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Computational redesign of a fluorogen activating protein with Rosetta
Nina G Bozhanova1, Joel M Harp2, Brian J Bender3
1Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America.
Plos Computational Biology
|November 8, 2021
Summary
Computational redesign of a fluorogen-activating protein (FAP) using Rosetta improved its photophysical properties. The new FAP variant, DiB-RM, enhances super-resolution imaging and split-system performance, showcasing in silico protein engineering potential.
Area of Science:
- Biochemistry
- Biophysics
- Computational Biology
Background:
- Genetically encoded fluorescent imaging tools are crucial for biological research.
- Fluorogen-activating proteins (FAPs) offer a versatile platform for such imaging.
- Current FAP development relies on laborious in vitro screening and selection processes.
Purpose of the Study:
- To computationally fine-tune the fluorogen-binding properties of an existing FAP (DiB1) using in silico methods.
- To improve the photophysical parameters and overall performance of FAPs for enhanced biological imaging.
- To explore the potential of computational modeling for accelerating FAP design and optimization.
Main Methods:
- Utilized Rosetta software for computational ligand docking of the BODIPY-like dye M739 to the DiB1 FAP.
- Performed protein binding pocket redesign to enhance fluorogen-binding affinity and specificity.
- Engineered a split system derived from the improved FAP variant.
- Evaluated the performance of the designed FAP (DiB-RM) in protein-PAINT super-resolution imaging.
Main Results:
- Despite initial inaccuracies in ligand docking, computational redesign yielded mutations that improved FAP DiB1's photophysical properties.
- The engineered DiB-RM variant demonstrated increased brightness, localization precision, and photostability compared to the parental DiB1.
- A novel split system derived from DiB-RM exhibited enhanced performance over its parental counterpart.
- The study provides insights into the challenges and outcomes of in silico FAP optimization.
Conclusions:
- Computational protein redesign is a viable strategy for optimizing FAPs, accelerating the development of advanced fluorescent imaging tools.
- The DiB-RM variant represents a significant improvement over DiB1 for super-resolution microscopy and split-system applications.
- In silico approaches can effectively enhance protein function, complementing traditional experimental methods in protein engineering.
