  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.
