DE-STRESS

DE-STRESS evaluates structural models of designed and engineered proteins to identify potential failures in de novo protein design such as low expression, misfolding, aggregation, or lack of function.

Key Features:

• Comprehensive Evaluation Metrics: Generates high-quality metrics for structural models of designed and engineered proteins to support reproducible, data-driven selection for experimental follow-up.
• Contextualization Tools: Provides tools to formally describe the properties required for a design to be considered fit for purpose.
• Early-stage Failure Identification: Evaluates designs prior to experimental characterization to detect risks including low expression, misfolding, aggregation, and lack of function.
• Benchmarking and Criteria Assessment: Assesses designs against established benchmarks and criteria using comprehensive datasets.

Scientific Applications:

• Protein Engineering and Design Prioritization: Enables selection of promising de novo protein designs to reduce experimental cost and attrition.
• Structural Stability and Folding Analysis: Provides insights into structural stability and folding pathways of designed proteins.
• Functional Potential Assessment: Informs evaluation of the functional potential of engineered proteins.

Methodology:

The methodology involves generation and analysis of structural models for engineered proteins, leveraging computational techniques and comprehensive datasets to assess aspects of protein design and evaluate designs against established benchmarks and criteria.