Jian Yu1, Yong Zhou, Isao Tanaka, Min Yao. 1. Graduate School of Life Science, Hokkaido University, Kita-Ku Kita-10 Nishi-8, Sapporo 0600810, Japan.
Abstract
MOTIVATION: Prediction of ligand binding sites of proteins is significant as it can provide insight into biological functions and reaction mechanisms of proteins. It is also a prerequisite for protein-ligand docking and an important step in structure-based drug design. RESULTS: We present a new algorithm, Roll, implemented in a program named POCASA, which can predict binding sites by detecting pockets and cavities of proteins with a rolling sphere. To evaluate the performance of POCASA, a test with the same data set as used in several existing methods was carried out. POCASA achieved a high success rate of 77%. In addition, the test results indicated that POCASA can predict good shapes of ligand binding sites. AVAILABILITY: A web version of POCASA is freely available at http://altair.sci.hokudai.ac.jp/g6/Research/POCASA_e.html.
MOTIVATION: Prediction of ligand binding sites of proteins is significant as it can provide insight into biological functions and reaction mechanisms of proteins. It is also a prerequisite for protein-ligand docking and an important step in structure-based drug design. RESULTS: We present a new algorithm, Roll, implemented in a program named POCASA, which can predict binding sites by detecting pockets and cavities of proteins with a rolling sphere. To evaluate the performance of POCASA, a test with the same data set as used in several existing methods was carried out. POCASA achieved a high success rate of 77%. In addition, the test results indicated that POCASA can predict good shapes of ligand binding sites. AVAILABILITY: A web version of POCASA is freely available at http://altair.sci.hokudai.ac.jp/g6/Research/POCASA_e.html.
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