Phosformer Transformer for Kinase-Specific Phosphosite Prediction
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Solution Overview
Problem
The prediction of kinase-specific phosphosites is challenging due to limited experimentally validated data and incomplete knowledge of sequence, structure, and functional features associated with kinase-substrate interactions, hindering the development of effective machine learning models for phosphosite prediction.
Innovation Solution
A Phosformer model, a transformer-based deep learning system, is developed to predict kinase-specific phosphosites by transforming protein sequences into context-aware sequences, using masked language modeling and a question-answering framework, which learns biologically meaningful features unsupervisedly and implicitly captures kinase-substrate interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general phosphosite prediction methods are used, then prediction coverage is improved, but kinase-specific prediction accuracy deteriorates
Solution Approach 1:
The patent segments the phosphosite prediction task into kinase-specific models, where each kinase family or individual kinase has its own dedicated prediction model. This segmentation allows the system to maintain high accuracy for each specific kinase while collectively covering a broad range of kinases through the ensemble of specialized models.
2Reliability
If more experimentally validated data is collected, then model training quality is improved, but data availability deteriorates
Solution Approach 1:
The patent performs preliminary actions by curating and preprocessing experimental data from multiple sources before model training, creating high-quality training datasets in advance. This preliminary data preparation includes filtering, validation, and organization of phosphorylation data to ensure maximum training quality from limited available data.
Solution Approach 2:
The patent develops prediction models that can be universally applied across different kinase families. The models are designed to handle diverse kinase types and substrate specificities, allowing a single framework to serve multiple functions and kinases, thereby maximizing the utility of limited training data across different prediction tasks.
3Device complexity
If incomplete knowledge of sequence, structure and functional features is used, then model complexity is reduced, but prediction accuracy deteriorates
Solution Approach 1:
The patent applies local quality by incorporating specific sequence, structure, and functional features that are locally relevant to each kinase's phosphorylation specificity. Rather than using comprehensive global features, the model focuses on locally important characteristics such as substrate motifs, kinase domain structures, and functional annotations that are critical for accurate prediction of each kinase's preferred substrates.
Data Source
AI summary
Various examples are provided related to phosphosite prediction. In one example, a system includes a computing device and an application for phosphosite prediction stored in memory. When executed, the application can cause the computing device to transform a protein sequence to a context-aware protein sequence by a Phosformer based transformer. The transformation can include predicting phosphorylation associations from the protein sequence based upon a trained Phosformer model and generating the context-aware protein sequence based upon the predicted phosphorylation associations, the context-aware protein sequence including a predicted phosphosite. The predicted phosphosite can be rendered for presentation to a user.