Protein functional-group 3D motif and its applications

Citation
Yz. Ye et al., Protein functional-group 3D motif and its applications, CHIN SCI B, 45(22), 2000, pp. 2044-2052
Citations number
22
Categorie Soggetti
Multidisciplinary
Journal title
CHINESE SCIENCE BULLETIN
ISSN journal
10016538 → ACNP
Volume
45
Issue
22
Year of publication
2000
Pages
2044 - 2052
Database
ISI
SICI code
1001-6538(200011)45:22<2044:PF3MAI>2.0.ZU;2-M
Abstract
Representing and recognizing protein active sites sequence motif (1D motif) and structural motif (3D motif) is an important topic for predicting and d esigning protein function. Prevalent methods for extracting and searching 3 D motif always consider residue as the minimal unit, which have limited sen sitivity. Here we present a new spatial representation of protein active si tes, called "functional-group 3D motif", based on the fact that the functio nal groups inside a residue contribute mostly to its function. Relevant alg orithm and computer program are developed, which could be widely used in th e function prediction and the study of structural-function relationship of proteins. As a test, we defined a functional-group 30 motif of the catalyti c triad and oxyanion hole with the structure of porcine trypsin (PDB code: 1mct) as the template. With our motif-searching program, we successfully fo und similar sub-structures in trypsins, subtilisins and alp hydrolases, whi ch show distinct folds but share similar catalytic mechanism. Moreover, thi s motif can be used to elucidate the structural basis of other proteins wit h variant catalytic triads by comparing it to those proteins. Finally, we s canned this motif against a non-redundant protein structure database to fin d its matches, and the results demonstrated the potential application of fu nctional group 3D motif in function prediction. Above all, compared with th e other 3D-motif representations on residues, the functional group 3D motif achieves better representation of protein active region, which is more sen sitive for protein function prediction.