In Silico Laboratory: New and Updated Tools for Protein-Centered Drug Discovery

Area of Science:

• Computational biology
• Structural biology
• Drug discovery

Background:

• Protein-ligand binding prediction is crucial for identifying novel therapeutics.
• Existing computational tools require updates to handle large-scale databases and AI-modeled structures.

Purpose of the Study:

• To provide an updated overview of the ProBiS computational tools for protein-ligand binding analysis.
• To highlight recent advancements and applications of ProBiS tools in drug discovery and structural biology.

Main Methods:

• Utilizing a maximum clique algorithm for efficient mining of protein structure databases (PDB, AlphaFoldDB).
• Employing ProBiS-Dock for proteome-wide ligand-binding prediction and hybrid flexible docking.
• Applying ProBiS-H2O and ProBiS-H2O MD to identify conserved water clusters and networks.
• Using the ProBiS-Fold web server for binding site prediction on AI-modeled protein structures.

Main Results:

• ProBiS tools enable large-scale database mining for drug repurposing and functional annotation.
• ProBiS-Dock database and algorithm enhance ligand-binding prediction.
• ProBiS-H2O and ProBiS-H2O MD reveal conserved hydration sites and allosteric water networks.
• ProBiS-Fold improves annotation of AI-modeled human proteome structures.
• Applications demonstrated in virtual screening for cancer targets and kinase analysis.

Conclusions:

• The ProBiS suite of tools offers comprehensive capabilities for protein-ligand interaction analysis.
• Integration of these tools accelerates the discovery of new therapeutic agents.
• Protocols are provided for effective utilization in structural biology and drug discovery workflows.