A Systematic Blueprint to Ligand the Proteome.

Area of Science:

• Drug discovery and development
• Proteomics
• Computational chemistry

Background:

• The human proteome presents a vast landscape for drug discovery.
• Identifying druggable targets is crucial for therapeutic advancements.
• WD40 repeat proteins are a significant class with largely untapped therapeutic potential.

Purpose of the Study:

• To outline a strategy for assessing the ligandability of WD40 repeat proteins.
• To leverage DNA-encoded libraries and machine learning for proteome-wide drug discovery.
• To contribute to the goals of the Target 2035 initiative.

Main Methods:

• Utilizing DNA-encoded libraries (DEL) for high-throughput screening.
• Applying machine learning algorithms to analyze screening data.
• Integrating computational approaches to predict protein ligandability.
• Focusing on the WD40 repeat protein superfamily.

Main Results:

• Demonstrated a feasible approach to evaluate the ligandability of protein targets.
• Identified potential strategies for discovering novel pharmacological tools.
• Highlighted the synergy between experimental screening and computational prediction.

Conclusions:

• The combined use of DNA-encoded libraries and machine learning offers a powerful strategy.
• This approach can accelerate the discovery of ligands for challenging protein targets like WD40 repeats.
• It provides a roadmap for expanding drug discovery efforts across the human proteome.