AlphaFold opens the doors to deorphanizing secreted proteins

Area of Science:

• Computational biology
• Molecular signaling
• Drug discovery

Background:

• Cell-cell communication relies on secreted peptides binding to transmembrane receptors.
• Identifying these receptor-ligand interactions is crucial for understanding biological processes and developing therapeutics.
• Many such interactions remain uncharacterized, termed 'orphan' receptors.

Purpose of the Study:

• To develop a computational pipeline for identifying single-pass transmembrane receptors specific to secreted peptides.
• To enable the deorphanization of diverse and previously unknown ligand-receptor pairs.
• To provide a strategy for screening weak and transient molecular interactions.

Main Methods:

• Utilized AlphaFold2, a deep learning model for protein structure prediction.
• Developed an in silico screening pipeline integrating structural and sequence-based analyses.
• Focused on identifying transmembrane domains and potential peptide binding sites.

Main Results:

• Successfully developed and validated an in silico screening pipeline.
• Demonstrated the pipeline's capability to identify single-pass transmembrane receptors for secreted peptides.
• Showcased the potential for deorphanizing a wide array of ligands.

Conclusions:

• The AlphaFold2-based in silico pipeline is effective for identifying single-pass transmembrane receptors.
• This method offers a valuable tool for deorphanizing ligands and understanding cell signaling.
• The strategy is applicable to screening for weak and transient molecular interactions in drug discovery.