Prediction of Protein Allosteric Signalling Pathways and Functional Residues Through Paths of Optimised Propensity

Area of Science:

• Biochemistry
• Computational Biology
• Pharmacology

Background:

• Allosteric regulation is crucial for protein function and drug discovery.
• Existing computational methods struggle to predict allosteric sites and signaling pathways effectively.

Purpose of the Study:

• To develop a computational method for identifying allosteric sites and signaling pathways.
• To aid in the design of novel allosteric modulators for therapeutic purposes.

Main Methods:

• Utilized bond-to-bond propensity analysis on energy-weighted atomistic protein graphs.
• Applied network analysis to identify allosteric sites from orthosteric sites and ligands.
• Developed a method to compute and score optimized propensity paths linking orthosteric and allosteric sites.

Main Results:

• Successfully identified key residues in orthosteric and allosteric sites for h-Ras, caspase-1, and PDK1, aligning with experimental data.
• Revealed pivotal signaling residues along identified pathways, offering new drug design targets.
• Demonstrated the ability to differentiate allosteric modulator activity using computed path scores.

Conclusions:

• The proposed computational method effectively predicts allosteric sites and signaling pathways.
• This approach provides valuable insights for allosteric drug discovery and modulator design.
• Identified key residues and pathways offer potential for developing targeted therapeutics.