FoldExplorer: Fast and Accurate Protein Structure Search with Sequence-Enhanced Graph Embedding

TL;DR

FoldExplorer is a deep learning-based tool that combines graph attention networks and protein language models to enable fast, accurate, and scalable protein structure searches, significantly outperforming existing methods.

Contribution

It introduces a novel graph attention neural network and language model-based embedding approach for protein structures, enhancing search speed and accuracy.

Findings

Achieves 5-8% performance improvement over state-of-the-art methods.

Maintains high speed on large-scale datasets.

Provides meaningful insights into protein space.

Abstract

The advent of highly accurate protein structure prediction methods has fueled an exponential expansion of the protein structure database. Consequently, there is a rising demand for rapid and precise structural homolog search. Traditional alignment-based methods are dedicated to precise comparisons between pairs, exhibiting high accuracy. However, their sluggish processing speed is no longer adequate for managing the current massive volume of data. In response to this challenge, we propose a novel deep-learning approach FoldExplorer. It harnesses the powerful capabilities of graph attention neural networks and protein large language models for protein structures and sequences data processing to generate embeddings for protein structures. The structural embeddings can be used for fast and accurate protein search. The embeddings also provide insights into the protein space. FoldExplorer demonstrates a substantial performance improvement of 5% to 8% over the current state-of-the-art algorithm on the benchmark datasets. Meanwhile, FoldExplorer does not compromise on search speed and excels particularly in searching on a large-scale dataset.