Efficient and scalable geometric hashing method for searching protein 3D structures

TL;DR

This paper introduces an efficient geometric hashing-based method for searching protein 3D structures, focusing on large-scale data handling and reducing computational costs with novel query types and optimization techniques.

Contribution

It proposes a new scalable algorithm for protein structure search using geometric hashing, with improvements for large datasets and practical application.

Findings

True positive rate of at least 0.8 in experiments

Effective reduction in storage and execution time

Reliable matching performance demonstrated

Abstract

As the structural databases continue to expand, efficient methods are required to search similar structures of the query structure from the database. There are many previous works about comparing protein 3D structures and scanning the database with a query structure. However, they generally have limitations on practical use because of large computational and storage requirements. We propose two new types of queries for searching similar sub-structures on the structural database: LSPM (Local Spatial Pattern Matching) and RLSPM (Reverse LSPM). Between two types of queries, we focus on RLSPM problem, because it is more practical and general than LSPM. As a naive algorithm, we adopt geometric hashing techniques to RLSPM problem and then propose our proposed algorithm which improves the baseline algorithm to deal with large-scale data and provide an efficient matching algorithm. We employ the sub-sampling and Z-ordering to reduce the storage requirement and execution time, respectively. We conduct our experiments to show the correctness and reliability of the proposed method. Our experiment shows that the true positive rate is at least 0.8 using the reliability measure.