Multiple protein structures and multiple ligands: effects on the apparent goodness of virtual screening results

As an extension to a previous published study (McGaughey et al., J Chem Inf Model 47:1504–1519, 2007) comparing 2D and 3D similarity methods to docking, we apply a subset of those virtual screening methods (TOPOSIM, SQW, ROCS-color, and Glide) to a set of protein/ligand pairs where the protein is the target for docking and the cocrystallized ligand is the target for the similarity methods. Each protein is represented by a maximum of five crystal structures. We search a diverse subset of the MDDR as well as a diverse small subset of the MCIDB, Merck’s proprietary database. It is seen that the relative effectiveness of virtual screening methods, as measured by the enrichment factor, is highly dependent on the particular crystal structure or ligand, and on the database being searched. 2D similarity methods appear very good for the MDDR, but poor for the MCIDB. However, ROCS-color (a 3D similarity method) does well for both databases. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Determination of cyclamate in foods by ultraperformance liquid chromatography/tandem mass spectrometry

A highly sensitive and selective method that requires minimal sample preparation was developed for the confirmation and quantitation of cyclamate in a variety of foods by high-performance liquid chromatography/tandem mass spectrometry (HPLC/MS/MS). Sample preparation consisted of homogenization followed by extraction and dilution of cyclamate with water. HPLC separation was achieved using a bridged ethyl hybrid C18 high-pressure column with a mobile phase consisting of 0.15% acetic acid and methanol. Under electrospray ionization negative conditions, quantitation was achieved by monitoring the fragment m/z = 79.7 while also collecting parent ion m/z = 177.9. Two food matrixes, diet soda and jelly, were subjected to a validation procedure in order to evaluate the applicability of the method. The cyclamate limit of detection for both matrixes was determined to be 0.050 microg/g with a limit of quantitation of 0.150 microg/g. The correlation coefficient of the calibration curves was >0.9998 from 0.0005 to 0.100 microg/mL. The method has been used for the determination of cyclamate in several foods and the results are presented.     

FLOG: A system to select ‘quasi-flexible’ ligands complementary to a receptor of known three-dimensional structure

Summary We present a system, FLOG (Flexible Ligands Oriented on Grid), that searches a database of 3D coordinates to find molecules complementary to a macromolecular receptor of known 3D structure. The philosophy of FLOG is similar to that reported for DOCK [Shoichet, B.K. et al., J. Comput. Chem., 13 (1992) 380]. In common with that system, we use a match center representation of the volume of the binding cavity and we use a clique-finding algorithm to generate trial orientations of each candidate ligand in the binding site. Also we use a grid representation of the receptor to assess the fit of each orientation. We have introduced a number of novel features within this paradigm. First, we address ligand flexibility by including up to 25 explicit conformations of each structure in our databases. Nonhydrogen atoms in each database entry are assigned one of seven atom types (anion, cation, donor, acceptor, polar, hydrophobic and other) based on their local bonded chemical environments. Second, we have devised a new grid-based scoring function compatible with this heavy atom representation of the ligands. This includes several potentials (electrostatic, hydrogen bonding, hydrophobic and van der Waals) calculated from the location of the receptor atoms. Third, we have improved the fitting stage of the search. Initial dockings are generated with a more efficient clique-finding algorithm. This new algorithm includes the concept of essential points, match centers that must be paired with a ligand atom. Also, we introduce the use of a rapid simplex-based rigid-body optimizer to refine the orientations. We demonstrate, using dihydrofolate reductase as a sample receptor, that the FLOG system can select known inhibitors from a large database of drug-like compounds.     

Characterization of cold spray titanium deposits by X-ray microscopy and microtomography.

Cold gas dynamic spray (cold spray) is a rapid deposition technology in which particles deposit at velocities above the speed of sound (approximately 340 ms-1). Generally, porosity forms in cold spray deposits due to insufficient deformation of particles. In this study, the unique capability of the X-ray microscopy and microtomography is utilized to visualize the internal structure of deposited material. The results show that this characterization technique successfully reveals porosities in the cold spray commercial purity (CP) titanium structure. Furthermore, microtomography images confirmed the experimental results for porosity measurements in which helium (compared with nitrogen) as carrier gas significantly decreases porosity in cold spray CP titanium.     

