Neural Network Protein Sequence Generation
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Solution Overview
Problem
Current methods for designing protein sequences that fold into specific structures are limited by low accuracy and the inability to sample a significant portion of the search space, making it difficult to precisely engineer sequences for predetermined shapes, which is crucial for various applications including protein-based therapeutics.
Innovation Solution
A computer-implemented neural network system processes partially filled protein sequences and edge indices to determine enhanced amino acid values, using graph convolution and edge attribute sets to generate complete sequences that fold into desired structures, overcoming the limitations of existing methods by improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional protein design methods are used, then the search space can be explored, but the accuracy of force fields is low and the methods are labor intensive
Solution Approach 1:
The patent replaces traditional mechanical/computational force field methods with a neural network system that processes protein sequences and structures. The neural network learns from training data to predict optimal amino acid sequences for target structures, substituting the need for labor-intensive force field calculations and manual sequence design.
Solution Approach 2:
The patent uses a neural network that has been trained on existing protein sequence-structure data to generate new sequences. The network copies learned patterns from training examples to predict sequences for target structures, avoiding the need to recalculate physics-based interactions from scratch for each design problem.
2Adaptability or versatility
If traditional protein design methods are used, then sequences can be designed, but the ability to sample a significant portion of the search space is limited
Solution Approach 1:
The patent employs a dynamic neural network system that can adapt to different target structures and design constraints. The network processes various input configurations and generates sequences for diverse protein structures, enabling comprehensive search space exploration without requiring complex manual intervention for each case.
3Manufacturing precision
If existing protein design methods are used, then some sequences can be generated, but the precision of folding into predetermined shapes is difficult to achieve
Solution Approach 1:
The patent performs preliminary training of the neural network on extensive protein sequence-structure data before actual design. This pre-learning phase enables the network to quickly generate accurate sequences for new target structures without time-consuming iterative optimization, achieving precise folding predictions efficiently.
Data Source
AI summary
A method and system for generating a protein sequence is implemented using a computer-implemented neural network. An empty or partially filed sequence of node elements, representing amino acid positions of the protein sequence, and an edge index, having edge elements defining physical interaction between amino acid positions, are received. The computer-implemented neural network operates to determine enhanced edge attribute values for edge elements of the edge index and enhanced amino acid values for node elements of the sequence. Amino acid values are generated for elements of the partially filed sequence having missing values.