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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Learning Proteome Domain Folding Using LSTMs in an Empirical Kernel Space.

Da Kuang¹, Dina Issakova², Junhyong Kim³

¹Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA.

Journal of Molecular Biology

|June 18, 2022

Summary

This study introduces an improved protein structure prediction method using a sequential neural network and a novel feature space. The approach enhances protein fold recognition accuracy with smaller training datasets and better cross-species generalization.

Keywords:

SCOPe long short-term memory networks protein empirical structure space proteins fold

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Published on: July 16, 2017

Area of Science:

Computational biology
Bioinformatics
Structural biology

Background:

Protein structural fold recognition is crucial for inferring protein function and guiding structural prediction.
Previous work introduced the Protein Empirical Structure Space (PESS) for protein structure prediction.

Purpose of the Study:

To extend the PESS approach for enhanced protein fold recognition.
To develop a more efficient and generalizable method for predicting protein structures.

Main Methods:

Generating the PESS feature space over fixed-length subsequences of query peptides.
Applying a sequential neural network model with a long short-term memory (LSTM) cell layer and a fully connected layer.

Main Results:

Achieved near state-of-the-art accuracy on fold recognition using a small training set.
Demonstrated improved generalization across species compared to previous methods.
Reduced the required training data size for effective protein fold prediction.

Conclusions:

The novel sequential neural network approach with PESS significantly improves protein fold recognition.
This method offers a more efficient and broadly applicable tool for predicting structures of newly discovered proteins.