Sequence Activity Models for Protein Library Optimization
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Solution Overview
Problem
Protein design is challenging due to the vastness of sequence space, making it difficult to identify functional proteins with desired properties efficiently, as existing methods are hindered by the NP-hard nature of the protein design problem and the complexity of exploring all possible protein variants.
Innovation Solution
The development of methods and systems for identifying amino acid residues to vary in protein sequences using sequence activity models, which predict activity based on amino acid residue types and positions, allowing for the systematic variation and optimization of protein libraries to impact desired activities such as stability or catalytic activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive exploration of sequence space is performed to identify functional proteins, then functional proteins with desired properties can be identified, but the computational time and resources required become prohibitively large due to the NP-hard nature of the problem
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict protein function and properties before performing exhaustive computational exploration. The system pre-trains models on known protein structures and functions, then uses these models to guide the search through sequence space, identifying promising candidates before full analysis. This preliminary computational preparation significantly reduces the time required for subsequent exhaustive searches while maintaining high reliability in identifying functional proteins.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between the sequence space exploration and functional protein identification. These models act as mediators that predict protein properties and guide the search process, enabling efficient navigation through the vast sequence space without requiring exhaustive computation of all possible sequences. The intermediary models filter and prioritize candidate sequences, reducing computational time while maintaining identification accuracy.
2Reliability
If directed evolution with high throughput screening is used to design proteins, then functional proteins can be identified, but the process becomes complex and time-consuming due to iterative recombination formats
Solution Approach 1:
The patent replaces the mechanical iterative recombination and screening process with computational machine learning models that can predict protein functions directly from sequences. Instead of physically performing iterative evolution experiments, the system uses AI models to simulate and predict outcomes, substituting the complex mechanical evolution process with computational prediction. This significantly reduces process complexity while maintaining reliable protein function identification.
Solution Approach 2:
The patent changes the fundamental parameters of the protein design process from physical iterative recombination to computational prediction. Rather than performing physical evolution cycles with screening, the system uses machine learning models to predict and optimize protein properties directly. This parameter change from physical to computational domain dramatically reduces complexity while maintaining functional identification reliability.
3Reliability
If systematic variation of amino acid residues is performed to optimize protein activity, then desired protein activities can be achieved, but the search space becomes too large to explore exhaustively
Solution Approach 1:
The patent applies partial action by using machine learning models to predict protein activities and identify only the most promising protein variants for further investigation. Instead of exhaustively generating and analyzing all possible protein variants, the system performs partial exploration guided by predictive models that filter out unlikely candidates. This approach achieves reliable protein activity optimization while dramatically reducing the number of variants that need to be systematically varied and tested.
Data Source
AI summary
The present invention generally relates to methods of rapidly and efficiently searching biologically-related data space. More specifically, the invention includes methods of identifying bio-molecules with desired properties, or which are most suitable for acquiring such properties, from complex bio-molecule libraries or sets of such libraries. The invention also provides methods of modeling sequence-activity relationships. As many of the methods are computer-implemented, the invention additionally provides digital systems and software for performing these methods.