The Privileged Chemical Space Predictor (PCSP): a computer program that identifies privileged chemical space from screens of modularly assembled chemical libraries.

Modularly assembled combinatorial libraries are often used to identify ligands that bind to and modulate the function of a protein or a nucleic acid. Much of the data from screening these compounds, however, is not efficiently utilized to define structure-activity relationships (SAR). If SAR data are accurately constructed, it can enable the design of more potent binders. Herein, we describe a computer program called Privileged Chemical Space Predictor (PCSP) that statistically determines SAR from high-throughput screening (HTS) data and then identifies features in small molecules that predispose them for binding a target. Features are scored for statistical significance and can be utilized to design improved second generation compounds or more target-focused libraries. The program's utility is demonstrated through analysis of a modularly assembled peptoid library that previously was screened for binding to and inhibiting a group I intron RNA from the fungal pathogen Candida albicans.

Two-dimensional combinatorial screening identifies specific aminoglycoside-RNA internal loop partners.

Herein is described the identification of RNA internal loops that bind to derivatives of neomycin B, neamine, tobramycin, and kanamycin A. RNA loop-ligand partners were identified by a two-dimensional combinatorial screening (2DCS) platform that probes RNA and chemical spaces simultaneously. In 2DCS, an aminoglycoside library immobilized onto an agarose microarray was probed for binding to a 3 x 3 nucleotide RNA internal loop library (81,920 interactions probed in duplicate in a single experiment). RNAs that bound aminoglycosides were harvested from the array via gel excision. RNA internal loop preferences for three aminoglycosides were identified from statistical analysis of selected structures. This provides consensus RNA internal loops that bind these structures and include: loops with potential GA pairs for the neomycin derivative, loops with potential GG pairs for the tobramycin derivative, and pyrimidine-rich loops for the kanamycin A derivative. Results with the neamine derivative show that it binds a variety of loops, including loops that contain potential GA pairs that also recognize the neomycin B derivative. All studied selected internal loops are specific for the aminoglycoside that they were selected to bind. Specificity was quantified for 16 selected internal loops by studying their binding to each of the arrayed aminoglycosides. Specificities ranged from 2- to 80-fold with an average specificity of 20-fold. These studies show that 2DCS is a unique platform to probe RNA and chemical space simultaneously to identify specific RNA motif-ligand interactions.

Probing a 2-aminobenzimidazole library for binding to RNA internal loops via two-dimensional combinatorial screening.

There are many potential RNA drug targets in bacterial, viral, and human transcriptomes. However, there are few small molecules that modulate RNA function. This is due, in part, to a lack of fundamental understanding about RNA-ligand interactions including the types of small molecules that bind to RNA structural elements and the RNA structural elements that bind to small molecules. In an effort to better understand RNA-ligand interactions, we diversified the 2-aminobenzimidazole core (2AB) and probed the resulting library for binding to a library of RNA internal loops. We chose the 2AB core for these studies because it is a privileged scaffold for binding RNA based on previous reports. These studies identified that N-methyl pyrrolidine, imidazole, and propylamine diversity elements at the R1 position increase binding to internal loops; variability at the R2 position is well tolerated. The preferred RNA loop space was also determined for five ligands using a statistical approach and identified trends that lead to selective recognition.

Small molecule microarrays of RNA-focused peptoids help identify inhibitors of a pathogenic group I intron.

Peptoids that inhibit the group I intron RNA from Candida albicans, an opportunistic pathogen that kills immunocompromised hosts, have been identified using microarrays. The arrayed peptoid library was constructed using submonomers with moieties similar to ones found in small molecules known to bind RNA. Library members that passed quality control analysis were spotted onto a microarray and screened for binding to the C. albicans group I intron ribozyme. Each ligand binder identified from microarray-based screening inhibited self-splicing in the presence of 1 mM nucleotide concentration of bulk yeast tRNA with IC(50)'s between 150 and 2200 microM. The binding signals and the corresponding IC(50)'s were used to identify features in the peptoids that predispose them for RNA binding. After statistical analysis of the peptoids' structures that bind, a second generation of inhibitors was constructed using these important features; all second generation inhibitors have improved potencies with IC(50)'s of <100 microM. The most potent inhibitor is composed of one phenylguanidine and three tryptamine submonomers and has an IC(50) of 31 microM. This compound is 6-fold more potent than pentamidine, a clinically used drug that inhibits self-splicing. These results show that (i) modulators of RNA function can be identified by designing RNA-focused chemical libraries and screening them via microarray; (ii) statistical analysis of ligand binders can identify features in leads that predispose them for binding to their targets; and (iii) features can then be programmed into second generation inhibitors to design ligands with improved potencies.