Difference between revisions of "Part:BBa K5271002"

Latest revision as of 09:08, 29 September 2024

EGFR-binding peptide -scrambled

A scrambled amino acid sequence that acts as a control for the EGFR binding region of the dual targeting nanobody -Panobody.

Profile

• Name: EGFR-binding peptide -scrambled
• Base Pairs: 372 bp
• Amino acid: 124 a.a
• Origin: Synthetic
• Properties: A scrambled control for the dual targeting nanobody -Panobody.

Usage and Biology

The design of this basic part began with an in-house bioinformatics analysis. Based on the dry lab result, we chose EGFR and HER2 as the dual targets for our Biobrick design. We created a dual targeting nanobody -Panobody for EGFR and HER2. To verify our design, we used Alphafold to create a three-dimensional model of Panobody and its scrambled control, Panobody-scrambled. [Jumper et al., 2021] Subsequently, we perform a molecular docking analysis to examine the binding affinity of the scrambled control.

The molecular docking analysis of Panobody- scrambled showed that it forms crucial hydrogen bonds and salt-bridge interactions with key residues (Lys739, Val769, Asp770, Hie773, Lys823, and Thr847) of the EGFR receptor (Fig. 1b); however, the binding appears less reinforced, as hydrogen bonds are not as widespread or integrative as in the Panobody-EGFR complex.

Figure 1. (a) 3D and 2D visualizations of molecular docking between the HER2 binding region of Panobody -scrambled the HER2 receptor. (b) 3D and 2D visualizations of molecular docking between the EGFR binding region of Panobody -scrambled and the EGFR receptor.

Design Note

Our preliminary results when it is joined with the HER2 nanobody by a linker, the linker should avoid cysteine residues since it potentially reduces the solubility of the dual targeting nanobody in prokaryotic expression system.

Source

The sequence of the EGFR-binding peptide -scrambled was randomly generated and had a same length with the EGFR binding region of Panobody.

Reference

• Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., ... & Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. nature, 596(7873), 583-589.

Sequence and Features