In silico based re-engineering of a computationally designed biosensor with altered signalling mode and improved...

Area of Science:

• Biomolecular Engineering
• Computational Biology
• Protein Design

Background:

• Rational design of biomolecular sensors requires custom control over binding affinities and detection mechanisms.
• Existing computational methods for biosensor design face challenges in fine-tuning these properties.
• Maltooligosaccharide (MOS)-detecting biosensors are valuable tools but require further engineering for specific applications.

Purpose of the Study:

• To re-engineer a computationally designed fluorescent MOS-detecting biosensor to alter its ligand-binding affinity.
• To analyze the underlying sensing mechanism of the re-engineered biosensors in silico.
• To demonstrate the utility of protein structural dynamics for rational biosensor design.

Main Methods:

• Amino acid substitutions were introduced into a protein scaffold (MalX) to create a set of biosensors with varied binding affinities.
• The Computational Identification of Non-disruptive Conjugation sites (CINC) pipeline was employed for in silico analysis.
• CINC utilizes molecular dynamics simulations and a custom algorithm to assess protein structural dynamics at the amino acid level.

Main Results:

• A biosensor set with binding affinities spanning over five orders of magnitude was generated.
• Two distinct output modes, 'ligand-sensing' and 'apo-sensing', were identified based on local conformational changes.
• The study demonstrated that individual amino acid residue dynamics can be engineered to control fluorescence reporting properties.

Conclusions:

• Protein structural dynamics are engineer-able features for rationally altering biosensor fluorescence reporting.
• The CINC workflow provides a platform for in silico design of custom biomolecular tools with specific dynamic properties.
• This work advances the rational design of biomolecular sensors with tunable affinities and detection mechanisms.