In the realm of drug discovery, understanding the mechanism of action of therapeutic compounds is crucial, and ribosome profiling (Ribo-seq) has emerged as a powerful tool for this purpose. Despite its strengths, the application of Ribo-seq in a high-throughput manner poses practical limitations. The resource-intensive nature of the technology makes it challenging to triage large numbers of compounds effectively.
The following example nicely illustrates the challenge and we also discuss our exciting new platform “c-MAST” which addresses this.

Figure 1. Ribosome density plot illustrating how PF864 treated samples (red trace) induces ribosomes to stall (peak of high density just downstream of the start of the transcripts protein coding region), with ribosome occupancy beyond this point diminished relative to control samples (green trace) indicating a decrease in protein synthesis. Plot generated by EIRNABio using publicly available data from Lintner et al., 2017. PLoS Biol 15(3):e2001882.
PSCK9 is a protein that regulates plasma LDL cholesterol levels by preventing the recycling of the LDL-receptor to the plasma membrane of hepatocytes. It is becoming an area of significant focus for a number of companies. The effectiveness of Ribo-seq has previously been demonstrated in elucidating the mechanisms behind compounds like PF864, which targets PCSK9 to lower LDL cholesterol. By visualizing ribosome density across various transcripts, Ribo-seq provides detailed insights into how these compounds influence protein synthesis, showcasing their specific effects on targeted genes.
However, while compounds such as PF864 may demonstrate efficacy, they can also elicit significant off-target effects, impacting other genes beyond their intended target. In fact, studies have identified 17 additional genes affected by PF864, highlighting the challenges of specificity in drug action. Ribo-seq stands out as an invaluable tool for capturing these off-target translational effects on a genome-wide scale, allowing researchers to map unintended interactions that might compromise therapeutic safety.
Notwithstanding the power of Ribo–seq it would be challenging to use it to triage large numbers of compounds. To address this, we have developed c–MAST (customized Multiplexed ASsessment of Translation), a high-throughput screening platform designed to facilitate the simultaneous assessment of the effect of many compounds on translation. By employing c–MAST, researchers can rapidly identify compounds that show significant translational effects across various genes, enhancing the ability to optimize drug specificity and minimize off-target effects.

Figure 2. Scatter plot highlighting the results from two of EIRNABio’s differential ribosome distribution detection algorithms (D-Corr & D-Max). The 17 genes identified in the original paper are labelled, with each being positively identified as exhibiting a differential ribosome distribution pattern following PF864 treatment by at least one of the detection algorithms. Plot generated by EIRNABio using publicly available data from Lintner et al., 2017. PLoS Biol 15(3):e2001882.
Thus in the example above, where 17 off-targets have been identified if someone wanted to undertake a screening of a library of 500 similar compounds to PF864 in order to establish specific compounds which only targeted the PSCK9 genes and not the other 17, this would likely necessitate the generation of 1500 ribosome profiling datasets (i.e. 500 experiments in triplicate). Alternatively an approach using reporter assays (e.g. GFP or luciferase reporters) could be used. This would necessitate high–throughput screening incorporating ca. 27,000 individual experimental conditions (i.e. 500 compounds in triplicate with 18 genes). In contrast c–MAST will provide the same information from a single multiplexed assay.
In summary, while Ribo–seq is adept at revealing both the mechanisms of action and off-target effects of compounds, c–MAST represents a promising advancement that will streamline the high-throughput screening process in the search for safe and effective therapeutics.