Hierarchical Bayesian Modeling of Pharmacophores in ...

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Oct 1, 2013 (3 years and 11 months ago)

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Hierarchical Bayesian Modeling of
Pharmacophores in Bioinformatics

Author(s):

Mardia, KV
(Mardia, Kanti V.)
1
;
Nyirongo, VB
(Nyirongo, Vysaul B.)
1
;
Fallaize, CJ
(Fallaize, Christopher J.)
1
;
Barber, S
(Barber, Stuart)
1
;
Jackson, RM
(Jackson, Richard M.)
2


Source:
BIOMETRICS


Volume:

67


Issue:

2


Pages:

611
-
619


DOI:

10.1111/j.1541
-
0420.2010.01460.x


Published:

JUN
2011

Times Cited:

0 (from Web of Science)

Cited References:

17

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Abstract:

One of the key ingredients in drug discovery is the
derivation of conceptual templates called pharmacophores. A
pharmacophore model characterizes the physicochemical
properties common to all active molecules, called ligands, bound
to a particular protein
receptor, together with their relative spatial
arrangement. Motivated by this important application, we develop
a Bayesian hierarchical model for the derivation of pharmacophore
templates from multiple configurations of point sets, partially
labeled by the

atom type of each point. The model is implemented
through a multistage template hunting algorithm that produces a
series of templates that capture the geometrical relationship of
atoms matched across multiple configurations. Chemical
information is incorp
orated by distinguishing between atoms of
different elements, whereby different elements are less likely to be
matched than atoms of the same element. We illustrate our
method through examples of deriving templates from sets of
ligands that all bind struct
urally related protein active sites and
show that the model is able to retrieve the key pharmacophore
features in two test cases.

Accession Number:

WOS:000292504000029

Document Type:

Article

Language:

English

Author Keywords:

Chemoinformatics
; Ligands; Markov chain
Monte Carlo; Multiple alignment; Pharmacophore; Shape analysis;
Spatial matching; Template

KeyWords Plus:

BINDING
-
SITES; EM ALGORITHM; PROTEIN;
ALIGNMENT

Reprint Address:

Mardia, KV (reprint author), Univ Leeds, Dept
Stat, Leeds LS2 9JT, W Yorkshire, England.

Addresses:


1. Univ Leeds, Dept Stat, Leeds LS2 9JT, W Yorkshire, England

2. Univ Leeds, Inst Mol & Cellular Biol, Leeds LS2 9JT, W
Yorkshire, England

E
-
mail Add
ress:

k.v.mardia@maths.leeds.ac.uk
;
stavn@maths.leeds.ac.uk
;
chrisf@maths.leeds.ac.uk
;
stuart@maths.leeds.ac.uk
;
r.m.jackson@leeds.ac.uk


Funding:

Funding Agency

Grant Number

EPSRC





[Show funding text]





Publisher:

WILEY
-
BLACKWELL, COMMERCE PLACE, 350
MAIN ST, MALDEN 02148, MA USA

Web of Science Categories:

Biology; Mathematical &
Computational Biology; Statistics & Probability

Times Cited:


0


This
article has been cited
0 times in Web of
Knowledge.

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Research Areas:

Life Sciences & Biomedicine
-

Other Topics;
Mathematical & Computational Biology; Mathematics

IDS Number:

789GS

ISSN:

0006
-
341X





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