Why Protein-Protein Interactions (PPIs) Matter in Therapeutic Discovery

What Are Protein-Protein Interactions (PPIs)?

Protein-protein interactions (PPIs) refer to the physical contact between two or more protein molecules. These interactions are often specific and transient and form the basis of almost every biological process. PPIs shape how cells respond to signals, replicate, divide, and even die. They can be stable, forming structural complexes like the ribosome or dynamic, as seen in signalling pathways.

Proteins interact through defined regions called binding interfaces. These regions comprise complementary surfaces, such as hydrophobic patches, hydrogen bonds, and electrostatic attractions. Disruption or modification of these interactions can lead to diseases. Therefore, targeting PPIs has become a key strategy in drug discovery.

Biological Functions Regulated by PPIs

PPIs in Signal Transduction Pathways

PPIs are the cornerstone of intracellular signalling. Ligand binding to cell-surface receptors often triggers a cascade of protein interactions. These include kinases, scaffolding proteins, and second messengers. For instance, the MAPK pathway involves sequential protein phosphorylation through direct interactions. Errors in such signalling cascades frequently contribute to cancer and other chronic diseases.

Protein Complexes in Transcription and Gene Regulation

Transcription factors must often bind co-activators or repressors to modulate gene expression. These complexes are formed via PPIs. For example, the interaction between the TATA-binding protein and other transcription machinery components is essential for RNA polymerase recruitment. Similarly, chromatin remodelling relies on multiprotein complexes that orchestrate DNA accessibility.

PPIs in Apoptosis and Cell Cycle Control

Apoptosis—the programmed cell death—is tightly regulated by pro-apoptotic and anti-apoptotic proteins. The BCL-2 family is a well-known example of a PPI dictating cell fate. Proteins like BAX and BAK require dimerization to permeabilize the mitochondrial membrane. Conversely, BCL-2 and BCL-xL inhibit this process through binding. Misregulation of these interactions often leads to uncontrolled cell proliferation.

Protein Interactions in Host-Pathogen Dynamics

Pathogens exploit host PPIs to establish infection. Viruses, in particular, encode proteins that mimic or disrupt host-protein interactions. For instance, HIV-1 integrates into host DNA through interactions between viral integrase and host cofactors. Understanding these interactions provides valuable insights for antiviral strategies.

PPIs as Therapeutic Targets

PPIs are increasingly recognized as critical nodal points in disease pathways. Small molecules or biologics that disrupt pathogenic PPIs can restore normal cellular function. Below are primary subdomains where PPIs have been targeted therapeutically, with representative examples and corresponding primary references.

Targeting Oncogenic Protein Complexes in Cancer

Cancer cells frequently depend on aberrant PPIs to sustain survival and proliferation. Two well-validated targets in this category are the MDM2-p53 interaction and anti-apoptotic BCL-2 family complexes.

BCL-2 family inhibitors

Anti-apoptotic BCL-2 proteins (BCL-2, BCL-xL BCL-w, MCL-1) bind and sequester pro‐apoptotic effectors (BAX, BAK). BH3 mimetics are small molecules that mimic the BH3 α-helix of pro-apoptotic proteins, binding the hydrophobic groove of anti-apoptotic BCL-2 members and liberating BAX/BAK to induce mitochondrial outer membrane permeabilization.

ABT-737 was the first highly potent BH3 mimetic. Oltersdorf et al. described ABT-737's design via fragment-based NMR screening and structure-guided linkage. ABT-737 binds BCL-2, BCL-xL, and BCL-w with low nanomolar affinity, inducing apoptosis in lymphoma and solid tumor xenografts (Oltersdorf, et al., 2005).

MDM2-p53 inhibitors

Under normal conditions, MDM2 binds p53 and promotes its degradation. In many tumours, MDM2 is overexpressed, leading to the suppression of p53's tumour-suppressor functions. Small molecules that bind the p53-binding pocket of MDM2 prevent this interaction, stabilizing p53 and triggering cell-cycle arrest or apoptosis. One of the first such agents was Nutlin-3a. Vassilev et al. used a structure-guided design to create Nutlin-3a, a potent MDM2 antagonist that activates p53 in cell lines and xenograft models, resulting in tumour growth inhibition in vivo (Lyubomir T et al., 2004). Subsequent optimization yielded more drug-like compounds now in clinical trials.

Figure 1. Structure and mode of binding of MDM2 inhibitors. (Lyubomir T, et al., 2004)

Modulating Neurodegenerative Pathways via PPIs

Neurodegenerative diseases often feature pathogenic protein aggregates. Modulating PPIs that drive aggregate formation or stabilize toxic oligomers can slow disease progression.

Tau-Fyn interaction inhibitors

In Alzheimer's, tau hyperphosphorylation facilitates binding to Fyn kinase, promoting synaptic dysfunction. Bhaskar et al. demonstrated that phosphorylation of tau enhances its interaction with Fyn SH3 domains, exacerbating neurotoxicity (Bhaskar K, et al. 2005). Efforts to disrupt this PPI have focused on small peptides or peptidomimetics that bind the proline-rich region of tau, preventing Fyn recruitment. Although no PPI inhibitor has yet reached clinical trials, several lead compounds show efficacy in cellular and transgenic mouse models by reducing tau-mediated excitotoxicity.

