Computational Analysis and Predictive Cheminformatics Modeling of Small Molecule Inhibitors of Epigenetic Modifiers

• Published in: PloS one 2016-09, Vol.11 (9), p.e0083032-e0083032
• Main Authors: Jamal, Salma, Arora, Sonam, Scaria, Vinod
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Animals; Antisense RNA; Bayesian analysis; Biological properties; Biology and life sciences; Biomolecules; Cancer; Computation; Computational Biology - methods; Computer and Information Sciences; Computer applications; Data mining; Datasets; Deoxyribonucleic acid; DNA; DNA methylation; Drug Discovery - methods; Epigenesis, Genetic - drug effects; Epigenetic inheritance; Filtration; Gene expression; Gene regulation; Genes; Genomes; Genomics; High-Throughput Screening Assays; Histone Demethylases - drug effects; Histone Methyltransferases; Histone-Lysine N-Methyltransferase - drug effects; Humans; Informatics; Inhibitors; Learning algorithms; Machine learning; Mathematical models; Medical screening; Medicine and Health Sciences; Models, Theoretical; Molecular modelling; Pharmacology; Pharmacophores; Physical Sciences; Post-translation; Prediction models; R&D; Reproducibility of Results; Research & development; Research and Analysis Methods; Ribonucleic acid; RNA; Sensitivity and Specificity; Small Molecule Libraries; Structure-Activity Relationship; Transferases
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0083032

The dynamic and differential regulation and expression of genes is majorly governed by the complex interactions of a subset of biomolecules in the cell operating at multiple levels starting from genome organisation to protein post-translational regulation. The regulatory layer contributed by the epigenetic layer has been one of the favourite areas of interest recently. This layer of regulation as we know today largely comprises of DNA modifications, histone modifications and noncoding RNA regulation and the interplay between each of these major components. Epigenetic regulation has been recently shown to be central to development of a number of disease processes. The availability of datasets of high-throughput screens for molecules for biological properties offer a new opportunity to develop computational methodologies which would enable in-silico screening of large molecular libraries. In the present study, we have used data from high throughput screens for the inhibitors of epigenetic modifiers. Computational predictive models were constructed based on the molecular descriptors. Machine learning algorithms for supervised training, Naive Bayes and Random Forest, were used to generate predictive models for the small molecule inhibitors of histone methyl-transferases and demethylases. Random forest, with the accuracy of 80%, was identified as the most accurate classifier. Further we complemented the study with substructure search approach filtering out the probable pharmacophores from the active molecules leading to drug molecules. We show that effective use of appropriate computational algorithms could be used to learn molecular and structural correlates of biological activities of small molecules. The computational models developed could be potentially used to screen and identify potential new biological activities of molecules from large molecular libraries and prioritise them for in-depth biological assays. To the best of our knowledge, this is the first and most comprehensive computational analysis towards understanding activities of small molecules inhibitors of epigenetic modifiers.