Few-Shot Protein Sequence Generation via Encoder-Decoder Model
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Solution Overview
Problem
Current methods for protein sequencing and generation face challenges due to the large search space of possible amino acid sequences and limited supervised training data, particularly in inferring the functional impact of protein mutations and generalizing to new protein families.
Innovation Solution
A sequence-to-sequence learning approach using an encoder-decoder model trained on a multiple sequence alignment (MSA) query matrix, which learns to encode protein family information and generate new sequences, thereby reducing the need for dedicated family models and improving accuracy over existing methods like PSSMs and pHMMs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional protein sequencing methods are used, then protein sequences can be determined, but the complexity increases exponentially due to the large number of amino acids
Solution Approach 1:
The patent introduces an encoder-decoder model as an intermediary between the input protein sequences and the output generated sequences. The encoder transforms input sequences into latent representations, and the decoder generates new sequences from these representations, simplifying the complex mapping relationship between amino acid sequences and reducing exponential complexity
Solution Approach 2:
The patent replaces traditional mechanical sequencing methods with a neural network-based generative model. Instead of using complex physical and chemical methods for protein sequencing and generation, the system uses deep learning models (transformer encoders and decoders) to predict and generate amino acid sequences, significantly reducing operational complexity
2Measurement precision
If dedicated family models are created for each protein family, then accuracy improves, but the device complexity and training requirements increase
Solution Approach 1:
The patent creates a universal encoder-decoder model that can handle multiple protein families simultaneously. The model is trained on diverse protein families and can generalize to new families without requiring dedicated models for each family. This universal approach maintains accuracy across different protein families while reducing the complexity of managing multiple family-specific models
Solution Approach 2:
The patent performs preliminary training of the encoder-decoder model on a diverse set of protein families before deployment. This pre-training allows the model to learn generalizable patterns and constraints across different protein families, enabling it to accurately model new families without requiring extensive family-specific training data or model customization
3Measurement precision
If more training data is collected for each protein family, then model accuracy improves, but the time and resources required increase
Solution Approach 1:
The patent achieves accurate protein sequence modeling without requiring exhaustive training data for each protein family. The encoder-decoder model, pre-trained on diverse families, can generalize effectively from limited family-specific data. This partial action approach (using less training data than traditionally required) maintains model accuracy while significantly reducing training time and computational resources
4Adaptability or versatility
If the model is trained to generalize across protein families, then adaptability improves, but the difficulty of training increases
Solution Approach 1:
The patent segments the training process into two distinct phases: (1) pre-training on diverse protein families to learn general patterns and constraints, and (2) fine-tuning or adaptation to specific target families. This segmentation of the training process makes it easier to manage and measure progress, while achieving strong generalization capabilities across different protein families
Data Source
AI summary
Embodiments described herein provide a new approach to learning generative models of proteins based on sequence-to-sequence learning. Specifically, sequence modeling is formulated as a few-shot learning problem: a single encoder-decoder model receives an input of a protein family which is encoded into a protein representation and the protein representation is then decoded into a distribution over sequences from that family. The model is trained on tens of thousands of multiple sequence alignments representing known protein families and evaluated on unseen families heldout from training.