAFIRs, which relate nAChR affinity with the log P and directional WHIM descriptors. The results reviewed herein show as QSAFIRs may helpfully complement the pharmacophores, thus enhancing the applicability of computer-aided methodologies in the field of nAChR agonists.

Triplex Selective 2-(2-Naphthyl)quinoline Compounds: Origins of Affinity and New Design Principles

A novel competition dialysis assay was used to investigate the structural selectivity of a series of substituted 2-(2-naphthyl)quinoline compounds designed to target triplex DNA. The interaction of 14 compounds with 13 different nucleic acid sequences and structures was studied. A striking selectivity for the triplex structure poly dA:[poly dT](2) was found for the majority of compounds studied. Quantitative analysis of the competition dialysis binding data using newly developed metrics revealed that these compounds are among the most selective triplex-binding agents synthesized to date. A quantitative structure-affinity relationship (QSAR) was derived using triplex binding data for all 14 compounds used in these studies. The QSAR revealed that the primary favorable determinant of triplex binding free energy is the solvent accessible surface area. Triplex binding affinity is negatively correlated with compound electron affinity and the number of hydrogen bond donors. The QSAR provides guidelines for the design of improved triplex-binding agents.

A rapid method for quantitative prediction of high affinity CTL epitopes: QSAR studies on peptides having affinity with the class I MHC molecular HLA-A*0201

It would be useful to develop a method to rapidly identify peptide epitopes for vaccine development. In this paper, empirical three-dimensional quantitative structure-affinity relationship (3D-QSAR) methods were used to study the relationship between the three dimensional structural parameters (the isotropic surface area, ISA, and the electronic charge index, ECI) of the HLA-A*0201 binding peptide and the HLA-A*0201/peptide binding affinities. A set of 102 peptides having affinity with the class I MHC HLA-A*0201 molecule was used as training set. A test set of 40 peptides was used to determine the predictive value of the models. The 3D-QSAR models gave a q 2 = 0.5724 and high r 2 pred = 0.6955. According to the standard regression coefficients, it is known that the hydrophobic interactions (in these studies, the ISA is highly correlative with the hydrophobic property) play a dominant role in peptide-MHC molecule binding, and also which amino acid residue with what property is needed at specific position of the peptide. The approach we have taken is highly complementary to the many excellent methods described in references and appears highly predictive. It is a rapid and convenient method for detecting high affinity peptide epitopes.

Benzimidazole derivatives. 3. 3D-QSAR/CoMFA model and computational simulation for the recognition of 5-HT(4) receptor antagonists.

A three-dimensional quantitative structure-affinity relationship study (3D-QSAR), using the comparative molecular field analysis (CoMFA) method, and subsequent computational simulation of ligand recognition have been successfully applied to explain the binding affinities for the 5-HT(4) receptor (5-HT(4)R) of a series of benzimidazole-4-carboxamides and carboxylates derivatives 1-24. The K(i) values of these compounds are in the range from 0.11 to 10 000 nM. The derived 3D-QSAR model shows high predictive ability (q(2) = 0.789 and r(2) = 0.997). Steric (contribution of 43.5%) and electrostatic (50.3%) fields and solvation energy (6.1%) of this novel class of 5-HT(4)R antagonists are relevant descriptors for structure-activity relationships. Computational simulation of the complexes between the benzimidazole-4-carboxamide UCM-21195 (5) and the carboxylate UCM-26995 (21) and a 3D model of the transmembrane domain of the 5-HT(4)R, constructed using the reported crystal structure of rhodopsin, have allowed us to define the molecular details of the ligand-receptor interaction that includes (i) the ionic interaction between the NH group of the protonated piperidine of the ligand and the carboxylate group of Asp(3.32), (ii) the hydrogen bond between the carbonyl oxygen of the ligand and the hydroxyl group of Ser(5.43), (iii) the hydrogen bond between the NH group of Asn(6.55) and the aromatic ring of carboxamides or the ether oxygen of carboxylates, (iv) the interaction of the electron-rich clouds of the aromatic ring of Phe(6.51) and the electron-poor hydrogens of the carbon atoms adjacent to the protonated piperidine nitrogen of the ligand, and (v) the pi-sigma stacking interaction between the benzimidazole system of the ligand and the benzene ring of Tyr(5.38). Moreover, the noticeable increase in potency at the 5-HT(4)R sites, by the introduction of a chloro or bromo atom at the 6-position of the aromatic ring, is attributed to the additional electrostatic and van der Waals interaction of the halogen atom in a small cavity located between transmembrane domains 5 and 6.

