De novo protein design enables targeting of intractable oncogenic interfaces
Varshika Ram Prakash1,2, Yusuf Najy1,2, Kalel Garrett1,2
1Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA.
Abstract:
Protein-protein interactions (PPIs) involving oncogenic drivers remain among the most intractable targets in cancer biology due to their dynamic conformations and limited accessibility to conventional small molecules. Although antibodies and indirect inhibitors have achieved clinical success against targets such as PD-1/PD-L1 and MYC, challenges persist related to tissue penetration, intracellular delivery, resistance, and incomplete blockade of key interface hotspots. Here, we present DesignForge, an integrated de novo protein design framework that combines deep-learning-based structure generation, sequence optimization, and energetic hotspot mapping to create compact miniprotein binders for PPIs. Using this approach, we engineered PD-1 mimetics predicted to disrupt the PD-1/PD-L1 immune checkpoint, designed scaffolds targeting the MYC/MAX dimerization interface, and generated KRAS binders in a manner predicted to occlude RAF interaction. The top designs showed high structural confidence by AlphaFold2, favorable stability metrics, and consistent hotspot engagement identified through MOE-based analyses. Collectively, these results establish DesignForge as a generalizable in silico platform for rational design of therapeutic protein binders that extend beyond antibody and small-molecule modalities to systematically target intractable oncogenic PPIs.
Insights
DesignForge, a new AI platform, creates novel miniproteins to target difficult cancer-driving protein-protein interactions (PPIs). This approach offers a new way to develop cancer therapies beyond traditional antibodies and small molecules.
Area of Science:
- Computational biology
- Protein engineering
- Drug discovery
Background:
- Protein-protein interactions (PPIs) involving oncogenic drivers are challenging cancer targets due to their complex nature and resistance to conventional drugs.
- Existing therapies like antibodies face limitations in tissue penetration, intracellular delivery, and resistance, necessitating novel therapeutic modalities.
Purpose of the Study:
- To develop an integrated computational framework, DesignForge, for *de novo* design of miniprotein binders targeting intractable oncogenic PPIs.
- To demonstrate the platform's capability in designing binders for key cancer targets including PD-1/PD-L1, MYC/MAX, and KRAS/RAF interactions.
Main Methods:
- Utilized deep learning for structure generation and sequence optimization.
- Integrated energetic hotspot mapping to guide miniprotein design.
- Employed AlphaFold2 for structural confidence assessment and MOE-based analyses for hotspot engagement.
Main Results:
- Successfully engineered PD-1 mimetics to disrupt the PD-1/PD-L1 immune checkpoint.
- Designed novel scaffolds targeting the MYC/MAX dimerization interface.
- Generated KRAS binders predicted to inhibit RAF interaction, with high structural confidence and stability.
Conclusions:
- DesignForge is a generalizable *in silico* platform for the rational design of therapeutic protein binders.
- This approach enables the systematic targeting of previously intractable oncogenic PPIs, extending beyond antibody and small-molecule modalities.
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