Biomedical subjects

Jeffrey D Schmitt

Publications and source records attributed to Jeffrey D Schmitt.

11 recordsLinked to original sources

PubMed2006

Orbital energetics and molecular recognition.

We present preliminary data demonstrating that orbital eigenenergy fluctuation recorded in the course of ab initio molecular dynamics calculations may contain information relevant in determining molecular behavior and recognition. A simple scheme is presented that maps these data to molecular descriptors. Using computational drug design as the context, these descriptors are compared with previous electronic eigenvalue descriptor methods with encouraging results.

Journal Article↗

PubMed2006

QSAR modeling of mono- and bis-quaternary ammonium salts that act as antagonists at neuronal nicotinic acetylcholine receptors mediating dopamine release.

Back-propagation artificial neural networks (ANNs) were trained on a dataset of 42 molecules with quantitative IC50 values to model structure-activity relationships of mono- and bis-quaternary ammonium salts as antagonists at neuronal nicotinic acetylcholine receptors (nAChR) mediating nicotine-evoked dopamine release. The ANN QSAR models produced a reasonable level of correlation between experimental and calculated log(1/IC50) (r2=0.76, r(cv)2=0.64). An external test for the models was performed on a dataset of 18 molecules with IC50 values >1 microM. Fourteen of these were correctly classified. Classification ability of various models, including self-organizing maps (SOM), for all 60 molecules was also evaluated. A detailed analysis of the modeling results revealed the following relative contributions of the used descriptors to the trained ANN QSAR model: approximately 44.0% from the length of the N-alkyl chain attached to the quaternary ammonium head group, approximately 20.0% from Moriguchi octanol-water partition coefficient of the molecule, approximately 13.0% from molecular surface area, approximately 12.6% from the first component shape directional WHIM index/unweighted, approximately 7.8% from Ghose-Crippen molar refractivity, and 2.6% from the lowest unoccupied molecular orbital energy. The ANN QSAR models were also evaluated using a set of 13 newly synthesized compounds (11 biologically active antagonists and two biologically inactive compounds) whose structures had not been previously utilized in the training set. Twelve among 13 compounds were predicted to be active which further supports the robustness of the trained models. Other insights from modeling include a structural modification in the bis-quinolinium series that involved replacing the 5 and/or 8 as well as the 5' and/or 8' carbon atoms with nitrogen atoms, predicting inactive compounds. Such data can be effectively used to reduce synthetic and in vitro screening activities by eliminating compounds of predicted low activity from the pool of candidate molecules for synthesis. The application of the ANN QSAR model has led to the successful discovery of six new compounds in this study with experimental IC50 values of less than 0.1 microM at nAChR subtypes responsible for mediating nicotine-evoked dopamine release, demonstrating that the ANN QSAR model is a valuable aid to drug discovery.

Dopamine↗

PubMed2005

Molecular modeling of mono- and bis-quaternary ammonium salts as ligands at the alpha4beta2 nicotinic acetylcholine receptor subtype using nonlinear techniques.

The neuronal nicotinic acetylcholine receptor (nAChR) has been a target for drug development studies for over a decade. A series of mono- and bis-quaternary ammonium salts, known to be antagonists at nAChRs, were separated into 3 structural classes and evaluated using both self-organizing map (SOM) and genetic functional approximation (GFA) algorithm models. Descriptors from these compounds were used to create several nonlinear quantitative structure-activity relationships (QSARs). The SOM methodology was effective in appropriately grouping these compounds with diverse structures and activities. The GFA models were also able to predict the activities of these molecules. Charge distribution and the hydrophobic free energies were found to be important indicators of bioactivity for this particular class of molecules. These QSAR approaches may be a useful to screen and select in silico new drug candidates from larger compound libraries to be further evaluated in in vitro biological assays.

Ligands↗

PubMed2005

2-(Arylmethyl)-3-substituted quinuclidines as selective alpha 7 nicotinic receptor ligands.

A series of 2-(arylmethyl)-3-substituted quinuclidines was developed as alpha7 neuronal nicotinic acetylcholine receptor (nAChR) agonists based on a putative pharmacophore model. The series is highly selective for the alpha7 over other nAChRs (e.g., the alpha4beta2 of the CNS, and the muscle and ganglionic subtypes) and is functionally tunable at alpha7. One member of the series, (+)-N-(1-azabicyclo[2.2.2]oct-3-yl)benzo[b]furan-2-carboxamide (+)-8l), has potent agonistic activity for the alpha7 nAChR (EC(50)=33nM, I(max)=1.0), at concentrations below those that result in desensitization.

