3D CNN Protein Stability Prediction via Neural Network
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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 protein adaptation 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, and generating mutated proteins with enhanced characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational methods perform detailed folding simulations to identify destabilizing residues, then identification accuracy improves, but computational time increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-training a neural network model on a large database of protein structures and sequences before actual stability prediction. The model learns consensus microenvironments and stabilizing patterns in advance, enabling rapid predictions without performing time-consuming folding simulations during the actual analysis phase.
Solution Approach 2:
The patent replaces the mechanical computational folding simulation system with a neural network-based predictive system. Instead of performing detailed physics-based simulations to identify destabilizing residues, the system uses a trained neural network that has learned from extensive training data, substituting computational mechanics with machine learning inference.
2Stability of the object's composition
If traditional protein engineering methods are used to stabilize proteins, then protein stability improves, but adaptation to radically different environmental conditions is limited
Solution Approach 1:
The patent applies parameter changes by using a neural network model that can predict stability across different environmental parameters (temperature, pH, solvent conditions). The system identifies residues that stabilize proteins under specific parameter conditions, enabling targeted engineering for adaptation to radically different environments rather than just general stability improvement.
Solution Approach 2:
The patent applies universality by creating a multi-functional prediction system that can evaluate protein stability under various environmental conditions simultaneously. The neural network model learns consensus patterns that are universally applicable across different protein families and environmental contexts, enabling the system to address multiple stability challenges with a single approach.
3Adaptability or versatility
If gain-of-function mutations are introduced to expand protein role, then functionality improves, but protein stability decreases
Solution Approach 1:
The patent applies local quality by identifying specific local residues that contribute to stability rather than making global changes to the protein sequence. The neural network predicts which local microenvironments need stabilization, allowing targeted mutations that preserve functionality while locally reinforcing stability where needed.
Solution Approach 2:
The patent applies feedback by using the neural network to predict the stability impact of proposed gain-of-function mutations before implementation. The system provides feedback on which functional mutations are likely to destabilize the protein, allowing iterative optimization where functionality is enhanced while maintaining stability through informed selection of mutations.
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.