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Updated: Oct 17, 2025

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

FoldHSphere: deep hyperspherical embeddings for protein fold recognition.

Amelia Villegas-Morcillo¹, Victoria Sanchez², Angel M Gomez²

¹Department of Signal Theory, Telematics and Communications, University of Granada, Periodista Daniel Saucedo Aranda, 18071, Granada, Spain. ameliavm@ugr.es.

BMC Bioinformatics

|October 13, 2021

Summary

We introduce FoldHSphere, a novel method for protein fold recognition that learns better protein embeddings. This approach successfully bridges the performance gap in identifying protein families and folds, even with low sequence similarity.

Keywords:

Deep neural networks Embedding learning Hyperspherical space Protein fold recognition Residual convolutions Thomson problem

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Last Updated: Oct 17, 2025

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Published on: July 16, 2017

Area of Science:

Computational biology
Bioinformatics
Machine learning in structural biology

Background:

Current deep learning models for protein fold recognition create embeddings that enhance prediction accuracy.
A performance gap persists at the fold and family levels, indicating room for improved protein fold representation.

Purpose of the Study:

To develop a superior embedding space for protein fold recognition.
To enhance the accuracy of predicting protein structures and families.

Main Methods:

The FoldHSphere method utilizes a two-stage training process.
Prototype vectors for each fold class are generated in hyperspherical space.
A neural network is trained using angular large margin cosine loss for embeddings clustered around hyperspherical prototypes.
ResCNN-GRU and ResCNN-BGRU architectures process protein sequences.

Main Results:

Hyperspherical embeddings effectively reduce the performance gap at both family and fold levels.
The FoldHSpherePro ensemble achieved 81.3% accuracy in fold-level prediction, surpassing existing state-of-the-art methods.
The method demonstrates efficiency in learning discriminative and representative embeddings for protein domains.

Conclusions:

The proposed hyperspherical embeddings are effective for identifying protein fold classes via pairwise comparison.
This approach is particularly powerful for distinguishing folds even with low amino acid sequence similarity.