Synthesis and pharmacological evaluation of 1-[(1,2-diphenyl-1H-4-imidazolyl)methyl]-4-phenylpiperazines with clozapine-like mixed activities at dopamine D(2), serotonin, and GABA(A) receptors.

A series of 18 1-[(1,2-diphenyl-1H-4-imidazolyl)methyl]-4-piperazines (1a-r) were designed and synthesized as possible ligands with mixed dopamine (DA) D(2)/serotonin 5-HT(1A) affinity, with the aim of identifying novel compounds with neurochemical and pharmacological properties similar to those of clozapine. The binding profile at D(2) like, 5-HT(1A), and 5-HT(2A) receptors of title compounds was determined. Modifications made in the phenyl rings of the parent compound (1a) produced congeners endowed with a broad range of binding affinities for DA D(2) like, serotonin 5-HT(1A), and 5-HT(2A) receptors, with IC(50) values ranging from 25 to >10,000 nM. As for the modification of the piperazine N(4)-phenyl ring, the affinities for both D(2) like and 5-HT(1A) receptors were progressively increased by introduction of ortho-methoxy and ethoxy groups (1b,o, respectively). Data revealed the presence of a para-chloro substituent in 1g to be associated with a relatively high affinity and substantial selectivity for D(2) like receptors, whereas the meta-chloro analogue 1f exhibited preferential affinity for 5-HT(1A) receptors. A quantitative structure-affinity relationship analysis of the measured binding data resulted in regression equations that highlighted substituent physicochemical properties modulating the binding to subtypes 1A and 2A of serotonin 5-HT receptors but not to D(2) like receptors. Thus, besides an electron-withdrawing field effect and ortho substitution, which both influence binding to serotonin 5-HT receptor subtypes, though to a different extent as revealed by regression coefficients in the multiparametric regression equations, the affinity of congeners 1a-r to 5-HT(1A) receptors proved to be linearly correlated with volume/polarizability descriptors, whereas their affinity to 5-HT(2A) receptors correlated with lipophilicity constants through a parabolic relationship. 1-[(1,2-Diphenyl-1H-4-imidazolyl)methyl]-4-(2-methoxyphenyl)piperazine (1b), with a D(2)/5-HT(1A) IC(50) ratio of approximately 1, was selected for a further pharmacological study. In rats, the intraperitoneal administration of compound 1b, like that of clozapine, induced an increase in the extracellular concentration of DA measured in the medial prefrontal cortex. Furthermore, 1b and clozapine each inhibited GABA-evoked Cl(-) currents at recombinant GABA(A) receptors expressed in Xenopus oocytes. These findings suggest that compound 1b may represent an interesting prototype of a novel class of drugs endowed with a neurochemical profile similar to that of atypical antipsychotics.

Estimation of activated carbon adsorption efficiency for organic vapours: I. A strategy for selecting test compounds

Strategies for developing quantitative structure–affinity relationships (QSAfR) for the prediction of break-through performance of 31 chlorinated hydrocarbons on activated carbon have been studied. Two different approaches for the selection of a limited set of compounds for modelling were evaluated through the predictive power of the resulting QSAfR models. When the model was based on a training-set selected without a rational strategy, the developed QSAfR model showed poor predictive performance. Accordingly, such models have a limited capability to produce information concerning the important adsorbate related parameters influencing adsorption. By using a strategy where multivariate data analytical techniques are used in conjunction with statistical experimental design to select a balanced set of compounds for break-through performance evaluation, it was possible to develop QSAfR models with high predictive capability.

