Contribution

Overview

Our work mainly uses the synergistic catalysis of CDA and CsnB enzymes to study how to directly synthesize chitooligosaccharides (COS) from chitin and improve the purity and industrial value of COS. Therefore, we used tools such as protein structure prediction and molecular docking simulation to study the protein structure and possible mutation sites of CsnB. We sorted out the principles and steps, as well as the effects of spatial conformational changes caused by mutation sites on enzyme activity and product specificity, hoping to provide methodological references for future iGEM teams. At the same time, we characterized BBa_K4335002 (StayGold), and the corresponding data has been added to the part registry page.

1 Advancing Protein Engineering with AlphaFold3 and AutoDock Vina

In our iGEM project, we integrated advanced computational tools to enhance our protein engineering efforts. By utilizing AlphaFold3 for protein structure prediction and AutoDock Vina for molecular docking simulations, we were able to rationally design mutations that improved enzyme activity and specificity. We believe that sharing our methodologies and insights will provide valuable guidance to future iGEM teams embarking on similar protein engineering projects.

Protein Structure Prediction with AlphaFold3

AlphaFold3 is an advancd AI-based protein folding algorithm developed by DeepMind. It leverages deep learning and a diffusion network to predict high-accuracy protein structures from amino acid sequences. The algorithm begins with a random cloud of atoms and iteratively refines the structure by considering evolutionary relationships and physical constraints, ultimately producing a highly accurate three-dimensional model.

Our Methodology

  • Step 1: Sequence Preparation
    • Target Protein: Chitosanase CsnB.
    • Sequence Acquisition: Retrieved the amino acid sequence from relevant databases.
    • Format: Ensured the sequence was in FASTA format for compatibility.
  • Step 2: Running AlphaFold3
    • Input: Uploaded the CsnB sequence into AlphaFold3.
    • Processing: The algorithm generates a predicted 3D structure, providing per-residue confidence scores (pLDDT values).
    • Output: Obtained a high-confidence structural model of CsnB.
  • Step 3: Structural Analysis
    • Visualization Tool: Used PyMOL to visualize the predicted structure.
    • Assessment: Analyzed structural features, such as active sites and binding pockets.
    • Preparation for Docking: Prepared the structure for molecular docking by removing unnecessary molecules and adding hydrogen atoms.

Molecular Docking Simulations with AutoDock Vina

AutoDock Vina is an open-source software for molecular docking that predicts how small molecules, such as substrates or inhibitors, bind to a receptor of known 3D structure. It uses an empirical scoring function and a sophisticated optimization algorithm to explore possible binding modes.

Our Methodology

  • Step 1: Preparation of Protein and Ligand
    • Protein (CsnB): Removed water molecules and added hydrogen atoms using AutoDockTools. Saved the prepared protein in PDBQT format.
    • Ligand (Chitohexaose): Retrieved the structure from the Protein Data Bank (PDB ID: 4OLT). Prepared the ligand by adding hydrogens and setting torsions. Saved the ligand in PDBQT format.
  • Step 2: Setting Up the Docking Parameters
    • Grid Box Definition: Centered the grid box on the active site of CsnB. Adjusted the grid size to encompass potential binding regions.
    • Parameter Configuration: Used default settings for exhaustiveness and energy range.
  • Step 3: Running the Docking Simulation
    • Execution: Ran AutoDock Vina via command line or graphical interface. Generated multiple docking poses with corresponding binding affinities.
  • Step 4: Analyzing Docking Results
    • Selection Criteria: Evaluated poses based on binding affinity scores and interaction types.
    • Visualization: Used PyMOL to visualize top-ranked docking poses. Identified key interactions such as hydrogen bonds, hydrophobic contacts, and π-π stacking.

Rational Design of Protein Mutants

Based on docking analysis, we identified amino acid residues in CsnB that are critical for substrate binding and catalysis. The goal was to modify these residues to enhance enzyme activity or alter product specificity.

