RNAs have been unravelled as unique molecules playing a critical role in developmental and physiological processes in all living organisms. RNA is involved in progression of different diseases, including infectious diseases (e.g. HIV, AIDS, hepatitis C), metabolic diseases (e.g. diabetes, cancer) and triplet repeat disorders (e.g. myotonic dystrophy, Huntington’s disease). Recently, the number of RNA-based molecular targets has grown, with a detailed elucidation of their structural and functional relationship being provided. In addition, several small molecule inhibitors have been successfully developed for a variety of RNA molecules .
The Life Chemicals RNA Focused Library comprises over 3,400 compounds with predicted RNA- binding activity. The library has been designed with two ligand-based approaches:
2D Similarity Search
Initially, a set of about 750 known RNA-binding molecules has been collected from the literature. It was narrowed down by binding activity via discarding compounds with low dissociation constant (Kd > 10 μM). At the next step, 2D similarity search has been performed against Life Chemicals HTS Stock Compound Collection with Tanimoto and Tversky index values of ≥ 0.80 and ≥ 0.85, respectively. Fragment Based/Chemical Hashed Fingerprints were used to compare the compound structures. Finally, PAINS compounds, as well as those with "bad" and reactive groups, have been filtered out from the resulting compound set.
The same training set of known RNA-binding molecules has been used to build a Bayesian model (Bayesian categorization methodology) to distinguish “good” molecules from the compounds with known activity. Both molecular fingerprints (circular FCFP6 fingerprints) and molecular properties (MW, No. of HBA, No. of HBD, LogP, PSA, No. of rotatable bonds, No. of rings) were involved in the construction of Bayesian model. At the next step, the model has been applied to Life Chemicals HTS Stock Compound Collection in order to predict RNA-binding compounds. PAINS compounds, as well as those with "bad" and reactive groups, have been filtered out from the resulting compound set.
Fig 1. Some representative compounds from Life Chemicals RNA Focused Library.
1. Mehta A, Sonam S, Gouri I, Loharch S, Sharma DK, Parkesh R. SMMRNA: a database of small molecule modulators of RNA. Nucleic Acids Res. 2014, 42 (Database issue):D132-41. doi: 10.1093/nar/gkt976.