Molecular dynamics simulation methods produce trajectories of atomic positions (and optionally velocities and energies) as a function of time and provide a representation of the sampling of a given molecule's energetically accessible conformational ensemble. As simulations on the 10-100 ns time scale become routine, with sampled configurations stored on the picosecond time scale, such trajectories contain large amounts of data. Data-mining techniques, like clustering, provide one means to group and make sense of the information in the trajectory. In this work, several clustering algorithms were implemented, compared, and utilized to understand MD trajectory data. The development of the algorithms into a freely available C code library, and their application to a simple test example of random (or systematically placed) points in a 2D plane (where the pairwise metric is the distance between points) provide a means to understand the relative performance. Eleven different clustering algorithms were developed, ranging from top-down splitting (hierarchical) and bottom-up aggregating (including single-linkage edge joining, centroid-linkage, average-linkage, complete-linkage, centripetal, and centripetal-complete) to various refinement (means, Bayesian, and self-organizing maps) and tree (COBWEB) algorithms. Systematic testing in the context of MD simulation of various DNA systems (including DNA single strands and the interaction of a minor groove binding drug DB226 with a DNA hairpin) allows a more direct assessment of the relative merits of the distinct clustering algorithms. Additionally, means to assess the relative performance and differences between the algorithms, to dynamically select the initial cluster count, and to achieve faster data mining by "sieved clustering" were evaluated. Overall, it was found that there is no one perfect "one size fits all" algorithm for clustering MD trajectories and that the results strongly depend on the choice of atoms for the pairwise comparison. Some algorithms tend to produce homogeneously sized clusters, whereas others have a tendency to produce singleton clusters. Issues related to the choice of a pairwise metric, clustering metrics, which atom selection is used for the comparison, and about the relative performance are discussed. Overall, the best performance was observed with the average-linkage, means, and SOM algorithms. If the cluster count is not known in advance, the hierarchical or average-linkage clustering algorithms are recommended. Although these algorithms perform well, it is important to be aware of the limitations or weaknesses of each algorithm, specifically the high sensitivity to outliers with hierarchical, the tendency to generate homogenously sized clusters with means, and the tendency to produce small or singleton clusters with average-linkage.

Phosducin (Pdc) and phosducin-like protein (PhLP) regulate G protein-mediated signaling by binding to the betagamma subunit complex of heterotrimeric G proteins (Gbetagamma) and removing the dimer from cell membranes. The binding of Pdc induces a conformational change in the beta-propeller structure of Gbetagamma, creating a pocket between blades 6 and 7. It has been proposed that the isoprenyl group of Gbetagamma inserts into this pocket, stabilizing the Pdc.Gbetagamma structure and decreasing the affinity of the complex for the lipid bilayer. To test this hypothesis, the binding of Pdc and PhLP to several Gbetagamma dimers containing variants of the beta or gamma subunit was measured. These variants included modifications of the isoprenyl group (gamma), residues involved in the conformational change (beta), and residues lining the proposed prenyl pocket (beta). Switching prenyl groups from farnesyl to geranylgeranyl or vice versa had little effect on binding. However, alanine substitution of one residue in the beta subunit involved in the conformational change (W332) decreased binding 5-fold. Alanine substitution of certain residues within the prenyl pocket caused only minor decreases in binding, while a lysine substitution of T329 within the pocket inhibited binding 10-fold. Molecular modeling of the binding energy of the Pdc.Gbeta(1)gamma(2) complex required insertion of the geranylgeranyl group into the prenyl pocket in order to accurately predict the effects of prenyl pocket amino acid substitutions. Finally, a dimer containing a gamma subunit with no prenyl group (gamma(2)-C68S) decreased binding by nearly 20-fold. These results support the structural model in which the prenyl group escapes contact with the aqueous milieu by inserting into the prenyl pocket and stabilizing the Pdc-binding conformation of Gbetagamma.

The purpose of this study was to carry out a thorough search of the conformational space of various adenine-containing nucleotides, applying a previously published searching procedure, known as the representative method. This method, which reduces the number of starting conformations required to explore all the important regions of conformational space, appears to be successful in finding all (or nearly all) the putative low-energy conformations of each molecule.

In an earlier study, De Winter and Herdewijn (J. Med. Chem. 1996, 37, 4727-4737) studied the binding of various 5-substituted 2'-deoxyuridine substrates to thymidine kinase of herpes simplex virus type-1. They used a computational procedure that achieves good correlation with experimentally determined IC50 values. We applied an alternative procedure to the same deoxyuridine substrates, using only three readily calculated quantities-the binding energy, the molecular surface area, and a flexibility factor. Our simplified method achieves the same degree of correlation with the IC50 values as did the earlier procedure. We then applied this procedure to examine the binding of various 5-substituted pyrimidine 1,5-anhydrohexitol substrates to thymidine kinase.