PGAR-Zernike: an ultra-fast, accurate and fully open-source structure retrieval toolkit for convenient structural database construction

Abstract

With the release of AlphaFold2, protein model databases are growing at an unprecedented rate. Efficient structure retrieval schemes are becoming more and more important to quickly analyze structure models. The core problem in structural retrieval is how to measure the similarity between structures. Some structure alignment algorithms can solve this problem but at a substantial time cost. At present, the state-of-the-art method is to convert protein structures into 3D Zernike descriptors and evaluate the similarity between structures by Euclidean distance. However, methods for computing 3D Zernike descriptors of protein structures are almost always based on structural surfaces and most are web servers, which is not conducive for users to analyze customized datasets. To overcome this limitation, we propose PGAR-Zernike, a convenient toolkit for computing different types of Zernike descriptors of structures: the user simply needs to enter one line of command to calculate the Zernike descriptors of all structures in a customized datasets. Compared with the state-of-the-art method based on 3D Zernike descriptors and an efficient structural comparison tool, PGAR-Zernike achieves higher retrieval accuracy and binary classification accuracy on benchmark datasets with different attributes. In addition, we show how \red{PGAR-Zernike} completes the construction of the descriptor database and the protocol used for the PDB dataset so as to facilitate the local deployment of this tool for interested readers. We construct a demonstration containing 590685 structures; at this scale, our retrieval system takes only 4 to 9 seconds to complete a retrieval, and experiments show that it reaches the state-of-the-art level in terms of accuracy. PGAR-Zernike is an open-source toolkit, whose source code and related data are accessible at \url{https://github.com/junhaiqi/PGAR-Zernike/