Automatic generation of 3D motifs for classification of protein binding sites |
| |
Authors: | Jean-Christophe Nebel Pawel Herzyk David R Gilbert |
| |
Institution: | (1) Faculty of Computing, Information Systems & Mathematics, Kingston University, KT1 2EE Kingston-upon-Thames, UK;(2) Bioinformatics Research Centre, University of Glasgow, G12 8QQ Glasgow, UK;(3) The Sir Henry Wellcome Functional Genomics Facility, Institute of Biomedical and Life Sciences, G12 8QQ University of Glasgow Glasgow, UK |
| |
Abstract: | Background Since many of the new protein structures delivered by high-throughput processes do not have any known function, there is a
need for structure-based prediction of protein function. Protein 3D structures can be clustered according to their fold or
secondary structures to produce classes of some functional significance. A recent alternative has been to detect specific
3D motifs which are often associated to active sites. Unfortunately, there are very few known 3D motifs, which are usually
the result of a manual process, compared to the number of sequential motifs already known. In this paper, we report a method
to automatically generate 3D motifs of protein structure binding sites based on consensus atom positions and evaluate it on
a set of adenine based ligands. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|