Recent Advances in Protein-Protein Interactions as Druggable Targets

Protein-protein interactions (PPIs) have long been a challenging frontier in drug discovery. These intricate biological connections are central to cellular function, yet their complex, often transient nature has historically made them “undruggable” – difficult to target with small molecules. Recent advancements in structural biology and computational chemistry, however, have redefined this perception and positioned PPIs as increasingly viable drug targets, transforming the landscape of therapeutic discovery [1-4]. Fig. 1 summarizes up-to-date achievements in drugging undruggable PPI-related proteins such as RAS, Bcl-2, p53, Myc through PPI modulations [1].

Fig. 1. PPI inhibitors and protein-DNA interaction inhibitors targeting undruggable proteins.
Adopted from [1].

Implementation of new technologies and resources, including computational tools and advanced screening library design, has eventually allowed for breaking barriers in targeting PPI interactions. For instance, recent studies [4,5] have demonstrated the potential to stabilize or disrupt PPIs using small molecules, peptides, or biologics, enabling the modulation of critical cellular pathways. These innovations have opened doors to targeting diseases where traditional approaches are insufficient, such as cancer, neurodegenerative disorders, and infectious diseases.

One of the most notable advancements has been the integration of artificial intelligence (AI) in PPI research. AI-driven algorithms are revolutionizing the prediction and design of PPI modulators. Machine learning techniques, combined with molecular docking and dynamics simulations (Fig. 2), pave the way to modeling complex PPI structures and identifying binding hot spots with remarkable accuracy [6-8]. In particular , AI has facilitated the design of small molecules that stabilize PPIs, – a novel approach distinct from the traditional strategy of inhibition [9]. Among other breakthroughs, cryo-electron microscopy (cryo-EM) and advanced nuclear magnetic resonance (NMR) spectroscopy provide unprecedented insights into PPI structures [10]. These tools are instrumental in visualizing transient interactions and identifying druggable sites, thus providing means to overcome a major hurdle in the field.

Fig. 2. Computational strategies for PPI drug discovery. Adopted from [7]

Harnessing these cutting-edge approaches, targeting PPIs holds promise across a range of therapeutic areas. In oncology, modulating interactions like MDM2-p53 has shown significant potential in reactivating tumor suppressor pathways [4,5]. Similarly, PPIs are critical in neurodegenerative diseases, where targeting protein aggregation can mitigate disease progression [11,12]. Recent studies have also highlighted the role of PPIs in infectious diseases, where disrupting pathogen-host interactions could pave the way for novel treatments [11,13]. Beyond traditional inhibitors, PPI stabilizers represent an emerging class of therapeutic agents. By enhancing protein complex formation, stabilizers offer expanded therapeutic opportunities, particularly in cases where restoring protein function is essential [9,14].

Against this background, Life Chemicals can offer its exceptionally promising resources and broad support for these advancements by its tailored compound collections, which allow exploring a wide range of PPI modulators. This not only accelerates the discovery process but also enhances the likelihood of identifying clinically viable candidates. Moreover, screening libraries tailored for PPI targets integrate seamlessly with AI-driven workflows, boosting efficiency and precision.

Life Chemicals PPI Screening Libraries are created using both ligand-based and receptor-based approaches to cover diverse interaction types:

• PPI Focused Libraries by Ligand-Based Approach: designed to identify small molecules that mimic key residues in PPI interfaces, these libraries facilitate the discovery of competitive inhibitors. In particular, Life Chemicals PPI Machine Learning Method Library (Fig. 3) encompasses over 6,500 PPI-targeting structurally-diverse screening compounds selected by the decision tree method.

Fig. 3. Principal component analysis (PCA) showing the accumulation of compounds best matching desired set of parameters.

• PPI Targeted Libraries by Receptor-Based Approach: employing docking algorithms, these libraries target structural pockets within protein complexes to stabilize or disrupt PPIs. For instance, drug-like screening compounds from MDM2–p53 library (Fig. 4) are designed to inhibit the MDM2-p53 interaction, thereby reactivating p53 function – a promising strategy in cancer therapy.

Fig. 4. Examples of virtual hit compounds (binding site: 4ZYF) from the MDM2–p53 library.

In addition, the PPI Fragment Library provides 11,100 readily-available fragment-like compounds for PPI-related fragment-based drug discovery.

Also , to diversify and expand your research toolbox, explore our related screening libraries:

• Molecular Degraders for Targeted Protein Degradation
• Peptidomimetic Compound Library

Order your custom compound selections and enjoy the most convenient terms and competitive pricing.

Please, contact us at marketing@lifechemicals.com for any additional information and price quotations.

Download SD files with compound structures directly from our Downloads section

References

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2. Oláh, J., Szénási, T., Lehotzky, A., Norris, V., Ovádi, J. (2022). Challenges in Discovering Drugs That Target the Protein-Protein Interactions of Disordered Proteins. Int J Mol Sci. 23(3):1550. DOI: 10.3390/ijms23031550
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4. Nada, H., Choi, Y., Kim, S. et al. (2024). New insights into protein–protein interaction modulators in drug discovery and therapeutic advance. Sig. Transduct. Target. Ther. 9:341. DOI: 10.1038/s41392-024-02036-3
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7. Rehman, A. U., Khurshid, B., Ali, Y., Rasheed, S., Wadood, A., Ng, H. L., et al. (2023). Computational approaches for the design of modulators targeting protein-protein interactions. Expert Opin. Drug Discov. 18(3):315-333. DOI: 10.1080/17460441.2023.2171396
8. Wodak, S. J., Vajda, S., Lensink, M. F., Kozakov, D., & Bates, P. A. (2023). Critical assessment of methods for predicting the 3D structure of proteins and protein complexes. Annual review of biophysics, 52(1):183-206. DOI: 10.1146/annurev-biophys-102622-084607
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13. Markovic, V., Szczepańska, A., & Berlicki, Ł. (2024). Antiviral Protein–Protein Interaction Inhibitors. J. Med. Chem., 67(5), 3205-3231. DOI: 10.1021/acs.jmedchem.3c01543
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