Protein Design Using Multi-Objective Learning and Experimental Feedback
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Solution Overview
Problem
Existing protein design systems face challenges in efficiently exploring vast search spaces, lacking iterative feedback, and computational inefficiencies, leading to suboptimal results and hallucinatory effects, especially when modifying amino acid positions in enzymes or designing viral genomes.
Innovation Solution
A multi-objective reinforcement learning (MORL) model is employed to generate and design protein and genome sequences, incorporating experimental data and feedback through iterative reinforcement learning loops, leveraging large language models (LLMs) to prioritize biological factors and manage computational resources efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing protein design systems explore vast search spaces using traditional methods, then they can generate diverse protein sequences, but they suffer from computational inefficiencies and lack iterative feedback leading to suboptimal results
Solution Approach 1:
The patent implements iterative reinforcement learning loops where the language model generates protein sequences, scoring functions evaluate them, and the results feed back to update the model. This closed-loop feedback mechanism allows the system to learn from previous iterations and improve sequence generation quality over time, resolving the contradiction by making the exploration process adaptive rather than purely computational
Solution Approach 2:
The system dynamically adjusts its search strategy by using reinforcement learning to modify generation parameters based on feedback from scoring functions. The language model evolves its generation approach iteratively, transitioning from static traditional methods to dynamic adaptive exploration that balances search space coverage with computational efficiency
2Ease of manufacture
If traditional protein design systems generate protein sequences without iterative feedback, then they can produce initial designs, but they result in hallucinatory effects and suboptimal results
Solution Approach 1:
The patent employs multiple scoring functions that evaluate generated sequences against biological constraints and properties. The feedback from these scoring functions is used to refine and validate sequences iteratively, ensuring they meet reliability criteria before final output. This feedback loop eliminates hallucinatory effects by continuously verifying sequence validity
Solution Approach 2:
The system performs preliminary validation and scoring of generated sequences before final selection. By evaluating sequences against multiple criteria in advance and iteratively refining them, the system ensures that only high-quality, valid sequences are output, preventing hallucinatory results from propagating to the final design
3Manufacturing precision
If multiple scoring functions are used to evaluate protein sequences, then the quality of selected sequences improves, but the computational complexity and time increase
Solution Approach 1:
The patent implements a hierarchical scoring approach where sequences are evaluated through multiple rounds of scoring with increasing stringency. Not all scoring functions are applied to all sequences equally - instead, the system applies scoring functions in stages, refining the sequence pool iteratively. This partial application of scoring functions reduces overall computational time while maintaining high selection accuracy
Solution Approach 2:
The evaluation process is segmented into multiple iterative stages, with different scoring functions applied at different stages. The language model generates sequences, which are then evaluated by various scoring functions in successive iterations. This segmentation allows the system to balance thorough evaluation with computational efficiency by not applying all scoring functions simultaneously to all sequences
Data Source
AI summary
A method for designing proteins using multi-objective reinforcement learning can include generating, by one or more processors using a machine model, based on an initial protein sequence data structure, a plurality of protein sequences, the machine learning model configured based on reinforcement learning from a plurality of reward metrics including at least one reward metric associated with experimental data regarding example sequence data, scoring, by the one or more processors, using a plurality of scoring functions, the plurality of protein sequences, to select a subset of protein sequences of the plurality of protein sequences, and outputting one or more selected protein sequences of the subset of selected protein sequences.