OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design

Area of Science:

• Computational biology and structural bioinformatics.
• Drug discovery and development.
• Antimicrobial resistance research.

Background:

• Antibiotic resistance is a growing threat, reducing drug efficacy.
• Predicting future resistance mutations is crucial for designing robust antibiotics.
• Computational structure-based protein design (CSPD) offers a pathway for such predictions.

Purpose of the Study:

• To present an updated protocol for predicting unseen antibiotic resistance mutations using CSPD.
• To demonstrate the application of the OSPREY software suite for prospective resistance prediction.
• To identify active site mutations that confer resistance while preserving enzyme function.

Main Methods:

• Utilized the OSPREY (Open Source Protein REdesign for You) suite of CSPD algorithms.
• Employed a combination of positive and negative design strategies.
• Focused on the dihydrofolate reductase enzyme from methicillin-resistant Staphylococcus aureus (SaDHFR) as a model system.

Main Results:

• Successfully predicted viable resistance mutations in SaDHFR.
• Demonstrated that predicted mutations confer resistance to an experimental inhibitor.
• Showcased the enhanced capabilities of the latest OSPREY version, including improved flexibility modeling and efficient multi-state design.

Conclusions:

• CSPD, particularly with advanced tools like OSPREY, can prospectively identify novel resistance mutations.
• This predictive capability enables the rational design of next-generation antibiotics effective against resistant pathogens.
• The presented protocol offers a valuable approach for pre-clinical drug development to combat antimicrobial resistance.