Highly Accurate Filters to Flag Frequent Hitters in AlphaScreen Assays by Suggesting their Mechanism.

AlphaScreen is one of the most widely used assay technologies in drug discovery due to its versatility, dynamic range and sensitivity. However, a presence of false positives and frequent hitters contributes to difficulties with an interpretation of measured HTS data. Although filters do exist to identify frequent hitters for AlphaScreen, they are frequently based on privileged scaffolds. The development of such filters is time consuming and requires deep domain knowledge. Recently, machine learning and artificial intelligence methods are emerging as important tools to advance drug discovery and chemoinformatics, including their application to identification of frequent hitters in screening assays. However, the relative performance and complementarity of the Machine Learning and scaffold-based techniques has not yet been comprehensively compared. In this study, we analysed filters based on the privileged scaffolds with filters built using machine learning. Our results demonstrate that machine-learning methods provide more accurate filters for identification of frequent hitters in AlphaScreen assays than scaffold-based methods and can be easily redeveloped once new data are measured. We present highly accurate models to identify frequent hitters in AlphaScreen assays.

Luciferase Advisor: High-Accuracy Model To Flag False Positive Hits in Luciferase HTS Assays.

Firefly luciferase is an enzyme that has found ubiquitous use in biological assays in high-throughput screening (HTS) campaigns. The inhibition of luciferase in such assays could lead to a false positive result. This issue has been known for a long time, and there have been significant efforts to identify luciferase inhibitors in order to enhance recognition of false positives in screening assays. However, although a large amount of publicly accessible luciferase counterscreen data is available, to date little effort has been devoted to building a chemoinformatic model that can identify such molecules in a given data set. In this study we developed models to identify these molecules using various methods, such as molecular docking, SMARTS screening, pharmacophores, and machine learning methods. Among the structure-based methods, the pharmacophore-based method showed promising results, with a balanced accuracy of 74.2%. However, machine-learning approaches using associative neural networks outperformed all of the other methods explored, producing a final model with a balanced accuracy of 89.7%. The high predictive accuracy of this model is expected to be useful for advising which compounds are potential luciferase inhibitors present in luciferase HTS assays. The models developed in this work are freely available at the OCHEM platform at http://ochem.eu .

Peptide utilizing carbon starvation gene yjiY is required for flagella mediated infection caused by Salmonella.

Peptide metabolism forms an important part of the metabolic network of Salmonella and to acquire these peptides the pathogen possesses a number of peptide transporters. While various peptide transporters known in Salmonella are well studied, very little is known about the carbon starvation (cst) genes, cstA and yjiY, which are also predicted to be involved in peptide metabolism. We investigated the role of these genes in the metabolism and pathogenesis of Salmonella and demonstrated for the first time that cst genes actually participate in transport of specific peptides in Salmonella. Further, we established that the carbon starvation gene yjiY affects the expression of flagella leading to poor adhesion of the bacterium to host cells. In contrast with the previously reported role of the gene cstA in virulence of Salmonella in C. elegans, we showed that yjiY is required for successful colonization of Salmonella in the mouse gut. Thus, cst genes not only contribute to the metabolism of Salmonella but also influence its virulence.

Engineering ß-sheets employing N-methylated heterochiral amino acids.

There is a lack of functional group diversity in the reverse turn motifs nucleating a ß-sheet conformation in designed peptides, proteins and foldamers. The majority of these sequences consist of d-Pro-l-Pro, d-Pro-Gly or Asn-Gly as the turn inducing motif restricting their biological application and physicochemical modulation. In this report, for the first time we elucidate that N-methylation of heterochiral amino acids in linear peptides nucleates ß-sheet conformation without the necessity of having a ring or covalent constraint at the reverse turn. Our results show that d-Pro can be conveniently substituted by any other N-methylated d-amino acid followed by an N-methylated l-amino acid or sarcosine to adopt a ßII' turn inducing the ß-sheet folding. Furthermore, we reveal that a single amino acid either at the i + 1 or i + 2 site of the reverse turn can modulate the right-handed twist, which eventually dictates the extent of the foldedness of the ß-hairpin.