Dilated Convolutional Networks for Fast Protein Function Prediction
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Solution Overview
Problem
Conventional techniques for predicting biological functions of proteins often require comparing amino acid sequences to large databases, consuming significant computational resources and time, and struggle with accuracy when few training examples are available.
Innovation Solution
A neural network architecture using dilated convolutional layers processes amino acid sequences directly to generate embeddings, which are pooled into a fixed-dimensionality representation, enabling efficient and accurate prediction of protein functions without alignment to other sequences, and facilitates clustering for novel protein categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques compare amino acid sequences to large databases, then comprehensive function prediction is achieved, but computational resources and time consumption increase significantly
Solution Approach 1:
The system pre-trains the neural network model on large databases of amino acid sequences and their functions, storing learned patterns in the model parameters. During actual prediction, the pre-trained model processes new sequences directly without requiring real-time database comparisons, thus achieving both high accuracy and fast prediction speed.
2Measurement precision
If conventional techniques use large database comparisons, then accurate function prediction is achieved, but computational complexity increases
Solution Approach 1:
The patent replaces the mechanical database comparison process with a neural network model that has learned sequence-function relationships during training. The model uses learned features and patterns stored in its parameters to predict protein functions directly from amino acid sequences, eliminating the need for complex real-time database searches and alignments.
3Reliability
If conventional techniques require alignment data, then accurate function prediction is achieved, but the method becomes inapplicable to sequences without alignment data
Solution Approach 1:
The neural network model is designed to process raw amino acid sequences directly and generate function predictions without requiring alignment data. The model learns universal sequence features during training that enable it to reliably predict functions of both known and novel protein sequences, making the system universally applicable across diverse sequence types.
4Adaptability or versatility
If the system processes sequences of varying lengths, then versatility is improved, but maintaining fixed-dimensionality embeddings becomes challenging
Solution Approach 1:
The system uses pooling operations that aggregate sequence features into fixed-dimensionality embeddings regardless of the input sequence length. By changing the parameter representation from variable-length sequences to fixed-length pooled embeddings, the model can efficiently process sequences of any length while maintaining consistent input dimensions for the prediction layers.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting biological functions of proteins. In one aspect, a method comprises: obtaining data defining a sequence of amino acids in a protein; processing the data defining the sequence of amino acids in the protein using a neural network, wherein: the neural network is a convolutional neural network comprising one or more dilated convolutional layers; and the neural network is configured to process the data defining the sequence of amino acids in the protein in accordance with trained parameter values of the neural network to generate a neural network output characterizing at least one predicted biological function of the sequence of amino acids in the protein; and identifying the predicted biological function of the sequence of amino acids in the protein using the neural network output.