Biomedical subjects

A Vedani

Publications and source records attributed to A Vedani.

12 recordsLinked to original sources

PubMed2001

Internet laboratory for predicting harmful effects triggered by drugs and chemicals. Concept and call for co-operation.

It is our objective to establish a virtual laboratory on the Internet to allow for an in silico estimation of harmful effects triggered by drugs, chemicals and their metabolites. Presently, our database includes validated models for five biological targets -- the Aryl hydrocarbon, serotonin 5HT-2A, cannabinoid, GABA (gamma-amino butter acid), and steroid receptors. It shall be continuously extended to include surrogates for any bioregulator known or presumed to mediate harmful effects. Free access to this virtual laboratory shall allow any interested party to estimate the harmful potential of a given substance prior to its synthesis. This is achieved by generating the three-dimensional structure of the compound and its possible metabolites in the computer, followed by calculating their binding affinity towards each receptor surrogate in the database. Only compounds/metabolites passing through this surrogate battery without displaying a significant affinity towards any member may be cleared for synthesis and preclinical studies. This way, potentially harmful compounds can be withdrawn from the evaluation pipeline before in vivo test are conducted, hence contributing to the reduction of animal testing in chemical and pharmaceutical research and development.

Animal Testing Alternatives↗

PubMed2000

Multiple-conformation and protonation-state representation in 4D-QSAR: the neurokinin-1 receptor system.

Using a 4D-QSAR approach (software Quasar) allowing for multiple-conformation, orientation, and protonation-state ligand representation as well as for the simulation of local induced-fit phenomena, we have validated a family of receptor surrogates for the neurokinin-1 (NK-1) receptor system. The evolution was based on a population of 500 receptor models and simulated during 40 000 crossover steps, corresponding to 80 generations. It yielded a cross-validated r(2) of 0.887 for the 50 ligands of the training set (represented by a total of 218 conformers and protomers) and a predictive r(2) of 0.834 for the 15 ligands of the test set (70 conformers and protomers). A series of five "scramble tests" (with an average predictive r(2) of -0.438) demonstrates the sensitivity of the surrogate toward the biological data, for which it should establish a QSAR. On the basis of this model, the activities of 12 new compounds - four of which have been synthesized and tested in the meantime - are predicted. For most of the NK-1 antagonists, the genetic algorithm selected a single entity - out of the up to 12 conformers or protomers - to preferably bind to the receptor surrogate. Moreover, the evolution converged at an identical protonation scheme for all NK-1 antagonists. This indicates that 4D-QSAR techniques may, indeed, reduce the bias associated with the choice of the bioactive conformation as each ligand molecule can be represented by an ensemble of conformations, orientations, and protonation states.

Combinatorial Chemistry Techniques↗

PubMed2000

[Transgenic art].

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Animal Welfare↗

PubMed2000

Multi-dimensional QSAR in drug research. Predicting binding affinities, toxicity and pharmacokinetic parameters.

Quantitative structure-activity relationships (QSAR) are an area of computational research which builds atomistic or virtual models to predict the biological activity or the toxicity of known or hypothetical substances. Of particular interest for the biomedical sciences are three-dimensional receptor surrogates (3D-QSAR) because they allow for the simulation of directional forces such as hydrogen bonds or metal-ligand contacts--key interactions for both molecular recognition and stereospecific ligand binding. While more powerful approaches make use of a genetic algorithm or a neural network to evolve a receptor surrogate, its predictive power still critically depends on the spatial alignment of the ligand molecules--mirroring the pharmacophore hypothesis--used to construct it. To avoid this bias, a recent development at our laboratory includes the possibility to represent each ligand molecule by an ensemble of conformations, orientations and protonation states as the fourth dimension (4D-QSAR). In addition, it allows for a potentially flexible receptor site (mimicking local induced fit) and solvent-accessible or shallow binding pockets. In this account, we seek to document the superiority of 4D-QSAR compared to 3D-concepts with simulations on the steroid, the aryl hydrocarbon and the neurokinin-1 receptor system. More complex, future applications of 4D-QSAR--the establishment of a virtual laboratory for the assessment of receptor-mediated toxicity and the prediction of oral bioavailability--are outlined.

Animals↗

PubMed1999

Ochratoxin binding to phenylalanyl-tRNA synthetase: computational approach to the mechanism of ochratoxicosis and its antagonism.

