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* Residue conservation analysis
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Enzyme class 1:
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Chains A, B:
E.C.6.3.4.14
- Biotin carboxylase.
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Reaction:
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ATP + biotin-[carboxyl-carrier-protein] + CO2 = ADP + phosphate + carboxy-biotin-[carboxyl-carrier-protein]
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ATP
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+
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biotin-[carboxyl-carrier-protein]
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+
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CO(2)
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=
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ADP
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+
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phosphate
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+
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carboxy-biotin-[carboxyl-carrier-protein]
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Enzyme class 2:
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Chains A, B:
E.C.6.4.1.2
- Acetyl-CoA carboxylase.
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Reaction:
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ATP + acetyl-CoA + HCO3- = ADP + phosphate + malonyl-CoA
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ATP
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+
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acetyl-CoA
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+
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HCO(3)(-)
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=
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ADP
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+
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phosphate
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+
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malonyl-CoA
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Cofactor:
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Biotin
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Biotin
Bound ligand (Het Group name =
OA4)
matches with 52.17% similarity
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Note, where more than one E.C. class is given (as above), each may
correspond to a different protein domain or, in the case of polyprotein
precursors, to a different mature protein.
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Molecule diagrams generated from .mol files obtained from the
KEGG ftp site
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Gene Ontology (GO) functional annotation
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Biological process
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metabolic process
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4 terms
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Biochemical function
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catalytic activity
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6 terms
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DOI no:
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Acs Chem Biol
4:473-483
(2009)
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PubMed id:
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Discovery of antibacterial biotin carboxylase inhibitors by virtual screening and fragment-based approaches.
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I.Mochalkin,
J.R.Miller,
L.Narasimhan,
V.Thanabal,
P.Erdman,
P.B.Cox,
J.V.Prasad,
S.Lightle,
M.D.Huband,
C.K.Stover.
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ABSTRACT
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As part of our effort to inhibit bacterial fatty acid biosynthesis through the
recently validated target biotin carboxylase, we employed a unique combination
of two emergent lead discovery strategies. We used both de novo fragment-based
drug discovery and virtual screening, which employs 3D shape and electrostatic
property similarity searching. We screened a collection of unbiased
low-molecular-weight molecules and identified a structurally diverse collection
of weak-binding but ligand-efficient fragments as potential building blocks for
biotin carboxylase ATP-competitive inhibitors. Through iterative cycles of
structure-based drug design relying on successive fragment costructures, we
improved the potency of the initial hits by up to 3000-fold while maintaining
their ligand-efficiency and desirable physicochemical properties. In one
example, hit-expansion efforts resulted in a series of amino-oxazoles with
antibacterial activity. These results successfully demonstrate that virtual
screening approaches can substantially augment fragment-based screening
approaches to identify novel antibacterial agents.
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Literature references that cite this PDB file's key reference
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PubMed id
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Reference
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A.Fabbretti,
C.O.Gualerzi,
and
L.Brandi
(2011).
How to cope with the quest for new antibiotics.
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FEBS Lett, 585,
1673-1681.
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B.R.Novak,
D.Moldovan,
G.L.Waldrop,
and
M.S.de Queiroz
(2011).
Behavior of the ATP grasp domain of biotin carboxylase monomers and dimers studied using molecular dynamics simulations.
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Proteins, 79,
622-632.
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P.J.Edwards
(2011).
The design and synthesis of libraries for the discovery of antibacterial and antifungal substances.
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Drug Discov Today, 16,
278-279.
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U.Pieper,
B.M.Webb,
D.T.Barkan,
D.Schneidman-Duhovny,
A.Schlessinger,
H.Braberg,
Z.Yang,
E.C.Meng,
E.F.Pettersen,
C.C.Huang,
R.S.Datta,
P.Sampathkumar,
M.S.Madhusudhan,
K.Sjölander,
T.E.Ferrin,
S.K.Burley,
and
A.Sali
(2011).
ModBase, a database of annotated comparative protein structure models, and associated resources.
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Nucleic Acids Res, 39,
D465-D474.
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A.G.Coyne,
D.E.Scott,
and
C.Abell
(2010).
Drugging challenging targets using fragment-based approaches.
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Curr Opin Chem Biol, 14,
299-307.
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C.W.Murray,
and
T.L.Blundell
(2010).
Structural biology in fragment-based drug design.
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Curr Opin Struct Biol, 20,
497-507.
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M.N.Gwynn,
A.Portnoy,
S.F.Rittenhouse,
and
D.J.Payne
(2010).
Challenges of antibacterial discovery revisited.
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Ann N Y Acad Sci, 1213,
5.
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The most recent references are shown first.
Citation data come partly from CiteXplore and partly
from an automated harvesting procedure. Note that this is likely to be
only a partial list as not all journals are covered by
either method. However, we are continually building up the citation data
so more and more references will be included with time.
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