Protein Structure Prediction Neural Network Training
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Solution Overview
Problem
Current methods for training neural networks to predict protein structures are limited by the need for expensive and time-consuming ground truth data, and the networks often overfit to irrelevant variations in multiple sequence alignments, reducing their ability to predict a wide variety of protein structures effectively.
Innovation Solution
The training system employs both paired and unpaired training examples, using a self-supervised approach to generate prediction targets and reduce overfitting, and a teacher-student framework to increase the amount of training data available, allowing the neural network to predict protein structures without requiring ground truth structures for all proteins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional supervised training methods are used with ground truth data, then prediction accuracy can be achieved for proteins with known structures, but the method is limited by the scarcity of ground truth data and cannot predict structures of proteins without experimental data
Solution Approach 1:
The system employs self-supervised learning where the neural network generates its own training targets by predicting structures of proteins with known structures. The network then uses these predicted structures as targets for proteins without experimental data, enabling the system to serve itself and expand its predictive capability without requiring external ground truth data for every protein
Solution Approach 2:
The system creates synthetic training examples by copying the structural patterns from proteins with known experimental structures. These copied structural predictions serve as surrogate ground truth data for proteins without experimental structures, allowing the network to learn and generalize structural prediction capabilities across diverse protein families
2Adaptability or versatility
If full multiple sequence alignments are used for training, then comprehensive evolutionary information is captured, but the networks overfit to irrelevant variations and fail to generalize to diverse protein structures
Solution Approach 1:
The system extracts only the essential structural prediction task from the complex multiple sequence alignment data. By focusing specifically on structure prediction rather than attempting to model all evolutionary variations, the network learns robust structural patterns without overfitting to irrelevant sequence variations
Solution Approach 2:
The training approach changes the fundamental parameter being optimized - instead of trying to capture all evolutionary information accurately, the system optimizes for structural prediction accuracy using a simplified objective function that focuses on geometric and physical constraints of protein folding
3Productivity
If expensive ground truth experimental data is collected for training, then accurate prediction targets are available, but the training process becomes time-consuming and resource-intensive
Solution Approach 1:
The system performs preliminary structure predictions on a subset of proteins with known structures to generate synthetic training targets before actual training begins. This preliminary action creates a ready-to-use training dataset that eliminates the need for time-consuming experimental data collection for every training example
Solution Approach 2:
Instead of collecting expensive experimental data for every training example, the system copies and adapts structural information from proteins with known structures to create synthetic training targets, dramatically reducing the time and resources required for data preparation while maintaining sufficient training quality
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training neural networks to predict the structure of a protein. In one aspect, a method comprises: obtaining, for each of a plurality of proteins, a full multiple sequence alignment for the protein; generating, for each of the plurality of proteins, target structure parameters characterizing a structure of the protein from the full multiple sequence alignment for the protein, comprising processing a representation of the full multiple sequence alignment for the protein using the structure prediction neural network to generate output structure parameters characterizing a structure of the protein, and determining the target structure parameters for the protein based on the output structure parameters for the protein; determining, for each of the plurality of proteins, a reduced multiple sequence alignment for the protein, comprising removing or masking data from the full multiple sequence alignment for the protein.