Identification of Small-Molecule Frequent Hitters of Glutathione S-Transferase-Glutathione Interaction.

In high-throughput screening (HTS) campaigns, the binding of glutathione S-transferase (GST) to glutathione (GSH) is used for detection of GST-tagged proteins in protein-protein interactions or enzyme assays. However, many false-positives, so-called frequent hitters (FH), arise that either prevent GST/GSH interaction or interfere with assay signal generation or detection. To identify GST-FH compounds, we analyzed the data of five independent AlphaScreen-based screening campaigns to classify compounds that inhibit the GST/GSH interaction. We identified 53 compounds affecting GST/GSH binding but not influencing His-tag/Ni(2+)-NTA interaction and general AlphaScreen signals. The structures of these 53 experimentally identified GST-FHs were analyzed in chemoinformatic studies to categorize substructural features that promote interference with GST/GSH binding. Here, we confirmed several existing chemoinformatic filters and more importantly extended them as well as added novel filters that specify compounds with anti-GST/GSH activity. Selected compounds were also tested using different antibody-based GST detection technologies and exhibited no interference clearly demonstrating specificity toward their GST/GSH interaction. Thus, these newly described GST-FH will further contribute to the identification of FH compounds containing promiscuous substructures. The developed filters were uploaded to the OCHEM website (http://ochem.eu) and are publicly accessible for analysis of future HTS results.

Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds.

The article describes a classification system termed "extended functional groups" (EFG), which are an extension of a set previously used by the CheckMol software, that covers in addition heterocyclic compound classes and periodic table groups. The functional groups are defined as SMARTS patterns and are available as part of the ToxAlerts tool (http://ochem.eu/alerts) of the On-line CHEmical database and Modeling (OCHEM) environment platform. The article describes the motivation and the main ideas behind this extension and demonstrates that EFG can be efficiently used to develop and interpret structure-activity relationship models.

Identification of Small-Molecule Frequent Hitters from AlphaScreen High-Throughput Screens.

Although small-molecule drug discovery efforts have focused largely on enzyme, receptor, and ion-channel targets, there has been an increase in such activities to search for protein-protein interaction (PPI) disruptors by applying high-throughout screening (HTS)-compatible protein-binding assays. However, a disadvantage of these assays is that many primary hits are frequent hitters regardless of the PPI being investigated. We have used the AlphaScreen technology to screen four different robust PPI assays each against 25,000 compounds. These activities led to the identification of 137 compounds that demonstrated repeated activity in all PPI assays. These compounds were subsequently evaluated in two AlphaScreen counter assays, leading to classification of compounds that either interfered with the AlphaScreen chemistry (60 compounds) or prevented the binding of the protein His-tag moiety to nickel chelate (Ni(2+)-NTA) beads of the AlphaScreen detection system (77 compounds). To further triage the 137 frequent hitters, we subsequently confirmed by a time-resolved fluorescence resonance energy transfer assay that most of these compounds were only frequent hitters in AlphaScreen assays. A chemoinformatics analysis of the apparent hits provided details of the compounds that can be flagged as frequent hitters of the AlphaScreen technology, and these data have broad applicability for users of these detection technologies.

An approximation of the Cioslowski­Mixon bond order indexes using the AlteQ approach.

Fast and reliable prediction of bond orders in organic systems based upon experimentally measured quantities can be performed using electron density features at bond critical points (J Am Chem Soc 105:5061­5068, 1983; J Phys Org Chem 16:133­141, 2003; Acta Cryst B 61:418­428, 2005; Acta Cryst B 63:142­150, 2007). These features are outcomes of low-temperature high-resolution X-ray diffraction experiments. However, a time-consuming procedure of gaining these quantities makes the prediction limited. In the present work we have employed an empirical approach AlteQ (J Comput Aided Mol Des 22:489­505, 2008) for evaluation of electron density properties. This approach uses a simple exponential function derived from comparison of electron density, gained from high-resolution X-ray crystallography, and distance to atomic nucleus what allows calculating density distribution in time-saving manner and gives results which are very close to experimental ones. As input data AlteQ accepts atomic coordinates of isolated molecules or molecular ensembles (for instance, protein­protein complexes or complexes of small molecules with proteins, etc.). Using AlteQ characteristics we have developed regression models predicting Cioslowski­Mixon bond order (CMBO) indexes (J Am Chem Soc 113(42):4142­4145, 1991). The models are characterized by high correlation coefficients lying in the range from 0.844 to 0.988 dependently on the type of covalent bond, thereby providing a bonding quantification that is in reasonable agreement with that obtained by orbital theory. Comparative analysis of CMBOs approximated using topological properties of AlteQ and experimental electron densities has shown that the models can be used for fast determination of bond orders directly from X-ray crystallography data and confirmed that AlteQ characteristics can replace experimental ones with satisfactory extent of accuracy.

ToxAlerts: a Web server of structural alerts for toxic chemicals and compounds with potential adverse reactions.

The article presents a Web-based platform for collecting and storing toxicological structural alerts from literature and for virtual screening of chemical libraries to flag potentially toxic chemicals and compounds that can cause adverse side effects. An alert is uniquely identified by a SMARTS template, a toxicological endpoint, and a publication where the alert was described. Additionally, the system allows storing complementary information such as name, comments, and mechanism of action, as well as other data. Most importantly, the platform can be easily used for fast virtual screening of large chemical datasets, focused libraries, or newly designed compounds against the toxicological alerts, providing a detailed profile of the chemicals grouped by structural alerts and endpoints. Such a facility can be used for decision making regarding whether a compound should be tested experimentally, validated with available QSAR models, or eliminated from consideration altogether. The alert-based screening can also be helpful for an easier interpretation of more complex QSAR models. The system is publicly accessible and tightly integrated with the Online Chemical Modeling Environment (OCHEM, http://ochem.eu). The system is open and expandable: any registered OCHEM user can introduce new alerts, browse, edit alerts introduced by other users, and virtually screen his/her data sets against all or selected alerts. The user sets being passed through the structural alerts can be used at OCHEM for other typical tasks: exporting in a wide variety of formats, development of QSAR models, additional filtering by other criteria, etc. The database already contains almost 600 structural alerts for such endpoints as mutagenicity, carcinogenicity, skin sensitization, compounds that undergo metabolic activation, and compounds that form reactive metabolites and, thus, can cause adverse reactions. The ToxAlerts platform is accessible on the Web at http://ochem.eu/alerts, and it is constantly growing.