Postprocessing of docked protein-ligand complexes using implicit solvation models.

Molecular docking plays an important role in drug discovery as a tool for the structure-based design of small organic ligands for macromolecules. Possible applications of docking are identification of the bioactive conformation of a protein-ligand complex and the ranking of different ligands with respect to their strength of binding to a particular target. We have investigated the effect of implicit water on the postprocessing of binding poses generated by molecular docking using MM-PB/GB-SA (molecular mechanics Poisson-Boltzmann and generalized Born surface area) methodology. The investigation was divided into three parts: geometry optimization, pose selection, and estimation of the relative binding energies of docked protein-ligand complexes. Appropriate geometry optimization afforded more accurate binding poses for 20% of the complexes investigated. The time required for this step was greatly reduced by minimizing the energy of the binding site using GB solvation models rather than minimizing the entire complex using the PB model. By optimizing the geometries of docking poses using the GB(HCT+SA) model then calculating their free energies of binding using the PB implicit solvent model, binding poses similar to those observed in crystal structures were obtained. Rescoring of these poses according to their calculated binding energies resulted in improved correlations with experimental binding data. These correlations could be further improved by applying the postprocessing to several of the most highly ranked poses rather than focusing exclusively on the top-scored pose. The postprocessing protocol was successfully applied to the analysis of a set of Factor Xa inhibitors and a set of glycopeptide ligands for the class II major histocompatibility complex (MHC) A(q) protein. These results indicate that the protocol for the postprocessing of docked protein-ligand complexes developed in this paper may be generally useful for structure-based design in drug discovery.

The relationship between office type and job satisfaction: Testing a multiple mediation model through ease of interaction and well-being.

Objectives This cross-sectional study investigated the associations between office type (cellular, shared-room, small open-plan, and medium-sized open-plan) and employees' ease of interaction with coworkers, subjective well-being, and job satisfaction. Methods A brief survey including measures of office type, ease of interaction with coworkers, subjective well-being, and job satisfaction was sent electronically to 1500 Swedish real-estate agents, 271 of whom returned usable surveys. The data were analyzed using a regression-based serial multiple mediation model (PROCESS Model 6), which tested whether the relationship between office type and job satisfaction would be mediated by ease of interaction and, in turn, subjective well-being. Results A negative relationship was found between the number of coworkers sharing an office and employees' job satisfaction. This association was serially mediated by ease of interaction with coworkers and subjective well-being, with employees working in small and medium-sized open-plan offices reporting lower levels of both these aspects than employees who work in either cellular or shared-room offices. Conclusions Open-plan offices may have short-term financial benefits, but these benefits may be lower than the costs associated with decreased job satisfaction and well-being. Therefore, decision-makers should consider the impact of office type on employees rather than focusing solely on cost-effective office layout, flexibility, and productivity.

A multivariate approach to investigate docking parameters' effects on docking performance.

Increasingly powerful docking programs for analyzing and estimating the strength of protein-ligand interactions have been developed in recent decades, and they are now valuable tools in drug discovery. Software used to perform dockings relies on a number of parameters that affect various steps in the docking procedure. However, identifying the best choices of the settings for these parameters is often challenging. Therefore, the settings of the parameters are quite often left at their default values, even though scientists with long experience with a specific docking tool know that modifying certain parameters can improve the results. In the study presented here, we have used statistical experimental design and subsequent regression based on root-mean-square deviation values using partial least-square projections to latent structures (PLS) to scrutinize the effects of different parameters on the docking performance of two software packages: FRED and GOLD. Protein-ligand complexes with a high level of ligand diversity were selected from the PDBbind database for the study, using principal component analysis based on 1D and 2D descriptors, and space-filling design. The PLS models showed quantitative relationships between the docking parameters and the ability of the programs to reproduce the ligand crystallographic conformation. The PLS models also revealed which of the parameters and what parameter settings were important for the docking performance of the two programs. Furthermore, the variation in docking results obtained with specific parameter settings for different protein-ligand complexes in the diverse set examined indicates that there is great potential for optimizing the parameter settings for selected sets of proteins.

Hierarchical PLS modeling for predicting the binding of a comprehensive set of structurally diverse protein-ligand complexes.

A new approach is presented for predicting ligand binding to proteins using hierarchical partial-least-squares regression to latent structures (Hi-PLS). Models were based on information from the 2002 release of the PDBbind database containing (after in-house refinement) high-resolution X-ray crystallography and binding affinity (Kd or Ki) data for 612 protein-ligand complexes. The complexes were characterized by four different descriptor blocks: three-dimensional (3D) structural descriptors of the proteins, protein-ligand interactions according to the Validate scoring function, binding site surface areas, and ligand 2D and 3D descriptors. These descriptor blocks were used in Hi-PLS models, generated using both linear and nonlinear terms, to relate the characterizations to pKd/i. The results show that each of the four descriptor blocks contributed to the model, and the predictions of pKd/i of the internal test set gave a root-mean-square error of prediction (RMSEP) of 1.65. The data were further divided according to the structural classification of the proteins, and Hi-PLS models were constructed for the resulting subclasses. The models for the four subclasses differed considerably in terms of both their ability to predict pKd/i (with RMSEPs ranging from 0.8 to 1.56) and the descriptor block that had the strongest influence. The models were validated with an external test set of 174 complexes from the 2003 release of the PDBbind database. The overall results show that the presented Hi-PLS methodology could facilitate the difficult task of predicting binding affinity.

Occurrence and environmental behavior of the bactericide triclosan and its methyl derivative in surface waters and in wastewater.

The bactericide triclosan and methyl triclosan, an environmental transformation product thereof, were detected in lakes and in a river in Switzerland at concentrations of up to 74 and 2 ng L(-1), respectively. Both compounds were emitted via wastewater treatment plants (WWTPs), with methyl triclosan probably being formed by biological methylation. A regional mass balance for a lake (Greifensee) indicated significant removal of triclosan by processes other than flushing. Laboratory experiments showed that triclosan in the dissociated form was rapidly decomposed in lake water when exposed to sunlight (half-life less than 1 h in August at 47 degrees latitude). Methyl triclosan and nondissociated triclosan, however, were relatively stable toward photodegradation. Modeling these experimental data for the situation of lake Greifensee indicated that photodegradation can account for the elimination of triclosan from the lake and suggested a seasonal dependence of the concentrations (lower in summer, higher in winter), consistent with observed concentrations. Although emissions of methyl triclosan from WWTPs were only approximately 2% relative to those of triclosan, its predicted concentration relative to triclosan in the epilimnion of the lake increases to 30% in summer. Passive sampling with semipermeable membrane devices (SPMDs) indicated the presence of methyl triclosan in lakes with inputs from anthropogenic sources but not in a remote mountain lake. Surprisingly, no parent triclosan was observed in the SPMDs from these lakes. Methyl triclosan appears to be preferentially accumulated in SPMDs under the conditions in these lakes, leading to concentrations comparable to those of persistent chlorinated organic pollutants.