©1996-2019 All
Rights Reserved.
Online Journal of Bioinformatics . You may not store
these pages in any form except for your own personal use. All other usage or
distribution is illegal under international copyright treaties. Permission to
use any of these pages in any other way besides the
before mentioned must be gained in writing from the publisher. This
article is exclusively copyrighted in its entirety to OJB publications. This
article may be copied once but may not be, reproduced or
re-transmitted without the express permission of the editors. This journal satisfies the refereeing requirements (DEST) for the Higher
Education Research Data Collection (Australia). Linking:To link to this page or any pages linking to this page
you must link directly to this page only here rather than put up your own page.
OJB®
Online
Journal of Bioinformatics ©
Volume 7
(2):74-84, 2006.
Structural Classification of Protein Using Surface Roughness Index
Singha S1,
Lahiri T1*, Dasgupta
AK2, Chakrabarti P3.
1Bioinformatics
Division,
Indian Institute of Information Technology, Allahabad and 2Department
of Biochemistry, Calcutta University and 3Department
of Biochemistry, Bose Institute, P1/12 CIT Scheme VIIM, Kolkata- 700 054,
India.
ABSTRACT
Singha S, Lahiri T, Dasgupta AK, Chakrabarti P.,
Structural Classification of Protein Using Surface Roughness Index, Onl J Bioinform., 7 (2):74-84,
2006. A
protein structural classification using surface roughness properties is
described. A protein surface characterizing parameter, Surface Roughness Index was designed which is made as an
invariant measure of surface geometry with respect to any orientation of a
protein. It was found that the topology of protein can be described from
the angle of its surface-roughness property which can serve to identify a
protein. Structural Classification of Proteins (SCOP) classify protein
into classes-folds-superfamilies-families which
correlated with the proposed classifier system to a reasonable extend. The
deviation from the classification result yielded by the proposed method from
that of the SCOP is explained and the significance of the information mined
from this deviation from SCOP is discussed.
KEYWORDS Classifier system, Invariant
measure, SCOP, Surface Roughness Index.