In Vivo Low-level Light Therapy Increases Cytochrome Oxidase in Skeletal Muscle

Low-level light therapy (LLLT) increases survival of cultured cells, improves behavioral recovery from neurodegeneration and speeds wound healing. These beneficial effects are thought to be mediated by upregulation of mitochondrial proteins, especially the respiratory enzyme cytochrome oxidase. However, the effects of in vivo LLLT on cytochrome oxidase in intact skeletal muscle have not been previously investigated. We used a sensitive method for enzyme histochemistry of cytochrome oxidase to examine the rat temporalis muscle 24 h after in vivo LLLT. The findings showed for the first time that in vivo LLLT induced a dose- and fiber type-dependent increase in cytochrome oxidase in muscle fibers. LLLT was particularly effective at enhancing the aerobic capacity of intermediate and red fibers. The findings suggest that LLLT may enhance the oxidative energy metabolic capacity of different types of muscle fibers, and that LLLT may be used to enhance the aerobic potential of skeletal muscle.     

Drug-like density: a method of quantifying the "bindability" of a protein target based on a very large set of pockets and drug-like ligands from the Protein Data Bank

One approach to estimating the "chemical tractability" of a candidate protein target where we know the atomic resolution structure is to examine the physical properties of potential binding sites. A number of other workers have addressed this issue. We characterize ~290,000 "pockets" from ~42,000 protein crystal structures in terms of a three parameter "pocket space": volume, buriedness, and hydrophobicity. A metric DLID (drug-like density) measures how likely a pocket is to bind a drug-like molecule. This is calculated from the count of other pockets in its local neighborhood in pocket space that contain drug-like cocrystallized ligands and the count of total pockets in the neighborhood. Surprisingly, despite being defined locally, a global trend in DLID can be predicted by a simple linear regression on log(volume), buriedness, and hydrophobicity. Two levels of simplification are necessary to relate the DLID of individual pockets to "targets": taking the best DLID per Protein Data Bank (PDB) entry (because any given crystal structure can have many pockets), and taking the median DLID over all PDB entries for the same target (because different crystal structures of the same protein can vary because of artifacts and real conformational changes). We can show that median DLIDs for targets that are detectably homologous in sequence are reasonably similar and that median DLIDs correlate with the "druggability" estimate of Cheng et al. (Nature Biotechnology 2007, 25, 71-75).     

Three useful dimensions for domain applicability in QSAR models using random forest

One popular metric for estimating the accuracy of prospective quantitative structure-activity relationship (QSAR) predictions is based on the similarity of the compound being predicted to compounds in the training set from which the QSAR model was built. More recent work in the field has indicated that other parameters might be equally or more important than similarity. Here we make use of two additional parameters: the variation of prediction among random forest trees (less variation among trees indicates more accurate prediction) and the prediction itself (certain ranges of activity are intrinsically easier to predict than others). The accuracy of prediction for a QSAR model, as measured by the root-mean-square error, can be estimated by cross-validation on the training set at the time of model-building and stored as a three-dimensional array of bins. This is an obvious extension of the one-dimensional array of bins we previously proposed for similarity to the training set [Sheridan et al. J. Chem. Inf. Comput. Sci.2004, 44, 1912-1928]. We show that using these three parameters simultaneously adds much more discrimination in prediction accuracy than any single parameter. This approach can be applied to any QSAR method that produces an ensemble of models. We also show that the root-mean-square errors produced by cross-validation are predictive of root-mean-square errors of compounds tested after the model was built.     

Measuring preferential attachment in growing networks with missing-timelines using Markov chain Monte Carlo

Preferential attachment is widely recognised as the principal driving force behind the evolution of many growing networks, and measuring the extent to which it occurs during the growth of a network is important for explaining its overall structure. Conventional methods require that the timeline of a growing network is known, that is, the order in which the nodes of the network appeared in time is available. But growing network datasets are commonly accompanied by missing-timelines, in which instance the order of the nodes in time cannot be readily ascertained from the data. To address this shortcoming, we propose a Markov chain Monte Carlo algorithm for measuring preferential attachment in growing networks with missing-timelines. Key to our approach is that any growing network model gives rise to a probability distribution over the space of networks. This enables a growing network model to be fitted to a growing network dataset with missing-timeline, allowing not only for the prevalence of preferential attachment to be estimated as a model parameter, but the timeline also. Parameter estimation is achieved by implementing a novel Metropolis–Hastings sampling scheme for updating both the preferential attachment parameter and timeline. A simulation study demonstrates that our method accurately measures the occurrence of preferential attachment in networks generated according to the underlying model. What is more, our approach is illustrated on a small sub-network of the United States patent citation network. Since the timeline for this example is in fact known, we are able to validate our approach against the conventional methods, showing that they give mutually consistent estimates.     

Conformational product control in the low-temperature photochemistry of cyclopropylcarbenes

[reaction: see text] Different conformers of two fused-ring cyclopropylchlorocarbenes are shown to undergo distinct, wavelength-selectable, photochemistry in low-temperature matrixes. Conformers with H and Cl syn give ring expansion predominantly, whereas those with H and Cl anti mainly fragment.