SCAM Detective: Accurate Predictor of Small, Colloidally Aggregating Molecules
| Autor: | Adam Yasgar, Ganesha Rai, Daniel Korn, Joshua Hochuli, Anton Simeonov, Rodolpho C. Braga, Alexander Tropsha, Alexey V. Zakharov, Stephen J. Capuzzi, Eugene N. Muratov, Vinicius M. Alves, Kyle H. Bowler |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
010404 medicinal & biomolecular chemistry
010304 chemical physics Computer science General Chemical Engineering 0103 physical sciences False positive paradox General Chemistry Computational biology Library and Information Sciences 01 natural sciences 0104 chemical sciences Computer Science Applications High-Throughput Screening Assays |
| Zdroj: | Journal of chemical information and modeling. 60(8) |
| ISSN: | 1549-960X |
| Popis: | Small, colloidally aggregating molecules (SCAMs) are the most common source of false positives in high-throughput screening (HTS) campaigns. Although SCAMs can be experimentally detected and suppressed by the addition of detergent in the assay buffer, detergent sensitivity is not routinely monitored in HTS. Computational methods are thus needed to flag potential SCAMs during HTS triage. In this study, we have developed and rigorously validated quantitative structure-interference relationship (QSIR) models of detergent-sensitive aggregation in several HTS campaigns under various assay conditions and screening concentrations. In particular, we have modeled detergent-sensitive aggregation in an AmpC β-lactamase assay, the preferred HTS counter-screen for aggregation, as well as in another assay that measures cruzain inhibition. Our models increase the accuracy of aggregation prediction by ∼53% in the β-lactamase assay and by ∼46% in the cruzain assay compared to previously published methods. We also discuss the importance of both assay conditions and screening concentrations in the development of QSIR models for various interference mechanisms besides aggregation. The models developed in this study are publicly available for fast prediction within the SCAM detective web application (https://scamdetective.mml.unc.edu/). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|