Target Residues and Rationale

  • Asp78 (D78): Mutation: D78Y (Aspartic acid to Tyrosine). Reason: Introduce aromatic interactions to stabilize substrate binding.
  • Val186 (V186): Mutation: V186Y (Valine to Tyrosine). Reason: Enhance π-π interactions with the sugar ring.
  • Lys260 (K260): Mutation: K260Y (Lysine to Tyrosine). Reason: Modify binding affinity and reduce steric hindrance.
  • Pro115 (P115): Mutation: P115A (Proline to Alanine). Reason: Increase flexibility in the binding site region.

Experimental Validation

We then purified the four expressed mutant proteins and the wild-type protein to study enzyme activity and analyze product purity.

Protein Expression and Purification

  • Expression System: E. coli BL21(DE3).
  • Induction: Used IPTG to induce protein expression.
  • Purification: Employed Ni-NTA affinity chromatography.
  • Verification: Confirmed protein size and purity via SDS-PAGE analysis.

Enzyme Activity Assays

Method: Used the DNS method to measure reducing sugars produced by enzymatic hydrolysis of chitosan.

  • Wild-type CsnB: Activity of 28.8 U/mL.
  • P115A Mutant: Increased activity by 15.2% compared to wild type.
  • D78Y and K260Y Mutants: Showed reduced activity but altered product profiles.

Product Analysis

Technique: Thin-Layer Chromatography (TLC).

  • D78Y and K260Y Mutants: Predominantly produced chitobiose, indicating a shift in product specificity.
  • P115A Mutant: Enhanced overall enzymatic activity without altering product distribution significantly.

Mechanism Analysis

Combining the experimental results with the mutation sites, analyzing the relationship between protein structure, enzyme activity, and product purity can provide data references for future iGEM teams interested in CsnB protein research.

  • Steric Hindrance and Enzyme Activity
    • Increased Steric Hindrance may reduce enzyme activity because it hinders the entry of substrates and the release of products.
    • Reduced Steric Hindrance may increase enzyme activity by promoting substrate binding and product release.
  • Dual Effects of π-π Interactions
    • Positive Effect: Enhances the positioning of substrates in the active site, improving catalytic efficiency.
    • Negative Effect: Overly stable binding may hinder product release and reduce enzyme turnover.
  • Importance of Active Site Flexibility
    • Increased Flexibility favors adaptive substrate binding and improves enzyme activity.
    • Reduced Flexibility may cause the active site to be too rigid, reducing the enzyme's catalytic efficiency.

Resources and Tools

  • AlphaFold Protein Structure Database: Access predicted protein structures at AlphaFold DB.
  • ColabFold: Utilize cloud-based resources for protein folding predictions at ColabFold.
  • AutoDock Vina: Download software and access tutorials at AutoDock Vina.
  • PyMOL: Molecular visualization tool available at PyMOL.
  • DNS Method Protocols: Find protocols for reducing sugar assays in biochemical method textbooks or online resources.

2 Characterization of BBa_K4335002 (StayGold)

StayGold, a newly discovered green fluorescent protein (GFP), exhibits significantly higher photostability than other GFPs, making it an ideal candidate for cellular labeling. To explore whether StayGold could be efficiently expressed in Escherichia coli BL21(DE3) using arabinose induction, the pET-PBAD-StayGold plasmid was constructed. The fluorescence intensity was measured at different optical densities (OD600 values of 0.8, 1.0, and 1.2), with results showing that the StayGold gene was successfully expressed with high fluorescence intensity compared to a control vector. Optimal expression was achieved at 30°C, demonstrating the best balance between growth and fluorescence.

These findings indicate that StayGold is a highly effective GFP with strong expression in E. coli under arabinose induction, providing a cost-effective and safer alternative for future fluorescence-based research.

All of the findings and characterization data have been updated in the part registry (BBa_K4335002).