3D CNN Protein Stability Prediction
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Solution Overview
Problem
Current protein engineering methods face challenges in stabilizing proteins for industrial use due to incomplete understanding of protein sequence/structure/function relationships, leading to conflicting computational solutions and inefficient identification of destabilizing residues, which hampers the adaptation of proteins to different environmental conditions.
Innovation Solution
A computer-implemented method using 3D convolutional neural networks to identify candidate residues for mutation by learning consensus microenvironments, predicting amino acid substitutions that improve protein stability by analyzing chemical environments and structural data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational folding simulations are performed to identify destabilizing residues, then the accuracy of residue identification improves, but the computational time increases significantly
Solution Approach 1:
The patent pre-calculates and stores microenvironment features for all 20 amino acid types across multiple protein structures during an offline training phase. This preliminary action creates a reference database that enables rapid prediction during online application, avoiding time-consuming simulations for each target protein while maintaining high identification accuracy through pre-learned consensus patterns
Solution Approach 2:
The patent replaces traditional physics-based folding simulations with a machine learning model that uses 3D convolutional neural networks to analyze microenvironment features. This substitution transforms the problem from computational physics calculations to pattern recognition, dramatically reducing computational time while preserving the ability to identify destabilizing residues through learned structural consensus
2Productivity
If traditional computational methods are used to identify destabilizing residues, then the process becomes time-consuming, but using simplified methods may reduce identification accuracy
Solution Approach 1:
The system performs preliminary training by pre-processing large datasets of protein structures and amino acid microenvironments offline. This creates a trained neural network model that encapsulates consensus patterns, enabling rapid and accurate identification of destabilizing residues in target proteins without repeating the computationally intensive learning process
Solution Approach 2:
The patent creates a computational model that copies and generalizes structural consensus patterns from multiple known protein structures. By learning from diverse examples during training, the model captures essential features of stable protein microenvironments and applies this knowledge to predict destabilizing residues in new proteins, maintaining accuracy while improving speed
Data Source
AI summary
A computer-implemented method of training a neural network to improve a characteristic of a protein comprises collecting a set of amino acid sequences from a database, compiling each amino acid sequence into a three-dimensional crystallographic structure of a folded protein, training a neural network with a subset of the three-dimensional crystallographic structures, identifying, with the neural network, a candidate residue to mutate in a target protein, and identifying, with the neural network, a predicted amino acid residue to substitute for the candidate residue, to produce a mutated protein, wherein the mutated protein demonstrates an improvement in a characteristic over the target protein. A system for improving a characteristic of a protein is also described. Improved blue fluorescent proteins generated using the system are also described.