Ochratoxin A (OA) is a toxic isocoumarin derivative released by various species of mold which grow on grain, coffee, and nuts, representing a serious worldwide health problem. Among other mechanisms of toxicity, it has been suggested that OA inhibits phenylalanyl-tRNA synthetase (PheRS), thereby reducing protein synthesis. Using the crystal structure of PheRS from Thermusthermophilus, we have modeled its interactions with OA as well as with phenylalanyl adenylate (FAMP), the high-affinity intermediate substrate of PheRS. Our results indicate that while OA may be capable of weakly inhibiting PheRS, the OA-PheRS complex cannot adopt the same conformation as does FAMP-PheRS, contrary to previous assumptions. Relative to FAMP, the phenylalanyl moiety is found to bind more shallowly and in a different overall conformation. Free-energy perturbation calculations of the relative free energies of binding of OA with the phenolic moiety protonated versus deprotonated suggest that the protonated form binds significantly more strongly. Two alternative binding modes were also identified which cannot be discounted on the basis of these calculations. Our results, however, do not suggest binding stronger than millimolar for any of the binding modes, a conclusion which is in agreement with more recent experimental findings. This, in turn, suggests that the previously observed antagonistic effects of aspartame and piroxicam are more likely due to their prevention of OA binding to human serum albumin than to PheRS, which is in agreement with binding studies as well as with preliminary simulations performed in our laboratory.

Binding Sites↗

PubMed1999

[Genetic algorithms in 3D-QSAR: The use of multiple ligand orientations for improved predictions of toxicity]

In a recent article in this journal, we discussed the application of a 3D-QSAR technique to the prediction of the toxicity of dibenzodioxins, dibenzofurans, and biphenyls (Vedani et al., 1999a). The use of such methods is legitimate because there is strong evidence that the toxicity is mediated by the Aryl hydrocarbon (Ah) receptor, a regulatory element involved in the mammalian metabolism of xenobiotics. In an extention to a concept developed at our laboratory (Vedani et al., 1998), we now show that safer predictions are possible if instead of a single - and, therefore, necessarily biased - assumption about the mutual orientation of the toxins, an ensemble of possible orientations is used for model construction. The contribution of a single entity within this ensemble to the toxin-receptor interaction energy is determined by a Boltzmann criterion. While in the single-orientation model two of the 26 toxins of an external test set were predicted false positive or false negative, all test substances are correctly predicted in the multiple-orientation model - including up to four different orientations per molecule - within a factor 10 of the experimental binding affinity. These results demonstrate that 3D-QSAR techniques based on a genetic algorithm can be used to predict the toxicity of chemical and pharmacological substances "in computo" if a receptor-mediated mechanism can be assumed. Consequently, the method can be used in toxicological screening assays, thereby replacing stressful tests on animals.

Journal Article↗

PubMed1999

[Genetic algorithms in 3D-QSAR: Predicting the toxicity of dibenzodioxins, dibenzofurans and biphenyls]

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) and related compounds represent serious environmental health hazards, whose effects include dermal toxicity, immunotoxicity, tumor promotion, developmental and reproductive toxicity. There is strong evidence that the toxicity is mediated by the Aryl hydrocarbon (Ah) receptor, a regulatory element involved in the mammalian metabolism of xenobiotics. Using Quasi-atomistic receptor modeling, a 3D-QSAR technique, we have generated a receptor model for the Ah receptor. The model was trained using 76 dibenzodioxins, dibenzofurans, and biphenyls, and tested using 26 compounds different therefrom. 24 of these test compounds (92.3%) were predicted to within a factor of 10 of the experimental binding affinity. One compound was predicted false positive, another false negative (7.7% total). The sensitivity of the model with respect to the biological activity was demonstrated by means of a scramble test with negative outcome. Quasi-atomistic receptor modeling can be used for the testing of chemicals and pharmaca for a toxicological potential in an early development phase and thereby replace stressful tests on animals

Journal Article↗

PubMed1998

Quasi-atomistic receptor modeling. A bridge between 3D QSAR and receptor fitting.

Quasi-atomistic receptor modeling bridges 3D QSAR and receptor modeling by combining receptor surface models populated with atomistic properties (hydrogen bonds, salt bridges, aromatic and aliphatic regions, solvent) with individually fitted receptor envelopes--simulating a flexible receptor cavity, adapted by means of an induced fit. To mimic amino-acid residues capable to engage in differently directed H-bonds with different ligand molecules at the true biological receptor, our approach includes H-bond flip-flop particles which, depending on a given ligand molecule, can simultaneously act as a H-bond donor and a H-bond acceptor. The use of a directional force field for hydrogen bonds allows for the simulation of ligand selectivity, including the discrimination of stereoisomers. Based on a series of ligand molecules with individually fitted receptor envelopes, the software Quasar allows to generate a family of receptor models by means of genetic algorithms combined with cross-validation protocols. Our concept has been used to derive semi-quantitative structure--activity relationships for a series of six receptor systems, including the beta 2-adrenergic, dopaminergic, aryl hydrocarbon, cannabinoid, neurokinin-1, and HIV protease, respectively. For these systems, quasi-atomistic receptor modeling, was able to predict the relative free energies of ligand binding of an independent set of test ligands within 0.55 to 0.94 kcal/mol of their experimental value, corresponding to an uncertainty in the binding affinity of a factor of 2.5 to 5.0.

