Biomedical subjects

Jeffrey J Sutherland

Publications and source records attributed to Jeffrey J Sutherland.

6 recordsLinked to original sources

PubMed2004

A comparison of methods for modeling quantitative structure-activity relationships.

A large number of methods are available for modeling quantitative structure-activity relationships (QSAR). We examine the predictive accuracy of several methods applied to data sets of inhibitors for angiotensin converting enzyme, acetylcholinesterase, benzodiazepine receptor, cyclooxygenase-2, dihydrofolate reductase, glycogen phosphorylase b, thermolysin, and thrombin. Descriptors calculated with CoMFA, CoMSIA, EVA, HQSAR, and traditional 2D and 2.5D descriptors were used for developing models with partial least squares (PLS). In addition, the genetic function approximation algorithm, genetic PLS, and back-propagation neural networks were used for deriving models from 2.5D descriptors (i.e., 2D descriptors and 3D descriptors calculated from CORINA structures and Gasteiger-Marsili charges). Predictive accuracy was assessed using designed test sets. It was found that HQSAR generally performs as well as CoMFA and CoMSIA; other descriptor sets performed less well. When 2.5D descriptors were used, only neural network ensembles were found to be similarly or more predictive than PLS models. In addition, we show that many cross-validation procedures yield similar estimates of the interpolative accuracy of methods. However, the lack of correspondence between cross-validated and test set predictive accuracy for four sets underscores the benefit of using designed test sets.

Algorithms↗

PubMed2004

Molecular modeling of the von Willebrand factor A2 Domain and the effects of associated type 2A von Willebrand disease mutations.

A homology model for the A2 domain of von Willebrand factor (VWF) is presented. A large number of target-template alignments were combined into a consensus alignment and used for constructing the model from the structures of six template proteins. Molecular dynamics simulation was used to study the structural and dynamic effects of eight mutations introduced into the model, all associated with type 2A von Willebrand disease. It was found that the group I mutations G1505R, L1540P and S1506L cause significant deviations over multiple regions of the protein, coupled to significant thermal fluctuations for G1505R and L1540P. This suggests that protein instability may be responsible for their intracellular retention. The group II mutations R1597W, E1638K and G1505E caused single loop displacements near the physiologic VWF proteolysis site between Y1605-M1606. These modest structural changes may affect interactions between VWF and the ADAMTS13 protease. The group II mutations I1628T and L1503Q caused no significant structural change in the protein, suggesting that inclusion of the protease in this model is necessary for understanding their effect. [Figure: see text]. Homology model of the von Willebrand factor A2 domain

Amino Acid Sequence↗

PubMed2004

Pruned receptor surface models and pharmacophores for three-dimensional database searching.

A pharmacophore represents the 3D arrangement of chemical features that are shared by molecules exhibiting activity at a protein receptor. Pharmacophores are routinely used in 3D database searching for identifying potential lead compounds. The lack of shape constraints causes the query to identify compounds that could not fit into the active site. In the absence of structural information, a receptor surface model (RSM) can be used to represent the active site. The RSM consists of a surface that envelops a set of known actives after these have been aligned using their common features. When used for database searching, a RSM is overconstraining as it restricts access to regions that could be occupied by ligands, such as the solvent-protein interface or unexplored pockets. We describe a protocol for developing pruned RSMs using information gleaned from 3D quantitative structure-activity relationship (QSAR) models. We examined the performance of queries that consist of pharmacophores used alone or with pruned or unpruned RSMs by performing searches on six databases containing known actives distributed among inactives. The pruned RSMs yield an average selectivity 1.8 times greater than that for pharmacophore queries, compared to 1.6 times for unpruned RSMs. However, the pruned RSMs retrieve on average 73% of the actives identified using the pharmacophores, compared to 40% for the unpruned RSMs. As such, pruned RSMs represent a useful compromise between the high sensitivity of pharmacophores and the high selectivity of unpruned RSMs.

Amidines↗

PubMed2003

Founder von Willebrand factor haplotype associated with type 1 von Willebrand disease.

