Inverse Folding Protein Sequence Optimization With Explainable PSSM Feedback
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Solution Overview
Problem
Conventional protein engineering techniques face challenges such as computationally intensive sampling, inefficiency, catastrophic forgetting, and lack of interpretability, making it difficult to navigate the vast protein sequence search space and achieve global optima.
Innovation Solution
A method using an inverse folding model with a Position-Specific Scoring Matrix (PSSM) and explainable AI frameworks to bias sequence generation, balancing exploration and exploitation, while maintaining structural accuracy and providing interpretable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional oversampling methods are used to generate protein sequences, then a large number of sequences can be produced, but the computational cost is extremely high and the efficiency is low
Solution Approach 1:
The patent changes the sampling parameters of the inverse folding model by introducing a Position-Specific Scoring Matrix (PSSM) that biases amino acid selection at each position based on learned patterns from high-performing sequences. This parameter change transforms the uniform sampling process into a guided sampling process, significantly improving the quality and diversity of generated sequences while reducing the total number of sequences needed
Solution Approach 2:
The patent implements a feedback mechanism where the PSSM is iteratively updated based on the performance of generated sequences. The model evaluates sequences using a predictor, computes attribution scores for each amino acid position, and uses this feedback to refine the PSSM for subsequent sampling rounds. This closed-loop feedback system continuously improves sequence generation quality without requiring proportional increases in computational resources
2Manufacturing precision
If Reinforcement Learning is used to fine-tune the generative model, then sequences with desired properties can be favored, but the model suffers from catastrophic forgetting and loses structural accuracy
Solution Approach 1:
The patent segments the optimization task into two independent components: (1) the inverse folding model that guarantees structurally valid sequences through its pre-trained structural understanding, and (2) the PSSM that guides sampling toward desired properties. By separating these functions, the patent avoids catastrophic forgetting because the structural knowledge in the inverse folding model remains intact while the PSSM handles property optimization
Solution Approach 2:
The PSSM acts as an intermediary between the inverse folding model and the property optimization goal. Instead of directly fine-tuning the model weights (which causes catastrophic forgetting), the PSSM mediates the sampling process by biasing amino acid selection based on property predictions. This intermediary approach allows property optimization without compromising structural validity
3Reliability
If folding accuracy is incorporated into the reward function, then structural validity can be maintained, but the computational burden increases significantly
Solution Approach 1:
The patent performs preliminary action by pre-training the inverse folding model on structural data before the optimization process. This pre-training establishes strong structural priors in the model, allowing it to generate structurally valid sequences without requiring expensive folding validation during optimization. The structural accuracy is built-in beforehand rather than verified iteratively
4Extent of automation
If conventional black-box optimization methods are used, then sequence generation can be automated, but interpretability is lost and rational design is hindered
Solution Approach 1:
The patent uses explainable AI frameworks to provide feedback on which amino acids contribute most to predicted properties. This feedback mechanism identifies specific positions and amino acids that drive property predictions, enabling rational design decisions while maintaining automation. The interpretability feedback guides subsequent sampling iterations without requiring manual intervention
Data Source
AI summary
A method (400) and system (100) for explainable optimization of protein sequence is disclosed. The method (400) includes initializing Position-Specific Scoring Matrix (PSSM) based on probability distribution of the inverse folding model. The method (400) may include generating plurality of protein sequences by sampling from an inverse folding model. The method (400) may further include predicting target property value for each of protein sequences using predictor models. The method (400) further includes computing delta value for each protein sequence by subtracting average predicted target property value across plurality of protein sequences from predicted value for each protein sequence. Further, the method (400) includes determining attribution scores for each amino acid in protein sequence using explainable AI. The method (400) further includes computing position-wise amino acid frequency distribution from protein sequences. The method (400) further includes updating PSSM by combining scaled attribution scores and scaled position-wise amino acid frequency distribution.