Biological Programming Language for Modular Protein Design Constraints
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Solution Overview
Problem
Existing techniques for specifying design requirements and constraints for biological molecules, such as proteins, are complex and unintuitive, lacking scalability and expressiveness for high-level design goals like functionality, structure, stability, and immunogenicity, hindering rapid prototyping and innovation in protein engineering.
Innovation Solution
A biological programming language and compiler framework that allows users to specify high-level design constraints modularly, which are then translated into conditioning inputs for generative biological models using large language models (LLMs), incorporating ethical and security filters to ensure safe and feasible designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing techniques are used to specify design requirements for biological molecules, then the specifications can be provided, but the complexity and lack of intuitiveness make the system difficult to operate and scale
Solution Approach 1:
The patent introduces a biological programming language as an intermediary layer between users and the complex underlying biological modeling systems. This language provides intuitive, high-level syntax for specifying design requirements while automatically translating them into the complex computational formats required by the biological molecule generation models, thereby resolving the contradiction between ease of operation and system complexity.
Solution Approach 2:
The specification system is segmented into modular components including a programming language layer, a compiler/translator layer, and the underlying generative models. This segmentation allows the user interface to remain simple and intuitive while the complexity is isolated in separate, manageable layers that handle the computational transformations and model interactions.
2Adaptability or versatility
If high-level design goals are expressed using existing techniques, then design requirements can be specified, but the expressiveness and modularity are insufficient for complex biological designs
Solution Approach 1:
The biological programming language is designed as a universal framework that can express multiple types of design requirements (structural, functional, stability, immunogenicity) using a consistent and modular syntax. This multi-functional language handles diverse biological design goals through a unified approach, increasing adaptability without proportionally increasing complexity.
Solution Approach 2:
The language framework is divided into modular components that can independently handle different aspects of biological design specification. This modularity allows the system to achieve high expressiveness for complex designs while keeping individual language constructs simple and manageable, resolving the contradiction between versatility and complexity.
3Manufacturing precision
If detailed biological constraints are specified, then design precision is improved, but the difficulty of detecting and measuring these constraints increases
Solution Approach 1:
The programming language and compiler framework serve as intermediaries that automatically translate high-level design constraints into precise computational representations that the generative models can process. This translation layer handles the complexity of constraint detection and measurement, allowing users to specify precise biological requirements without directly dealing with the measurement and validation complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where the compiler validates specified constraints against biological plausibility and model capabilities, providing guidance and corrections. This feedback loop helps maintain precision in protein design while reducing the burden on users to manually detect and measure complex biological constraints.
Data Source
AI summary
A biological programming specification that identifies at least one protein design condition in accordance with a biological programming language is received. A machine learning model is used to convert the biological programming specification to a model input format version for a biological reasoning model. The model input format version is used as a conditioning input for the biological reasoning model to generate a protein design having the at least one protein design condition.