Protein Representation via Knowledge Graph and Active Learning
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Solution Overview
Problem
Conventional protein property prediction methods rely heavily on expensive and time-consuming experimental verification and require large amounts of annotated data, limiting their efficiency and accuracy, while existing embedding representations fail to utilize expert knowledge effectively, leading to underfitting and local optima issues.
Innovation Solution
A protein transformation method based on an amino acid knowledge graph and active learning, which enhances protein data by integrating biochemical attributes, uses a combination of graph neural networks and pre-trained models for representation learning, and employs active learning to select representative samples for manual annotation and model training, thereby improving prediction accuracy with reduced annotation costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional supervised learning is used to learn protein representations, then model training can be performed with sufficient data, but it requires a large number of annotated data which are time-consuming and resource-consuming to obtain
Solution Approach 1:
The patent applies preliminary action by pre-training protein representation models using unlabeled data and knowledge graph embeddings before actual prediction tasks. The model is pre-trained on abundant unlabeled protein sequences to learn general representations, then fine-tuned with minimal annotated data. This preliminary learning phase reduces the need for extensive annotation while maintaining prediction accuracy.
Solution Approach 2:
The patent introduces knowledge graph embeddings as an intermediary that bridges unlabeled data and labeled data. The knowledge graph encodes biochemical relationships and properties, serving as a mediator that provides structured prior knowledge to guide the learning process. This intermediary enables the model to leverage unlabeled data more effectively while requiring fewer annotated samples for accurate prediction.
2Loss of information
If pre-trained models like MSA-transformer or ESM are used directly, then global protein sequence knowledge can be captured, but the representation effect of specific protein samples is poor and expert rule knowledge is not utilized
Solution Approach 1:
The patent merges multiple representation sources: pre-trained model embeddings (MSA-transformer or ESM), knowledge graph embeddings containing biochemical rules, and sample-specific features. These different embedding types are concatenated and processed together through neural network layers to produce unified protein representations that capture both global semantic knowledge and specific sample characteristics along with expert rule knowledge.
Solution Approach 2:
The patent creates composite protein representations by combining heterogeneous information sources. The representation vector is composed of multiple components: pre-trained model outputs, knowledge graph embeddings, and amino acid composition features. This composite approach integrates diverse knowledge types (sequence semantics, biochemical rules, structural properties) to achieve superior representation quality for specific samples while maintaining broad knowledge coverage.
3Reliability
If human expert knowledge encoding methods like Geogiev encoding are used, then domain knowledge can be incorporated, but the representations fall into local optima and representation capabilities are limited
Solution Approach 1:
The patent applies dynamics by replacing static expert-encoded representations with dynamic, learnable embeddings. Instead of fixed encodings like Geogiev encoding, the model uses neural network layers that adaptively learn optimal representations from data. The embeddings are updated during training to capture both domain knowledge and patterns in the data, avoiding local optima while maintaining reliability through knowledge graph constraints.
Solution Approach 2:
The patent changes parameters by transforming fixed expert knowledge encodings into learnable parameters. The knowledge graph embeddings are initialized with domain knowledge but then refined through gradient-based optimization during training. This allows the representations to evolve from static expert opinions to adaptive, data-driven parameters that maintain domain accuracy while gaining versatility for different prediction tasks.
4Productivity
If active learning is applied to select representative samples, then training efficiency can be improved and annotation costs reduced, but additional computational overhead is introduced for sample selection
Solution Approach 1:
The patent replaces manual sample selection mechanics with automated computational methods. Instead of experts manually identifying representative samples, the system uses uncertainty sampling and diversity-based selection algorithms that automatically identify informative samples. This substitution reduces annotation costs and improves training efficiency while the computational overhead is managed through efficient implementation of selection criteria.
Data Source
AI summary
The present invention discloses a protein transformation method based on an amino acid knowledge graph and active learning, including: building an amino acid knowledge graph based on biochemical attributes of amino acids; enhancing protein data in combination with the amino acid knowledge graph to obtain enhanced protein data, and performing representation learning to obtain first enhanced protein representations; performing representation learning on the protein data or the protein data and the amino acid knowledge graph by using a pre-trained protein model to obtain second enhanced protein representations; synthesizing the first enhanced protein representations and the second enhanced protein representations to obtain enhanced protein representations; taking the enhanced protein representations as samples, and through active learning, screening out representative samples from the samples, manually annotating protein properties, and training a protein property prediction model by using the manually annotated representative samples; and performing protein transformation by using the protein property prediction model. Therefore, rapid and accurate protein transformation can be implemented.