Protein Structure Tokenizer Using Geometric Local Encoding
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Solution Overview
Problem
Existing large language models lack an effective way to encode and process biological structures, particularly protein structures, limiting their ability to accurately predict and generate meaningful biological information.
Innovation Solution
A biological structure tokenizer that encodes biological structures into tokens, utilizing geometric attention to capture local and global structure, enabling a multi-track biological language model to predict and generate protein sequences, structures, and functions efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If biological structures are directly input into large language models, then the model can process general language data, but it cannot accurately encode and capture biological structure information
Solution Approach 1:
The patent introduces a biological structure tokenizer as an intermediary component between the input biological structure data and the large language model. This tokenizer encodes biological structures (such as protein structures) into a format that the LLM can process, thereby enabling the model to accurately capture biological structure information without directly inputting raw structure data. The tokenizer acts as a mediator that translates biological structure into a representation suitable for the LLM's processing capabilities.
2Measurement precision
If complex biological structures are encoded in detail, then prediction accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments complex biological structures into smaller, manageable units through the biological structure tokenizer. Instead of processing entire protein structures as single complex entities, the tokenizer divides them into local structural units or motifs that can be processed independently. This segmentation reduces computational complexity while preserving the essential structural information needed for accurate predictions.
Solution Approach 2:
The patent applies local quality by focusing the encoding process on locally relevant structural features rather than treating the entire biological structure uniformly. The tokenizer captures local structural characteristics (such as secondary structure elements, solvent accessibility, and local geometry) that are most critical for prediction accuracy, while using attention mechanisms to selectively process different regions based on their importance.
3Loss of information
If the model processes entire protein structures globally, then overall structure is captured, but local structural details are lost
Solution Approach 1:
The patent implements a nested structure where local structural encodings are embedded within the global protein structure representation. The biological structure tokenizer first encodes local structural units (such as amino acid residues and their immediate environments), then these local encodings are nested within the overall protein structure context. This nested approach allows the model to simultaneously capture both local structural details and global structural organization.
Solution Approach 2:
The patent adds a local structural dimension to the traditional global protein structure analysis. By introducing local structural encodings as an additional dimension of representation, the model can process both global and local structural information simultaneously. The attention mechanism operates across multiple dimensions (global structure and local details) to integrate information from different spatial scales.
Data Source
AI summary
For a specific amino acid in a protein, physically neighboring amino acids of the specific amino acid are determined in a local physical protein structure. Representations of the determined physically neighboring amino acids are included in a structure encoder input for the specific amino acid. The structure encoder input is provided to an autoencoder trained using geometric loss to determine a token representing the local physical protein structure for the specific amino acid.