Animals↗

PubMed2005

Protonation-induced stereoisomerism in nicotine: conformational studies using classical (AMBER) and ab initio (Car-Parrinello) molecular dynamics.

A variety of biologically active small molecules contain prochiral tertiary amines, which become chiral centers upon protonation. S-nicotine, the prototypical nicotinic acetylcholine receptor agonist, produces two diastereomers on protonation. Results, using both classical (AMBER) and ab initio (Car-Parrinello) molecular dynamical studies, illustrate the significant differences in conformational space explored by each diastereomer. As is expected, this phenomenon has an appreciable effect on nicotine's energy hypersurface and leads to differentiation in molecular shape and divergent sampling. Thus, protonation induced isomerism can produce dynamic effects that may influence the behavior of a molecule in its interaction with a target protein. We also examine differences in the conformational dynamics for each diastereomer as quantified by both molecular dynamics methods.

Molecular Conformation↗

PubMed2004

Targacept active conformation search: a new method for predicting the conformation of a ligand bound to its protein target.

Targacept active conformation search (TACS) is a novel variation of well-established three-dimensional quantitative structure--activity relationship methodologies that seeks to determine probable conformation(s) of ligands bound to their protein targets. A combination of affinity or activity data and energetically accessible conformational ensembles, each conformer described by three-dimensional (3-D) sensitive descriptors, forms the basis of the TACS data model. Recursive pruning is used to reduce the size of both the conformational ensemble and the descriptor space until the TACS data model contains just enough information to determine probable conformation(s) of ligands bound to their protein targets. The TACS algorithm is comprised of five components: (1) conformational ensemble generation, (2) 3-D sensitive descriptor calculation, (3) ensemble descriptor preprocessing, (4) model generation, and (5) prediction of bound conformation(s). Significantly, this method precludes the need for subjective or objective molecular alignment. We report the application of this technique to five benchmark protein-ligand couples where the conformation of a bound ligand has been previously established using X-ray crystallography: 9-cis-retinoic (1) and 9-trans-retinoic acid (2), both agonists for the retinoic acid receptor gamma, compounds KH1060 (3) and MC1288 (4), which bind to the vitamin D3 receptor, and R04 (5), an inhibitor bound to human rhinovirus 14 thermolysin. The binding conformations predicted by TACS were compared to the crystallographic structures extracted from their respective binding sites using root-mean-squared deviation (rmsd) criteria. Three of the conformations found using TACS were within crystallographic error. 9-cis-Retinoic acid, 9-trans-retinoic acid, and MC1288, when superimposed on their crystallographic structures, gave rmsd values of 0.22, 0.17, and 0.34 A, respectively. The rmsd values for KH1060 (1.54 A) and R04 (1.01 A) were larger but still reasonable.

Binding Sites↗

PubMed2004

Mapping potential energy surfaces.

A recently proposed dynamical method [A. Laio and M. Parrinello, Proc. Natl. Acad. Sci. U.S.A. 99, 12562 (2002)] allows us to globally sample the free energy surface. This approach uses a coarse-grained non-Markovian dynamics to bias microscopic atomic trajectories. After a sufficiently long simulation time, the global free energy surface can be reconstructed from the non-Markovian dynamics. Here we apply this scheme to study the T=0 free energy surface, i.e., the potential energy surface in coarse-grained space. We show that the accuracy of the reconstructed potential energy surface can be dramatically improved by a simple postprocessing procedure with only minor computational overhead. We illustrate this approach by conducting conformational analysis on a small organic molecule, demonstrating its superiority over traditional unbiased approaches in sampling potential energy surfaces in coarse-grained space.

Journal Article↗

PubMed2002

Targeting neuronal nicotinic receptors: a path to new therapies.