Novel Potent 5-HT 3 Receptor Ligands Based on the Pyrrolidone Structure: Synthesis, Biological Evaluation, and Computational Rationalization of the Ligand–Receptor Interaction Modalities

Novel conformationally constrained derivatives of classical 5-HT 3 receptor antagonists were designed and synthesized with the aim of probing the central 5-HT 3 receptor recognition site in a systematic way. The newly-synthesized compounds were tested for their potential ability to inhibit [ 3H]granisetron specific binding to 5-HT 3 receptor in rat cortical membranes. These studies revealed subnanomolar affinity in some of the compounds under study. The most potent ligand in this series was found to be quinuclidine derivative ( S)- 7i, which showed an affinity comparable with that of the reference ligand granisetron. The potential 5-HT 3 agonist/antagonist activity of some selected compounds was assessed in vitro on the 5-HT 3 receptor-dependent [ 14C]guanidinium uptake in NG 108-15 cells. Both of the tropane derivatives tested in this functional assay ( 7a and 9a) showed antagonist properties, while the quinuclidine derivatives studied [the enantiomers of compounds 7i, 8g, and 9g, and compound ( R)- 8h] showed a full range of intrinsic efficacies. Therefore, the functional behavior of these 5-HT 3 receptor ligands appears to be affected by the structural features of both the azabicyclo moiety and the heteroaromatic portion. In agreement with the data obtained on NG 108-15 cells, investigations on the 5-HT 3 receptor-dependent Bezold–Jarisch reflex in urethane-anaesthetized rats confirmed the 5-HT 3 receptor antagonist properties of compounds 7a and ( S)- 7i showing for these compounds ID 50 values of 2.8 and 181 μg/kg, respectively. Finally, compounds 7a, ( S)- 7i and 9a (at the doses of 0.01, 1.0, and 0.01 mg/kg ip, respectively) prevented scopolamine-induced amnesia in the mouse passive avoidance test suggestive of a potential usefulness in cognitive disorders for these compounds. Qualitative and quantitative structure–affinity relationship studies were carried out by means of theoretical descriptors derived on a single structure and ad-hoc defined size and shape descriptors (indirect approach). The results showed to be useful in capturing information relevant to ligand–receptor interaction. Additional information derived by the analysis of the energy minimized 3-D structures of the ligand–receptor complexes (direct approach) suggested interesting mechanistic and methodological considerations on the binding mode multiplicity at the 5-HT 3 receptors and on the degree of tolerance allowed in the alignment of molecules for the indirect approach, respectively.

Toward the quantitative prediction of T-cell epitopes: coMFA and coMSIA studies of peptides with affinity for the class I MHC molecule HLA-A*0201.

A set of 102 peptides with affinity for the class I MHC HLA-A0201 molecule was subjected to three-dimensional quantitative structure-affinity relationship (3D QSAR) studies using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). A test set of 50 peptides was used to determine the predictive value of the models. The CoMFA models gave q(2) and r(2)pred below 0.5. The best CoMSIA model has q(2) = 0.542 and r(2)pred = 0.679, and includes hydrophobic, steric, and H-bond donor fields. The hydrophobic interactions play a dominant role in peptide-MHC molecule binding. CoMSIA coefficient contour maps were used to analyze the structural features of the peptides accounting for the affinity in terms of the three positively contributing physicochemical properties: local hydrophobicity, steric bulk and hydrogen-bond-donor ability.

Analytical Improvement in Measuring Formation Constants of Inclusion Complexes between β-Cyclodextrin and Phenolic Compounds

Inclusion complex formation betweensixteen para-substituted phenols andβ-cyclodextrin have been investigated in orderto establish Quantitative Structure AffinityRelationships. An analytical methodology is proposed,in order to obtain reliable evaluation of bindingaffinities. Potentiometry and circular dichroism havebeen applied to define experimental conditions and toconfirm postulated equilibriums. In addition, the useof algorithmic treatments and concentrationoptimisation to determine formation constants leads tocoherent values between 1H NMR, direct UVSpectroscopy and the spectral displacement method. Theresults emphasise the contribution of van der Waalsinteractions, provided that no significant differencein the dipole of the molecule arises from thepara-substituent.

Novel potent and selective central 5-HT3 receptor ligands provided with different intrinsic efficacy. 2. Molecular basis of the intrinsic efficacy of arylpiperazine derivatives at the central 5-HT3 receptors.