To date, no dominant mutation has been identified in a significant proportion of patients with type 1 von Willebrand disease (VWD). In this study, we examined 70 families as part of the Canadian Type 1 VWD Study. The entire VWF gene was sequenced for 1 index case, revealing 2 sequence variations: intron 30 (5312-19A>C) and exon 28 at Tyr1584Cys (4751A>G). The Tyr1584Cys variation was identified in 14.3% (10 of 70) of the families and was in phase with the 5312-19A>C variation in 7 (10.0%) families. Both variants were observed in 2 of 10 UK families with type 1 VWD, but neither variant was found in 200 and 100 healthy, unrelated persons, respectively. Mean von Willebrand factor antigen (VWF:Ag), VWF ristocetin cofactor (VWF:RCo), and factor VIII coagulant activity (FVIII:C) for the index cases in these families are 0.4 U/mL, 0.36 U/mL, and 0.54 U/mL, respectively, and VWF multimer patterns show no qualitative abnormalities. Aberrant VWF splicing was not observed in these patients, and both alleles of the VWF gene are expressed as RNA. Molecular dynamic simulation was performed on a homology model of the VWF-A2 domain containing the Tyr1584Cys mutation. This showed that no significant structural changes occur as a result of the substitution but that a new solvent-exposed reactive thiol group is apparent. Expression studies revealed that the Tyr1584Cys mutation results in increased intracellular retention of the VWF protein. We demonstrate that all the families with the Tyr1584Cys mutation share a common, evolved VWF haplotype, suggesting that this mutation is ancient. This is the first report of a mutation that segregates in a significant proportion of patients with type 1 VWD.

Alleles↗

PubMedDate not supplied

Development of quantitative structure-activity relationships and classification models for anticonvulsant activity of hydantoin analogues.

Classification and QSAR analysis was performed on a large set of hydantoin derivatives with measured anticonvulsant activity in mice and rats. The classification set comprised 287 hydantoins having maximal electroshock (MES) activity expressed in qualitative form. A subset of 94 hydantoins with MES ED(50) values was used for QSAR analysis. Numerical descriptors were generated to encode topological, geometric/structural, electronic, and thermodynamic properties of molecules. Analyses were performed with training and test sets of diverse compounds selected using their representation in a principal component space. Cell- and distance metric-based selection methods were employed in this process. For QSAR, a genetic algorithm (GA) was used for selecting subsets of 5-9 descriptors that minimize the rms error on the training sets. The most predictive models have rms errors of 0.86 (r(2) = 0.64) and 0.73 (r(2) = 0.75) ln(1/ED(50)) units on the cell- and distance metric-derived test sets, respectively, and showed convergence in the selected descriptors. Classification models were developed using recursive partitioning (RP) and spline-fitting with a GA (SFGA), a novel method we have implemented. The most predictive RP and SFGA models have classification rates of 75% and 80% on the test sets; both methods produced models with similar discriminating features. For QSAR and classification, consensus schemes gave improved predictive accuracy.

Algorithms↗

PubMedDate not supplied

Spline-fitting with a genetic algorithm: a method for developing classification structure-activity relationships.

Classification methods allow for the development of structure-activity relationship models when the target property is categorical rather than continuous. We describe a classification method which fits descriptor splines to activities, with descriptors selected using a genetic algorithm. This method, which we identify as SFGA, is compared to the well-established techniques of recursive partitioning (RP) and soft independent modeling by class analogy (SIMCA) using five series of compounds: cyclooxygenase-2 (COX-2) inhibitors, benzodiazepine receptor (BZR) ligands, estrogen receptor (ER) ligands, dihydrofolate reductase (DHFR) inhibitors, and monoamine oxidase (MAO) inhibitors. Only 1-D and 2-D descriptors were used. Approximately 40% of compounds in each series were assigned to a test set, "cherry-picked" from the complete set such that they lie outside the training set as much as possible. SFGA produced models that were more predictive for all but the DHFR set, for which SIMCA was most predictive. RP gave the least predictive models for all but the MAO set. A similar trend was observed when using training and test sets to which compounds were randomly assigned and when gradually eliminating compounds from the (designed) training set. The stability of models was examined for the random and reduced sets, where stability means that classification statistics and the selected descriptors are similar for models derived from different sets. Here, SIMCA produced the most stable models, followed by SFGA and RP. We show that a consensus approach that combines all three methods outperforms the single best model for all data sets.

Algorithms↗