Inhibiting 14-3-3/tau PPIs

14-3-3 proteins interact with phosphorylated tau, influencing tau stability and aggregation. Milroy et al. have used X-ray crystallography of 14-3-3/tau peptide complexes to guide design of small-molecule inhibitors that occupy the tau-binding groove on 14-3-3, thereby blocking the interaction and reducing tau oligomerization in vitro (Milroy L G, et al. 2015).

Figure 2. PPI stabilizer (blue) and inhibitor (green) space of 14-3-3 (orange). (Milroy L G, et al. 2015)

PPIs in Autoimmune and Infectious Diseases

Pathogenic PPIs also play central roles in immune dysregulation and microbial infection. Therapies targeting these interfaces can modulate immune responses or block pathogen entry and replication.

Immune checkpoint blockade (PD-1/PD-L1)

Tumours evade immune surveillance by exploiting the PD-1/PD-L1 axis; PD-L1 on tumour or myeloid cells binds PD-1 on T cells, inhibiting T-cell activation. Topalian et al. conducted a landmark phase I trial of the anti–PD–1 antibody nivolumab. In 296 patients with advanced cancers, nivolumab produced durable objective responses: 18% in non–non-small-cell lung cancer, 28% in melanoma, and 27% in renal‐cell carcinoma, with favourable safety profiles. Significantly, response correlated with pretreatment PD-L1 expression on tumour cells (Topalian S L et al. 2012). This proof of concept has spurred the development of multiple PPI‐blocking antibodies in immuno-oncology.

HIV-1 integrase–LEDGF/p75 inhibitors

HIV-1 integrates its cDNA into the host genome through interactions between viral integrase (IN) and the host cofactor LEDGF/p75. Cherepanov et al. solved the crystal structure of the IN catalytic core domain bound to the LEDGF/p75 integrase-binding domain, revealing the interface used by IN to dock onto LEDGF/p75 (P. Cherepanov, et al. 2005). Small molecules (LEDGINs) that bind the LEDGF/p75‐binding pocket on IN disrupt this PPI, reducing HIV integration efficiency and viral replication in vitro.

Figure 3. Molecular mechanism of the IN-LEDGF interaction. (P. Cherepanov, et al. 2005)

Techniques to Study and Validate Therapeutic PPIs

Computational Approaches for PPI-Targeted Drug Discovery

Structure-Based Virtual Screening of PPI Interfaces

Virtual screening uses structural data to identify molecules that bind PPI interfaces. This approach narrows down candidate compounds before experimental validation. Molecular docking and dynamic simulations improve prediction accuracy.

In Silico Docking for Peptide and Small Molecule Design

Computational docking predicts how peptides or small molecules interact with target proteins. This helps design molecules that mimic or inhibit protein interfaces. It is particularly valuable when structural data are available.

PPI Databases and Predictive Models: STRING, IntAct, BioGRID

Several databases catalog known and predicted PPIs. STRING, IntAct, and BioGRID are widely used for network mapping and target prioritization. They integrate experimental data, computational predictions, and curated literature.

AI and Machine Learning in PPI-Drug Interaction Forecasting

Artificial intelligence has transformed PPI research. Machine learning models can predict druggable interfaces, rank interaction strength, and forecast off-target effects. These technologies accelerate drug discovery by refining target selection.

Future Challenges and Perspectives

• Cell Surface Proteomics

PPI surfaces are often large and flat.

They lack deep pockets for small-molecule binding.

This limits the design of high-affinity ligands.

• Transient and Context-Dependent Interactions

Many PPIs form only under specific conditions.

Their fleeting nature complicates detection and validation.

Capturing these interactions requires precise temporal resolution.

• Off-Target Effects and Delivery Barriers

Peptide-based PPI inhibitors may affect unintended proteins.

Ensuring selectivity remains a critical challenge.

Efficient cellular delivery of large molecules is also difficult.

• Advances in Structural and Computational Tools

Cryo-electron microscopy is revealing complex assemblies.

Deep learning models predict PPI interfaces with higher accuracy.

Cell-permeable macrocycles offer new scaffolds for interface targeting.

• Integration of Multi-Omics Data

Combining proteomics, transcriptomics, and metabolomics refines network maps.

Context-specific PPI networks improve target selection.

Personalized datasets will guide bespoke therapeutic strategies.

References

• Lyubomir T., et al. In Vivo Activation of the p53 Pathway by Small-Molecule Antagonists of MDM2. Science, 2004, 303,844-848. DOI: 10.1126/science.1092472
• Oltersdorf, et al. An inhibitor of Bcl-2 family proteins induces regression of solid tumours. Nature, 2005, 435, 677–681. DOI: 10.1038/nature03579
• Bhaskar K, Yen S H, Lee G. Disease-related modifications in tau affect the interaction between Fyn and Tau. Journal of Biological Chemistry, 2005, 280(42): 35119-35125. DOI: 10.1074/jbc.M505895200
• Milroy L G, et al. Stabilizer‐Guided Inhibition of Protein–Protein Interactions. Angewandte Chemie International Edition, 2015, 54(52): 15720-15724. DOI: 10.1002/anie.201507976
• Topalian S L, et al. Safety, activity, and immune correlates of anti–PD-1 antibody in cancer. New England Journal of Medicine, 2012, 366(26): 2443-2454. DOI: 10.1056/NEJMoa1200690
• P. Cherepanov, et al. Structural basis for the recognition between HIV-1 integrase and transcriptional coactivator p75, Proc. Natl. Acad. Sci. U.S.A. 2005, 102 (48) 17308-17313. DOI: 10.1073/pnas.0506924102