PocketOptimizer

PocketOptimizer optimizes protein binding pockets to design and rank mutations that improve small-molecule ligand affinity and specificity for biotechnological and biomedical applications.

Key Features:

• Modular design framework: Allows selection and combination of force fields, rotamer libraries, and scoring functions to customize the computational design pipeline.
• Support for diverse force fields and scoring functions: Integrates multiple force fields and scoring functions for accurate modeling and energetic evaluation of protein–ligand interactions.
• Backbone-dependent rotamer library: Incorporates a backbone-dependent rotamer library to improve side-chain conformation predictions within binding pockets.
• Algorithmic improvements: Refined algorithms enhance prediction of beneficial mutations that increase ligand affinity.
• Systematic mutation evaluation: Evaluates potential mutations in binding sites considering intra- and intermolecular interactions and combinatorial search of sequence/conformation space.

Scientific Applications:

• Protein–ligand design: Design and optimization of proteins with specific small-molecule binding properties by predicting pocket mutations.
• Drug discovery: Identification and prioritization of mutations that increase selectivity and affinity for target molecules in drug discovery campaigns.
• Biotechnological and biomedical applications: Engineering proteins with tailored ligand sensitivity and specificity for biotechnological or biomedical use.
• Protein engineering research: Generation of computational starting points for experimental validation and further engineering.

Methodology:

Systematic computational evaluation of potential mutations in protein binding sites that considers intra- and intermolecular interactions, leverages advanced modeling to explore the combinatorial mutation/conformation space, and uses a modular selection of force fields, rotamer libraries, and scoring functions for iterative testing and refinement.