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.

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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. (Blood. 2003;102:549-557)

Docking methods are used to predict the manner in which a ligand binds to a protein receptor. Many studies have assessed the success rate of programs in self-docking tests, whereby a ligand is docked into the protein structure from which it was extracted. Cross-docking, or using a protein structure from a complex containing a different ligand, provides a more realistic assessment of a docking program's ability to reproduce X-ray results. In this work, cross-docking was performed with CDocker, Fred, and Rocs using multiple X-ray structures for eight proteins (two kinases, one nuclear hormone receptor, one serine protease, two metalloproteases, and two phosphodiesterases). While average cross-docking accuracy is not encouraging, it is shown that using the protein structure from the complex that contains the bound ligand most similar to the docked ligand increases docking accuracy for all methods ("similarity selection"). Identifying the most successful protein conformer ("best selection") and similarity selection substantially reduce the difference between self-docking and average cross-docking accuracy. We identify universal predictors of docking accuracy (i.e., showing consistent behavior across most protein-method combinations), and show that models for predicting docking accuracy built using these parameters can be used to select the most appropriate docking method.

Despite investment in toxicogenomics, nonclinical safety studies are still used to predict clinical liabilities for new drug candidates. Network-based approaches for genomic analysis help overcome challenges with whole-genome transcriptional profiling using limited numbers of treatments for phenotypes of interest. Herein, we apply co-expression network analysis to safety assessment using rat liver gene expression data to define 415 modules, exhibiting unique transcriptional control, organized in a visual representation of the transcriptome (the 'TXG-MAP'). Accounting for the overall transcriptional activity resulting from treatment, we explain mechanisms of toxicity and predict distinct toxicity phenotypes using module associations. We demonstrate that early network responses complement traditional histology-based assessment in predicting outcomes for longer studies and identify a novel mechanism of hepatotoxicity involving endoplasmic reticulum stress and Nrf2 activation. Module-based molecular subtypes of cholestatic injury derived using rat translate to human. Moreover, compared to gene-level analysis alone, combining module and gene-level analysis performed in sequence identifies significantly more phenotype-gene associations, including established and novel biomarkers of liver injury.

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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

An association of drugs with their proteomic family reveals that molecular properties of drugs targeting proteases, lipid and peptide G-protein-coupled receptors (GPCRs), and nuclear hormone receptors significantly exceed limits for some properties in the "rule of five", while drugs targeting cytochrome P450s, biogenic amine GPCRs, and transporters have significantly lower values for certain properties. Also, the variation in drug targets appears to be a factor explaining increasing molecular weight over time.