The structural heterogeneity, the ubiquity of anatomical distribution, and the demonstrated modulation of biological functions is consistent with the view that nicotinic cholinergic signaling plays a key regulatory role in the brain and influences a number of neuronal processes including sensory processing, motor activity, and cognitive function. It has become evident that perturbation of nicotinic cholinergic neurotransmission can result in diverse CNS pathologies providing the potential for therapeutic intervention in a number of neurodegenerative, neuropsychiatric and neurological disorders. This review will provide a status on the rationale for neuronal therapies targeting neuronal nicotinic receptors (nAChRs) and discusses the multifaceted beneficial effects than can be achieved through manipulation of cholinergic pharmacology. Recent advances and issues relating to rational drug design based on the structure of acetylcholine binding protein are discussed.

Animals↗

PubMedDate not supplied

Genetic algorithms and self-organizing maps: a powerful combination for modeling complex QSAR and QSPR problems.

Modeling non-linear descriptor-target activity/property relationships with many dependent descriptors has been a long-standing challenge in the design of biologically active molecules. In an effort to address this problem, we couple the supervised self-organizing map with the genetic algorithm. Although self-organizing maps are non-linear and topology-preserving techniques that hold great potential for modeling and decoding relationships, the large number of descriptors in typical quantitative structure-activity relationship or quantitative structure-property relationship analysis may lead to spurious correlation(s) and/or difficulty in the interpretation of resulting models. To reduce the number of descriptors to a manageable size, we chose the genetic algorithm for descriptor selection because of its flexibility and efficiency in solving complex problems. Feasibility studies were conducted using six different datasets, of moderate-to-large size and moderate-to-great diversity; each with a different biological endpoint. Since favorable training set statistics do not necessarily indicate a highly predictive model, the quality of all models was confirmed by withholding a portion of each dataset for external validation. We also address the variability introduced onto modeling through dataset partitioning and through the stochastic nature of the combined genetic algorithm supervised self-organizing map method using the z-score and other tests. Experiments show that the combined method provides comparable accuracy to the supervised self-organizing map alone, but using significantly fewer descriptors in the models generated. We observed consistently better results than partial least squares models. We conclude that the combination of genetic algorithms with the supervised self-organizing map shows great potential as a quantitative structure-activity/property relationship modeling tool.

Algorithms↗

PubMedDate not supplied

Supervised self-organizing maps in drug discovery. 1. Robust behavior with overdetermined data sets.

The utility of the supervised Kohonen self-organizing map was assessed and compared to several statistical methods used in QSAR analysis. The self-organizing map (SOM) describes a family of nonlinear, topology preserving mapping methods with attributes of both vector quantization and clustering that provides visualization options unavailable with other nonlinear methods. In contrast to most chemometric methods, the supervised SOM (sSOM) is shown to be relatively insensitive to noise and feature redundancy. Additionally, sSOMs can make use of descriptors having only nominal linear correlation with the target property. Results herein are contrasted to partial least squares, stepwise multiple linear regression, the genetic functional algorithm, and genetic partial least squares, collectively referred to throughout as the "standard methods". The k-nearest neighbor (kNN) classification method was also performed to provide a direct comparison with a different classification method. The widely studied dihydrofolate reductase (DHFR) inhibition data set of Hansch and Silipo is used to evaluate the ability of sSOMs to classify unknowns as a function of increasing class resolution. The contribution of the sSOM neighborhood kernel to its predictive ability is assessed in two experiments: (1) training with the k-means clustering limit, where the neighborhood radius is zero throughout the training regimen, and (2) training the sSOM until the neighborhood radius is reduced to zero. Results demonstrate that sSOMs provide more accurate predictions than standard linear QSAR methods.

Algorithms↗

PubMedDate not supplied

Supervised self-organizing maps in drug discovery. 2. Improvements in descriptor selection and model validation.

The modeling of nonlinear descriptor-target relationships is a topic of considerable interest in drug discovery. We, herein, continue reporting the use of the self-organizing map-a nonlinear, topology-preserving pattern recognition technique that exhibits considerable promise in modeling and decoding these relationships. Since simulated annealing is an efficient tool for solving optimization problems, we combined the supervised self-organizing map with simulated annealing to build high-quality, highly predictive quantitative structure-activity/property relationship models. This technique was applied to six data sets representing a variety of biological endpoints. Since a high statistical correlation in the training set does not indicate a highly predictive model, the quality of all the models was confirmed by withholding a portion of each data set for external validation. Finally, we introduce new cross-validation and dynamic partitioning techniques to address model overfitting and assessment.

Algorithms↗