Unknown

Dataset Information

0

Effects of the difference in similarity measures on the comparison of ligand-binding pockets using a reduced vector representation of pockets.


ABSTRACT: Comprehensive analysis and comparison of protein ligand-binding pockets are important to predict the ligands which bind to parts of putative ligand binding pockets. Because of the recent increase of protein structure information, such analysis demands a fast and efficient method for comparing ligand binding pockets. Previously we proposed a fast alignment-free method based on a simple representation of a ligand binding pocket with one 11-dimensional vector, which is suitable for such analysis. Based on that method, we conducted this study to expand and revise similarity measures of binding pockets and to investigate the effects of those modifications with two datasets for improving the ability to detect similar binding pockets. The new method exhibits higher detection performance of similar binding pockets than the previous methods and another existing accurate alignment-dependent method: APoc. Results also show that the effects of the modifications depend on the difficulty of the dataset, implying some avenues for methods of improvement.

SUBMITTER: Nakamura T 

PROVIDER: S-EPMC5042158 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

altmetric image

Publications

Effects of the difference in similarity measures on the comparison of ligand-binding pockets using a reduced vector representation of pockets.

Nakamura Tsukasa T   Tomii Kentaro K  

Biophysics and physicobiology 20160714


Comprehensive analysis and comparison of protein ligand-binding pockets are important to predict the ligands which bind to parts of putative ligand binding pockets. Because of the recent increase of protein structure information, such analysis demands a fast and efficient method for comparing ligand binding pockets. Previously we proposed a fast alignment-free method based on a simple representation of a ligand binding pocket with one 11-dimensional vector, which is suitable for such analysis. B  ...[more]

Similar Datasets

| S-EPMC7144620 | biostudies-literature
| S-EPMC3294165 | biostudies-literature
| S-EPMC3029693 | biostudies-literature
| S-EPMC4112621 | biostudies-literature
| S-EPMC3245044 | biostudies-literature
| S-EPMC4631714 | biostudies-literature
| S-EPMC5845264 | biostudies-literature
| S-EPMC2898321 | biostudies-literature
| S-EPMC3599440 | biostudies-literature
| S-EPMC6375647 | biostudies-literature