1 research outputs found
Efficient and scalable geometric hashing method for searching protein 3D structures
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.Comment: 9 pages, 1 figure