Novel 5-HT3 receptor ligands were designed and synthesized with the aim of obtaining deeper insight into the molecular basis of the intrinsic efficacy of arylpiperazines interacting with the central 5-HT3 receptor. The newly synthesized compounds and some previously published compounds belonging to the same class of heteroarylpiperazines were tested for their potential ability to displace [3H]granisetron from rat cortical membranes. These 5-HT3 receptor binding studies revealed subnanomolar affinity in several of the compounds under study. The most active ligands were quipazine derivatives bearing a phenyl group in the 4-position and various oxygenated alkyl side chains in the 3-position of the quinoline nucleus. Qualitative and theoretical quantitative structure-affinity relationship studies were carried out, and the interaction model for the 5-HT3 ligands related to quipazine with their receptor, proposed in part 1 of the present work, was updated to incorporate the latest data. The potential 5-HT3 agonist/antagonist activity of 12 selected compounds was assessed in vitro on the 5-HT3 receptor-dependent [14C]guanidinium uptake in NG 108-15 cells. Their intrinsic efficacy ranged from the 5-HT3 full agonist properties of compounds 7a and 8h, i to those of partial agonists 10a,d and antagonists 8b,d,e, and 9c, d,h,i. The comparison between these functional data and those relative to the previously described compounds suggested that in this class of 5-HT3 ligands the intrinsic efficacy is modulated in a rather subtle manner by the steric features of the heteroaryl moiety.

Novel potent and selective central 5-HT3 receptor ligands provided with different intrinsic efficacy. 1. Mapping the central 5-HT3 receptor binding site by arylpiperazine derivatives.

Synthesis and pharmacological evaluation of a series of condensed quinoline and pyridine derivatives bearing a N-methylpiperazine moiety attached to the 2-position of the quinoline or pyridine nucleus are described. 5-HT receptor binding studies revealed subnanomolar affinity for the 5-HT3 receptor subtype in some of the compounds under study. The most active compound (5b) displayed a Ki value about 1 order of magnitude higher than that of quipazine along with a higher selectivity. The potential 5-HT3 agonist/antagonist activity of four selected compounds was assessed in vitro on 5-HT3 receptor-dependent [14C]guanidinium uptake in NG 108-15 cells. Compound 5j acted as a 5-HT3 agonist in this assay with an EC50 value close to that reported for quipazine, while 5b was a partial agonist with an EC50 value of about 0.25 nM, and compound 5c possessed antagonist properties with an IC50 value (approximately 8 nM) in the same range as those of previously characterized 5-HT3 receptor antagonists. Qualitative and quantitative structure-affinity relationship studies carried out by making use of theoretical molecular descriptors allowed to elucidate the role of the main pharmacophoric components and to develop a model for the interaction of the 5-HT3 ligands related to quipazine with their receptor.

α1-Adrenoceptor subtype selectivity: Molecular modelling and theoretical quantitative structure—affinity relationships

This study constitutes a preliminary rationalization, at the molecular level, of antagonist selectivity towards the three cloned α1-adrenergic receptor (α1-AR) subtypes. Molecular dynamics simulations allowed a structural/dynamics analysis of the seven α-helix-bundle models of the bovine α1a-, hamster α1b-, and rat α1d-AR subtypes. The results showed that the transmembrane domains of these subtypes have different dynamic behaviours and different topographies of the binding sites, which are mainly constituted by conserved residues. In particular, the α1a-AR binding site is more flexible and topographically different with respect to the other two subtypes. The results of the theoretical structural/dynamics analysis of the isolated receptors are consistent with the binding affinities of the 16 antagonists tested towards the three cloned α1-AR subtypes. Moreover, the theoretical quantitative structure-affinity relationships obtained from the antagonist-receptor interaction models further corroborates the hypothesis that selectivity towards one preferential subtype is mainly modulated by receptor and/or ligand distortion energies. In other words, subtype selectivity seems to be mainly guided by the dynamic complementarity (induced fit) between ligand and receptor. On the basis of the quantitative models presented it is possible to predict both affinities and selectivities of putative α1-AR ligands as well as to estimate the theoretical α1-AR subtype affinities and selectivities of existing antagonists.