Engineering

Design-Build-Test-Learn: Our Engineering Approach

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Engineering Cycles

Our project follows the Design-Build-Test-Learn (DBTL) engineering cycle, an iterative approach that allows us to continuously improve our system through multiple rounds of refinement. This methodology provides a structured framework for tackling complex problems in synthetic biology.

Data Crawling and Preprocessing

  • Write crawler scripts and obtain the original data from LAMP, APD3, DRAMP and DBAASP respectively through API interfaces and HTML parsing methods.
  • Write data cleaning and format conversion scripts to ensure that the data is converted to a unified JSON format.
  • Focus on developing the conversion module to convert fields such as "Hemolytic Activity" and "Target Organism" from complex nesting to flat structures or readable strings.
  • Generate the SPADE ID, correlate the original data, and ensure a unique identifier.

Platform Development

  • The back-end server is set up to achieve the functions of data storage, management and retrieval.
  • Front-end interface development, designing intuitive filter condition controls and result display.
  • Jointly complete the SPADE system integration test with the front-end and back-end to ensure the correct response of the interface and the correct display of data.

Function and Performance Testing

Data Quality Test

  • Integrity check: Confirm that no data is missing and the format of key fields is correct.
  • Consistency verification: Check the uniformity of fields after data fusion from each database to avoid repetition or conflict.
  • Structural rationality: Whether the key fields "hemolytic activity" and "target organism" are concise and easy to parse.

Functional Test

  • Test the front-end filtering function to ensure that the multi-dimensional filtering conditions take effect normally.
  • Back-end data query speed test to ensure that the response time meets expectations.
  • SPADE ID uniqueness test to avoid repetition or errors.

Performance Test

  • Batch data import and export performance.
  • Large-scale query load testing to ensure system stability.

Dry Lab Engineering

Our dry lab team focused on optimizing antimicrobial peptide production and testing systems. Through multiple engineering cycles, we developed efficient expression vectors, purification protocols, and activity assays.

Click on any node in the engineering cycle diagram to explore our iterative process in detail.

Design Peptide Design & Optimization

In the design phase, we utilized computational tools to identify and optimize promising antimicrobial peptide sequences. Key considerations included:

  • Analysis of existing antimicrobial peptide databases to identify effective motifs
  • Optimization of sequence parameters including charge, hydrophobicity, and amphipathicity
  • Codon optimization for expression in our bacterial system
  • Design of expression vectors with appropriate regulatory elements

Our design process was informed by literature research, protein modeling, and consultations with experts in the field of antimicrobial peptides.

Design Highlights
Sequence Optimization

Improved peptide efficacy through strategic amino acid substitutions

Expression System

Engineered vector with inducible promoter and fusion tags

Build Construct Assembly & Transformation

During the build phase, we implemented our designs through molecular cloning and genetic engineering techniques:

  • Synthesis of codon-optimized gene fragments
  • PCR amplification and verification
  • Restriction enzyme digestion and ligation
  • Transformation into expression host
  • Colony PCR and sequence verification

We constructed multiple variants of our expression system to test different design hypotheses and optimization strategies.

Build Methodology
Golden Gate Assembly

Efficient one-pot cloning of multiple fragments

Expression Strains

Optimized host selection for high-yield production

Test Peptide Expression & Activity Assays

The testing phase involved rigorous characterization of our engineered systems:

  • Peptide expression optimization using different induction conditions
  • Protein purification and quantification
  • Antimicrobial activity assays against pathogenic bacteria
  • Stability testing in various environments
  • Toxicity assessment against mammalian cells

Each construct was systematically evaluated to quantify performance and identify opportunities for improvement.

Key Performance Metrics

Antimicrobial activity against various bacterial strains

Learn Data Analysis & Next Steps

In the learning phase, we analyzed our results to extract key insights:

  • Correlation between peptide sequence features and antimicrobial activity
  • Identification of expression bottlenecks and purification challenges
  • Evaluation of structure-function relationships
  • Integration of feedback from collaborators and experts

These learnings informed subsequent iterations of our engineering cycle, enabling us to refine our designs and experimental approaches.

Key Learnings
Expression Optimization

Identified optimal induction timing and temperature

Activity Determinants

Discovered critical amino acid positions affecting function

Engineering Iterations

D
Design

We selected three promising antimicrobial peptide sequences from literature and designed expression vectors with a 6xHis tag for purification.

B
Build

Synthesized gene fragments were cloned into pET28a vectors and transformed into BL21(DE3) cells for expression.

T
Test

Expression was induced with IPTG and peptides were purified using Ni-NTA chromatography. Antimicrobial activity was tested against E. coli.

L
Learn

We observed low expression levels and peptide degradation. The direct fusion approach resulted in poor yield and activity.

D
Design

Redesigned constructs with SUMO fusion partner to improve solubility and prevent degradation. Optimized codon usage for E. coli.

B
Build

Cloned peptide sequences into pET-SUMO vector system. Incorporated TEV protease cleavage site for tag removal.

T
Test

Tested expression under various conditions. Optimized purification protocol for SUMO-fusion proteins and TEV cleavage efficiency.

L
Learn

SUMO fusion significantly improved expression yield and solubility. TEV cleavage was efficient but required optimization of buffer conditions.

D
Design

Based on our findings, we designed a library of peptide variants with systematic amino acid substitutions to enhance antimicrobial activity.

B
Build

Generated peptide variant library using site-directed mutagenesis and PCR assembly. Validated constructs by sequencing.

T
Test

Expressed and purified peptide variants. Conducted comprehensive antimicrobial activity screening against diverse pathogenic bacteria.

L
Learn

Identified key sequence determinants of antimicrobial activity. Discovered variants with enhanced potency against gram-negative bacteria.

Human Practices Engineering

Our human practices initiatives involved stakeholder engagement, educational outreach, and ethical considerations to ensure our project addresses real-world needs and concerns.

Click on any node in the engineering cycle diagram to explore our human practices work in detail.

Engagement Iterations