Multi-Chain Protein Encoding with Linker-Based Chain Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional computational methods for predicting properties of multi-chain proteins are inconsistent and unreliable due to sensitivity in encoding and presenting multi-chain proteins, failing to distinguish between different chains and being limited by the need for extensive experimental data.
Innovation Solution
A method involving generating concatenated amino acid sequences with linkers, encoding them using a protein language model, and processing with a trained machine learning model to accurately predict properties such as aggregation, stability, and viscosity, while augmenting training data with permutations and linkers to enhance model training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computational methods are used to predict multi-chain protein properties, then the prediction process is simple, but the prediction accuracy and reliability are low
Solution Approach 1:
The patent segments multi-chain protein sequences by inserting unique linker tokens between chains, allowing the model to distinguish individual chains within concatenated sequences. This segmentation approach transforms the complex multi-chain prediction problem into manageable chain-level representations while maintaining overall protein property prediction accuracy.
Solution Approach 2:
The patent introduces linker tokens as intermediary elements between different protein chains in the sequence representation. These linkers serve as mediators that enable the machine learning model to identify chain boundaries and relationships, improving prediction reliability without requiring complex separate processing for each chain.
2Reliability
If extensive experimental data is collected for training, then model prediction reliability improves, but time consumption and cost increase
Solution Approach 1:
The patent applies data augmentation techniques as a preliminary action during training, generating synthetic variations of existing protein sequences through permutations and linker insertions. This preliminary data preparation expands the training dataset without requiring additional experimental measurements, thereby improving model reliability while avoiding the time and cost of collecting more experimental data.
3Reliability
If multi-chain proteins are encoded without distinguishing chains, then encoding is simple, but prediction consistency deteriorates
Solution Approach 1:
The patent applies local quality differentiation by inserting unique linker tokens at specific locations between chains in the sequence. This local modification enables the encoding to distinguish between different chains while maintaining the overall simplicity of concatenated sequence representation, thereby achieving prediction consistency without excessive encoding complexity.
Data Source
AI summary
Described herein are techniques for predicting one or more properties of a multi-chain protein, the multi-chain protein including at least a first chain and a second chain. In some embodiments, the techniques include: obtaining sequence data for the multi-chain protein, the sequence data indicating a first amino acid sequence specifying at least a portion of the first chain and a second amino acid sequence specifying at least a portion of the second chain; generating a concatenated amino acid sequence by concatenating the first amino acid sequence, a linker, and the second amino acid sequence; encoding the concatenated amino acid sequence to obtain a numeric representation of the concatenated amino acid sequence; and processing the numeric representation of the concatenated amino acid sequence using a trained machine learning model to obtain an output indicative of the one or more properties of the multi-chain protein.