Challenges, Opportunities, and RNA Bioinformatics at Nexco

In recent years, and especially after being the star component of various vaccines against Covid-19, RNA has emerged as a major player in therapeutic interventions. This includes not only messenger RNA (mRNA) as the information code to produce therapeutic proteins inside the body, as with mRNA Covid-19 vaccines, but also as the target of small molecules that alter their function and stability, most often by inhibiting their translation.

Therefore, while traditionally overlooked because most pharma and medicine focus on proteins as targets, mRNA now stands as an exciting new target for drug discovery, particularly with the advent of small-molecule drugs that bind to it specifically. Yet, alongside this potential come some considerable challenges, including issues of sequence specificity, off-target effects, and delivery mechanisms. Bioinformatics plays a crucial role in overcoming many of these hurdles by providing tools and models that can refine target selection, predict molecular interactions, and streamline drug development processes.

The Promise of RNA-Targeting Therapeutics

RNA-targeting therapeutics represent a breakthrough in the treatment of diseases for which protein-targeting drugs are either inadequate or unavailable. mRNA-based vaccines, for example, offer the advantage that minimal amounts of material introduced into cells can translate into large numbers of copies of the epitope-displaying protein that ultimately triggers immunity.

As a target, modulating mRNA directly with small molecules allows the regulation (most often blockage) of pathological gene expression right at its source. This offers novel ways to treat genetic disorders, cancers, and other diseases that arise from aberrant protein production.

However, translating this potential into viable therapies is no small feat. RNA molecules are highly dynamic and can adopt complex secondary and tertiary structures, making it difficult to identify stable binding sites for small molecules. Additionally, RNA’s ubiquitous presence in the cell increases the risk of off-target interactions, which can lead to unintended side effects.

The Role of Bioinformatics in the Discovery of RNA-Targeting Drugs

This is where bioinformatics steps in, acting as a critical enabler for RNA-targeting drug discovery. Computational tools can be used to predict RNA structures with high accuracy, map out potential binding pockets, rapidly scan off-target sites that could elicit undesired secondary effects, and to simulate the interactions between RNA molecules and small molecules. Here are some key areas where “RNA bioinformatics” is making a significant impact:

RNA Structure Prediction

The prediction of RNA folding is essential for identifying stable and functional regions that are suitable drug targets. Despite its apparent simplicity as compared to proteins, with only four rather than 20 letters in the alphabet, prediction of the three-dimensional structures of RNA molecules is hard.

In CASP, the organization best-known for tracking the state of the art of protein structure prediction and from which AlphaFold and similar models came out, a specialized sub-track of the context is devoted to assessing the state of the art of RNA structure prediction. Their latest report, in CASP15, found that although the global fold of RNA targets is usually quite well captured, substantial challenges remain in modeling fine details (analysis here). In summary, it turns out that despite RNA being simpler, we can’t today predict the 3D structures of RNA molecules as reliably and accurately as we can do for most proteins.

Binding Site Identification

Bioinformatics platforms also allow researchers to identify druggable pockets on the RNA structure where small molecules can bind effectively. This involves modeling the dynamics of RNA and small molecule interactions, narrowing down the best candidates for in-vitro validation.

A particular role is played here by the most modern models for protein structure prediction, like Deepmind’s AlphaFold 3 and (actually appearing first) the AI-based program RoseTTAFold-AllAtoms from D. Baker’s lab — both Nobel Prize winners in 2024! Both of these programs, as well as others that came out afterwards, were born from the idea of modeling protein structures but can actually model DNA and RNA as well, and also ions and some small molecules. Thus, these modern AI models can handle all the relevant kinds of molecules involved in RNA investigations. You can know more about these “multimodal structure prediction systems” in these blog posts:

• AlphaFold 3 Advances the Future AI Technologies for Pharma and Biotech
• AlphaFold and Other AI Tools for Molecular Structure Go Beyond Proteins

Off-Target Prediction

RNA molecules are obviously different from proteins; however, being formed by only 4 basic units, they have less chemical variability. Together with their higher flexibility and less structural variability, this increases the risk of a drug binding to non-target RNAs. Bioinformatics tools enable the prediction of potential off-target effects by comparing RNA binding sites across the transcriptome, improving specificity, and reducing unwanted side effects. This was typically executed at the sequence level, which still today remains by far the fastest way; however, the new multimodal molecular modeling programs mentioned in the previous section can enable, in principle, investigations based in 3D docking by co-folding the tested RNA molecules with the binders.

Optimization of Drug Candidates

Once a small molecule is identified as a potential RNA-binding drug, bioinformatics, or rather cheminformatics, algorithms can in principle help to optimize its chemical properties to improve affinity, specificity, and bioavailability, as in any other effort to create a new drug.

The Future of RNA-Targeting Therapeutics

Overcoming Challenges

Despite all the highly promising advances, several challenges remain in the development of RNA-targeting therapeutics. Delivery mechanisms for small molecules targeting RNA are not yet fully optimized, with issues related to stability, tissue targeting, and cellular uptake still needing refinement. Furthermore, as the RNA space is relatively new, the computational models are continually evolving and improving — recall for example that a few sections above we mentioned their 3D modeling isn’t as accurate as that available for protein structures. This all means that ongoing collaboration between bioinformatics specialists, molecular biologists, and chemists will remain vital.

Looking Ahead from Nexco’s Perspective

Looking ahead, the integration of bioinformatics with RNA-targeting drug discovery will likely accelerate the pace at which new therapies reach the market. As always, bioinformatics will play a central role in transforming this promising therapeutic concept into a reality for patients suffering from a wide range of diseases. In particular, as computational models become more accurate and predictive power increases, and — we pose — especially as multimodal systems like AlphaFold 3 evolve further, all drug development pipelines will benefit from reduced costs and faster timelines, and this certainly includes RNA as targets.

With years of experience looking at RNA-related data, we at Nexco specialize in providing cutting-edge bioinformatics solutions to support drug discovery initiatives already up from the fundamental biological exploratory studies when looking for targets. By leveraging advanced algorithms, machine learning, and now RNA-capable structural modeling, we enable pharmaceutical companies to unlock the full potential of RNA as a target for therapies.

Key references

• Assessment of three-dimensional RNA structure prediction in CASP15
• Drugging RNA - Nature Biotechnology