Protein structure prediction in structure-based ligand design and virtual screening.

Area of Science:

• Computational biology
• Structural bioinformatics
• Drug discovery

Background:

• Advances in protein modeling and structure databases enable genome-to-drug lead strategies.
• Virtual screening against predicted protein targets is increasingly utilized.
• Homology modeling's accuracy depends on sequence similarity but offers significant advantages.

Purpose of the Study:

• To review advancements in protein structure prediction methods, particularly homology modeling.
• To assess the application of homology modeling in pharmaceutical research and drug discovery.
• To highlight successful genome-to-drug lead investigations using homology modeling.

Main Methods:

• Utilizing state-of-the-art sequence similarity comparison and fold recognition algorithms.
• Employing homology modeling to predict protein structures for drug targets.
• Analyzing computational predictions in the context of rational structure-based drug design.

Main Results:

• Homology modeling is a vital approach when experimental structure determination is infeasible.
• The method aids in identifying and validating drug targets.
• Successful applications demonstrate its utility in lead compound identification and optimization.

Conclusions:

• Protein structure prediction via homology modeling is essential for modern drug discovery pipelines.
• Advancements in modeling significantly enhance the genome-to-drug lead process.
• This technique facilitates efficient drug target identification and lead optimization.