scDock: streamlining drug discovery targeting cell-cell communication via scRNA-seq analysis and molecular docking

Area of Science:

• Computational Biology
• Bioinformatics
• Drug Discovery

Background:

• Targeting intercellular communication networks is a promising therapeutic strategy.
• Linking single-cell RNA sequencing (scRNA-seq) analysis to structure-based drug screening is complex and requires significant bioinformatics expertise.

Purpose of the Study:

• To present scDock, an integrated and user-friendly pipeline connecting scRNA-seq data processing, cell-cell communication inference, and molecular docking-based drug discovery.
• To provide an accessible end-to-end solution for communication-targeted drug discovery from single-cell data, addressing a critical gap in the field.

Main Methods:

• scDock integrates scRNA-seq data processing, cell-cell communication inference, and molecular docking using a single configuration file.
• The pipeline automates the identification of disease-relevant ligand-receptor interactions from scRNA-seq data.
• Structure-based virtual screening is performed against communication targets using Protein Data Bank (PDB) or AlphaFold-predicted protein structures.

Main Results:

• scDock enables users to execute a complete workflow from raw scRNA-seq data to ranked drug candidates without programming skills.
• The pipeline generates comprehensive outputs, allowing exploration of intercellular signaling alterations.
• Therapeutic compounds targeting specific cell-cell communications can be discovered.

Conclusions:

• scDock offers a seamless and accessible solution for communication-targeted drug discovery using single-cell data.
• The pipeline empowers researchers by simplifying complex bioinformatics workflows.
• This approach facilitates the development of novel therapeutics by targeting intercellular communication networks.