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

Abstract

Protein engineering and design principles employing the 20 standard amino acids have been extensively used to achieve stable protein scaffolds and deliver their specific activities. Although this confers some advantages, it often restricts the sequence, chemical space, and ultimately the functional diversity of proteins. Moreover, although site-specific incorporation of non-natural amino acids (nnAAs) has been proven to be a valuable strategy in protein engineering and therapeutics development, its utility in the affinity-maturation of nanobodies is not fully explored. Besides, current experimental methods do not routinely employ nnAAs due to their enormous library size and infinite combinations. To address this, we have developed an integrated computational pipeline employing structure-based protein design methodologies, molecular dynamics simulations and free energy calculations, for the binding affinity prediction of an nnAA-incorporated nanobody toward its target and selection of potent binders. We show that by incorporating halogenated tyrosines, the affinity of 9G8 nanobody can be improved toward epidermal growth factor receptor (EGFR), a crucial cancer target. Surface plasmon resonance (SPR) assays showed that the binding of several 3-chloro-l-tyrosine (3MY)-incorporated nanobodies were improved up to 6-fold into a picomolar range, and the computationally estimated binding affinities shared a Pearson’s r of 0.87 with SPR results. The improved affinity was found to be due to enhanced van der Waals interactions of key 3MY-proximate nanobody residues with EGFR, and an overall increase in the nanobody’s structural stability. In conclusion, we show that our method can facilitate screening large libraries and predict potent site-specific nnAA-incorporated nanobody binders against crucial disease-targets.

From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics

“…Peptides, small proteins, and nanobodies are prone to enzymatic degradation and immune responses when introduced into the human body. , Various strategies, including structural modifications, stabilizing agent conjugation, or carrier protein fusion, can enhance stability and mitigate immunogenicity. , Rational design and computational simulations can predict immunogenic regions and guide the development of modified molecules. Incorporating non-natural amino acids and scaffold-based engineering offers promising avenues to overcome stability and immunogenicity concerns. , These strategies aim to enhance the translational potential of designed molecules for therapeutic development.…”

“…Incorporating non-natural amino acids and scaffold-based engineering offers promising avenues to overcome stability and immunogenicity concerns. 142,143 These strategies aim to enhance the translational potential of designed molecules for therapeutic development.…”

Nanobody Engineering: Computational Modelling and Design for Biomedical and Therapeutic Applications

“…Molecular dynamics (MD) simulations are valuable for investigating nanobody-antigen interactions and dynamics [107][108][109]. These simulations offer unique insights into identifying changes in the flexibility of CDR loops upon nanobody-antigen binding [102], and elucidating the molecular mechanisms underlying nanobody-antigen interactions, providing perspectives that complement experimental observations.…”

“…Computational affinity maturation of nanobodies refers to the process of using computational techniques to enhance the binding affinity of nanobodies by iteratively designing and optimising nanobody sequences or structures to improve their interactions with target antigens [123]. Computational methods enable the exploration of vast sequence and structural space to identify mutations [136,137], modifications or non-natural amino acid incorporations [108] that enhance nanobody binding affinity while maintaining specificity and stability [107]. A computational protocol based on MD simulations, molecular docking scores, FoldX stability prediction, CamSol and A3D solubility estimations resulted in accurate scoring methodologies for predicting experimental yields and identifying the structural modifications induced by mutations [138].…”