Graph Transformer Protein Structure Prediction via MSA Embeddings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining protein structure are time-consuming, expensive, and computationally prohibitive, and existing protein structure prediction models rely on classical methods that are inefficient and require extensive computational resources.
Innovation Solution
An attention-based graph architecture that employs MSA Transformer embeddings to directly produce three-dimensional folded structures from protein sequences, leveraging geometric deep learning to map sequences to folded structures and enable end-to-end protein structure prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experimental methods (x-ray crystallography, cryo-EM) are used to determine protein structure, then structure determination accuracy is improved, but time consumption and computational cost increase significantly
Solution Approach 1:
The patent replaces experimental physical methods (x-ray crystallography, cryo-EM) with a computational machine learning system that uses MSA transformer embeddings and graph neural networks to predict protein structures in silico, eliminating the need for time-consuming experimental procedures while maintaining prediction accuracy
Solution Approach 2:
The patent creates a computational model that copies and simulates the outcomes of expensive experimental methods by training on existing structural data, allowing rapid prediction of protein structures without performing actual experimental measurements
2Power
If classical modeling methods are used for protein structure prediction, then computational resources are reduced, but prediction accuracy and efficiency deteriorate
Solution Approach 1:
The patent changes the computational approach by using pre-computed MSA transformer embeddings as input features, transforming the prediction task into a pattern recognition problem that can be solved efficiently with graph neural networks, achieving both accuracy and computational efficiency
Solution Approach 2:
The patent performs preliminary computation of MSA transformer embeddings and distance matrices before the actual structure prediction, pre-processing the data to capture essential structural information, which then enables fast and accurate prediction through the graph neural network
3Productivity
If machine learning approaches are deployed to map sequence-to-structure, then prediction speed is improved, but model complexity and data requirements increase
Solution Approach 1:
The patent segments the protein structure prediction task into distinct components: MSA transformer embedding generation, distance matrix computation, graph construction, and graph neural network prediction, allowing each component to be optimized independently and reducing overall model complexity
Solution Approach 2:
The patent introduces intermediate representations including MSA transformer embeddings and distance matrices as mediators between the protein sequence and the final structure prediction, simplifying the learning task by breaking down the complex sequence-to-structure mapping into manageable stages
Data Source
AI summary
An attention-based graph architecture that exploits MSA Transformer embeddings to directly produce models of three-dimensional folded structures from protein sequences includes a method and system for augmenting the protein sequence to obtain multiple sequence alignments, producing enriched individual and pairwise embeddings from the multiple sequence alignments using an MSA-Transformer, extracting relevant features and structure latent states from the enriched individual and pairwise embeddings for use by a downstream graph transformer, assigning individual and pairwise embeddings to nodes and edges, respectively, using the downstream graph transformer to operate on node representations through an attention-based mechanism that considers pairwise edge attributes to obtain final node encodings, and projecting the final node encodings to form the computer-modeled folded protein structure. An induced distogram of the computer-modeled folded protein structure may be computed.