Algorithms↗

PubMed1993

Pseudo-receptor modeling: a new concept for the three-dimensional construction of receptor binding sites.

Molecular design of small molecules intended to target a macromolecule generally utilizes one of two computational approaches: "receptor fitting" or "receptor mapping". A comprehensive strategy for the design of potent, selective and novel ligands for cell-bound receptors combines the two by means of "pseudoreceptor modeling". Definition of a refined pharmacophore model is the first step. A subsequent step involves the construction of a pseudoreceptor--an explicit molecular binding pocket--for the bioactive conformation of a series of ligands with high affinity for a particular receptor subtype. The receptor-mapping program "Yak" allows the construction of a peptidic pseudoreceptor around any single small molecule or molecular ensemble of interest. The fidelity of the approach is exemplified by application to the active site of the enzymes human carbonic anhydrase I and thermolysin, followed by comparison with their known X-Ray crystal structures.

Binding Sites↗

PubMed1991

A molecular model for the active site of S-adenosyl-L-homocysteine hydrolase.

S-adenosyl-L-homocysteine hydrolase (AdoHcy hydrolase, EC 3.3.1.1), a specific target for antiviral drug design, catalyzes the hydrolysis of AdoHcy to adenosine (Ado) and homocysteine (Hcy) as well as the synthesis of AdoHcy from Ado and Hcy. The enzyme isolated from different sources has been shown to contain tightly bound NAD+. Based on the 2.0 A-resolution X-ray crystal structure of dogfish lactate dehydrogenase (LDH), which is functionally homologous to AdoHcy hydrolase, and the primary sequence of rat liver AdoHcy hydrolase, we have derived a molecular model of an extended active site for AdoHcy hydrolase. The computational mutation was performed using the software MUTAR (Yeh et al., University of Kansas, Lawrence), followed by molecular mechanics optimizations using the programs AMBER (Singh et al., University of California, San Francisco) and YETI (Vedani, University of Kansas). Solvation of the model structure was achieved by use of the program SOLVGEN (Jacober, University of Kansas); 56 water molecules were explicitly included in all refinements. Some of these may be involved in the catalytic reaction. We also studied a model of the complex of AdoHcy hydrolase with NAD+, as well as the ternary complexes of the enzyme, NAD+, and substrate or inhibitor molecules. Our refined model is capable of explaining part of the redox reaction catalyzed by AdoHcy hydrolase and has been used to differentiate the relative binding strength of inhibitors.

Adenosylhomocysteinase↗

PubMed1984

Structure-activity relationships of sulfonamide drugs and human carbonic anhydrase C: modeling of inhibitor molecules into the receptor site of the enzyme with an interactive computer graphics display.

We have analyzed the molecular interaction of 28 sulfonamide inhibitors with human carbonic anhydrase C (HCAC) using an interactive computer graphic display. Small aromatic sulfonamides gain most of their inhibitory power towards HCAC from the interaction of hydrogen bond acceptors at the para or meta positions with hydrophilic residues of the enzyme. Additional coordinated water molecules stabilize the complexes of heterocyclic sulfonamides (i.e., thiophenes, 1,3-thiazoles, and 1,3,4-thiadiazoles) with HCAC. We propose two different modes of binding of the heterocyclic ring with respect to the active site cleft: the heterocyclic sulfur atom of a 3,4-unsubstituted thiophene approaches the oxygen atom of a coordinated water molecule (sulfur "out"), whereas in 3,4-unsubstituted 1,3,4-thiadiazoles, the sulfur is in contact with a hydrophobic part of the receptor site (sulfur "in"). This proposal is consistent with crystallographic evidence. Sulfonamides with two aromatic or heterocyclic rings also interact with a hydrophobic pocket of the enzyme located greater than 10 A away from the active site metal Zn2+. We also discuss the possibility that the relative inactivity of thiazide diuretics is due to the steric interaction of the ortho chlorine atom with the enzyme receptor cavity.

Binding Sites↗

PubMedDate not supplied

In silico prediction of receptor-mediated environmental toxic phenomena-application to endocrine disruption.

It is an objective of our institution to establish a virtual laboratory allowing for a reliable in silico estimation of the harmful effects triggered by drugs, chemicals and their metabolites. In the recent past, we have developed the underlying technology (Multi-dimensional QSAR: Quasar and Raptor) and compiled a pilot system including the 3D models of three receptors known to mediate endocrine-disrupting effects (the aryl hydrocarbon receptor, the estrogen receptor and the androgen receptor, respectively) and validated them against 310 compounds (drugs, chemicals, toxins). Within this set up we could demonstrate that our concepts are able to both recognize toxic compounds substantially different from those used in the training set as well as to classify harmless compounds clearly as being non-toxic. This suggests that our approach can be used for the prediction of adverse effects of drug molecules and chemicals.

Binding Sites↗