Evaluating methods targeting Protein-Protein Interactions

scientifically visualization of a coronavirus  spike protein

Protein-protein interactions (PPIs) are becoming increasingly relevant in the pathology of many diseases, including cancer. The issue, however, is developing an effective way of targeting them. 

The latest advancements in methods targeting PPIs are designed to overcome the challenges limiting the conventional methods. Targeting PPIs offers another potential therapeutic target for diseases with complex biological profiles

PPIs are an integral part of the physiology of living organisms, as complexes which control biological pathways mediated by proteins. Critical cellular processes including DNA replication and translation cannot occur without functional-specific proteins. The network of PPIs, known as an interactome, is essential to facilitate important molecular mechanisms like signal transduction and cell death.

Dysfunctional PPIs can create problems, however, and have been identified in the pathology of a multitude of diseases. Understanding the mechanism of PPIs and developing methods to target aberrant ones has been a key strategy in drug development. The rationale behind this strategy is that the classic drug targets in diseases like cancer are often enzymes and receptors, hence PPIs represent an attractive therapeutic target due to the fact that current therapeutic targets are typically protein-based.

“PPIs are an integral part of the physiology of living organisms, as complexes which control biological pathways mediated by proteins.” 

Current challenges and conventional approaches in targeting PPIs

In comparison with the classic, small molecule drug discovery approach, the modulation of PPIs is more challenging. Whereas targets like receptors contain an obvious ligand-binding site for drug molecules to interact with, PPIs comprise a more complex structure. The following points summarise these challenges:

  • PPIs possess a high-affinity association due to the amino-acid residue arrangement. This makes it a challenge for small molecules to inhibit the strong interaction.
  • The surface of PPIs, known as the interface, is typically flat with a limited number of grooves. This makes it difficult for specifically designed small molecules to bind to.
  • The interface is highly hydrophobic, hence any compounds containing water would be repelled.

The flat interface remains the primary challenge facing the development of PPI modulators. In comparison with proteins like enzymes, which possess binding sites for complementary ligands, the interface proves difficult to find a matching molecule. However, dotting the interface are areas of amino acid residue that contribute to the binding-free energy, known as hot-spots. 

These regions are critical for optimal interactions between proteins. The significance of hot-spots proves it necessary to develop methods that design PPI modulators which are complementary to the hot-spots of PPI interfaces.

One example of a method targeting PPIs is virtual screening. One of the main issues in the development of PPI modulators is identifying the PPIs associated with the disease and whether they are ‘’druggable’ out of thousands available. Virtual screening can analyse protein surfaces to locate the binding site. 

From here, the binding sites are classified into two categories which determine whether a structure- or ligand-based approach is required. It is a popular method in systems biology, requiring fewer compounds to be screened in bioassays, reducing time and cost. Despite these benefits, virtual screening reports high false positive rates when identifying specific PPIs, which limits its use to initial screening only. 

Novel approaches to optimise PPIs: Meeting a clinical demand 

The prevalence of aberrant PPIs in diseases like cancer reinforces the necessity to develop strategies to better target these biological components. A recent clinical research paper highlights the importance of targeting of PPIs in stem cells of cancer. A PPI network was constructed from nine colon stem cell markers in order to identify potential genetic targets in patients. The combination of the PPI database and the ‘high-throughput gene profiles’ revealed a cluster of colon cancer stem cell (CSCs) related genes. 

From the cluster, the protein TMEM17 was identified as a novel CSC-related gene, whose depletion can “suppress the proliferation of colorectal cancer (CRC) cells and sensitise CRC cells to chemotherapy. The identification of the TMEM17 within a PPI network establishes a therapeutic target for CRC treatment, which further highlights the potential of targeting PPIs as an effective strategy in cancer.

A widely utilised method of detecting PPIs is Yeast-two-hybrid (Y2H) which has recently been optimised to overcome the challenges of methods like virtual screening. According to a 2021 publication “…recent advancements have made it possible to generate and analyse genome-wide PPI networks en masse by coupling Y2H with next-generation sequencing technology (NGS)”. 

The combination of NGS with Y2H is one of the recent innovative changes to the original Y2H. This innovative method now brings technology closer to developing genome-wide PPI networks. Despite the promising changes to optimise these methods, there are still challenges that need to be addressed. 

One of the primary problems with Y2H, like virtual screening, is the number of false positives that occur with autoactivation of Y2H-inducible reporter genes. This arises when either the DNA binding- or activation-domain activates the transcription of “Y2H reporter genes, irrespective of the presence of any PPI”. 

A recently published study was able to overcome this issue, however, by developing a type of conditional negative selection to screen for false positives. This represents a significant step in targeting PPIs: by removing the change of false positives, Y2H can more accurately identify the target PPIs in disease states.

The evolving methods of targeting PPIs highlight the need to integrate 3D structural information into systems biology. In PPI networks, proteins are defined as nodes which are connected by edges (interactions); however there is no reference to the structure of the proteins, nor the mechanism of the interactions involved. The introduction of computational methods however, could be used to “interrogate these interactions” which can complement the “available experimental evidence”. The addition of structural information enables a more informed understanding about the functionality of these PPI networks.

What does this mean for clinical research?

The continuous development of methods targeting PPIs allows researchers to gain better insights into the impact of micro changes of specific proteins, and the impact on a macro scale with the manifestation of diseases. In oncology especially, targeting PPIs opens the door to the development of therapeutic drugs with greater selectivity and specificity to rival conventional treatment.

Charlotte Di Salvo

Charlotte Di Salvo is the lead medical writer for PharmaFeatures, exploring the pharmaceutical industry, clinical research and academia.