An integrated computational pipeline for designing high-affinity nanobodies with expanded genetic codes

Area of Science:

• Biochemistry
• Computational Biology
• Protein Engineering

Background:

• Standard protein engineering with 20 amino acids limits functional diversity.
• Site-specific incorporation of non-natural amino acids (nnAAs) is valuable but underexplored for nanobody affinity maturation.
• Current experimental methods struggle with the vast combinatorial possibilities of nnAAs.

Purpose of the Study:

• To develop an integrated computational pipeline for predicting binding affinity of nnAA-incorporated nanobodies.
• To select potent nanobody binders using structure-based design, molecular dynamics, and free energy calculations.
• To explore the utility of nnAAs, specifically halogenated tyrosines, for enhancing nanobody affinity.

Main Methods:

• Developed an integrated computational pipeline combining structure-based design, molecular dynamics simulations, and free energy calculations.
• Employed site-specific incorporation of non-natural amino acids (nnAAs), focusing on halogenated tyrosines.
• Validated computational predictions using Surface Plasmon Resonance (SPR) assays.

Main Results:

• Incorporating 3-chloro-l-tyrosine (3MY) improved the binding affinity of the 9G8 nanobody to epidermal growth factor receptor (EGFR) up to 6-fold.
• Computational binding affinity predictions showed high correlation (Pearson's r = 0.87) with experimental SPR results.
• Enhanced affinity was attributed to improved van der Waals interactions and increased nanobody structural stability.

Conclusions:

• The developed computational pipeline effectively screens large libraries and predicts potent nnAA-incorporated nanobody binders.
• Site-specific incorporation of halogenated tyrosines is a viable strategy for enhancing nanobody affinity against disease targets like EGFR.
• This approach facilitates the development of advanced protein therapeutics with improved binding characteristics.