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PDBsum entry 2rb2
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* Residue conservation analysis
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Enzyme class:
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E.C.3.2.1.17
- lysozyme.
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Reaction:
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Hydrolysis of the 1,4-beta-linkages between N-acetyl-D-glucosamine and N-acetylmuramic acid in peptidoglycan heteropolymers of the prokaryotes cell walls.
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DOI no:
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J Mol Biol
377:914-934
(2008)
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PubMed id:
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Rescoring docking hit lists for model cavity sites: predictions and experimental testing.
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A.P.Graves,
D.M.Shivakumar,
S.E.Boyce,
M.P.Jacobson,
D.A.Case,
B.K.Shoichet.
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ABSTRACT
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Molecular docking computationally screens thousands to millions of organic
molecules against protein structures, looking for those with complementary fits.
Many approximations are made, often resulting in low "hit rates." A strategy to
overcome these approximations is to rescore top-ranked docked molecules using a
better but slower method. One such is afforded by molecular
mechanics-generalized Born surface area (MM-GBSA) techniques. These more
physically realistic methods have improved models for solvation and
electrostatic interactions and conformational change compared to most docking
programs. To investigate MM-GBSA rescoring, we re-ranked docking hit lists in
three small buried sites: a hydrophobic cavity that binds apolar ligands, a
slightly polar cavity that binds aryl and hydrogen-bonding ligands, and an
anionic cavity that binds cationic ligands. These sites are simple;
consequently, incorrect predictions can be attributed to particular errors in
the method, and many likely ligands may actually be tested. In retrospective
calculations, MM-GBSA techniques with binding-site minimization better
distinguished the known ligands for each cavity from the known decoys compared
to the docking calculation alone. This encouraged us to test rescoring
prospectively on molecules that ranked poorly by docking but that ranked well
when rescored by MM-GBSA. A total of 33 molecules highly ranked by MM-GBSA for
the three cavities were tested experimentally. Of these, 23 were observed to
bind--these are docking false negatives rescued by rescoring. The 10 remaining
molecules are true negatives by docking and false positives by MM-GBSA. X-ray
crystal structures were determined for 21 of these 23 molecules. In many cases,
the geometry prediction by MM-GBSA improved the initial docking pose and more
closely resembled the crystallographic result; yet in several cases, the
rescored geometry failed to capture large conformational changes in the protein.
Intriguingly, rescoring not only rescued docking false positives, but also
introduced several new false positives into the top-ranking molecules. We
consider the origins of the successes and failures in MM-GBSA rescoring in these
model cavity sites and the prospects for rescoring in biologically relevant
targets.
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Selected figure(s)
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Figure 3.
Fig. 3. Predicted and experimental ligand orientations for
the hydrophobic L99A cavity. The carbon atoms of the
crystallographic pose, the DOCK predicted pose, the AMBERDOCK
predicted pose, and the PLOP predicted pose are colored gray,
yellow, cyan, and magenta, respectively. The F[o] − F[c] omit
electron density maps (green mesh) are contoured at 2.5–3.0σ
(a) β-chlorophenetole (1), (b) 4-(methylthio)nitrobenzene (2),
(c) 2,6-difluorobenzylbromide (4), (d) 2-ethoxyphenol (5), and
(e) 3-methylbenzylazide (6) bound to L99A.
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Figure 4.
Fig. 4. Predicted and experimental ligand orientations for
the polar L99A/M102Q cavity site. The carbon atoms of the
crystallographic, DOCK, AMBERDOCK, and PLOP predicted poses are
colored gray, yellow, cyan, and magenta, respectively. Hydrogen
bonds are depicted with dashed lines. The F[o] − F[c] electron
density omit maps (green mesh) are contoured at 2.5–3.0σ. (a)
n-Phenylglycinonitrile (10), (b) 2-nitrothiophene (11), (c)
2-(n-propylthio)ethanol (12), (d) 3-methylbenzylazide (6), (e)
2-phenoxyethanol (9), and (f)
(R)-(+)-3-chloro-1-phenyl-1-propanol (13) bound to L99A/M102Q.
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The above figures are
reprinted
by permission from Elsevier:
J Mol Biol
(2008,
377,
914-934)
copyright 2008.
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Figures were
selected
by an automated process.
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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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J.D.Chodera,
D.L.Mobley,
M.R.Shirts,
R.W.Dixon,
K.Branson,
and
V.S.Pande
(2011).
Alchemical free energy methods for drug discovery: progress and challenges.
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Curr Opin Struct Biol,
21,
150-160.
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M.A.Lill,
and
M.L.Danielson
(2011).
Computer-aided drug design platform using PyMOL.
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J Comput Aided Mol Des,
25,
13-19.
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R.J.Woods,
and
M.B.Tessier
(2010).
Computational glycoscience: characterizing the spatial and temporal properties of glycans and glycan-protein complexes.
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Curr Opin Struct Biol,
20,
575-583.
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C.S.Rapp,
C.Schonbrun,
M.P.Jacobson,
C.Kalyanaraman,
and
N.Huang
(2009).
Automated site preparation in physics-based rescoring of receptor ligand complexes.
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Proteins,
77,
52-61.
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D.L.Mobley,
and
K.A.Dill
(2009).
Binding of small-molecule ligands to proteins: "what you see" is not always "what you get".
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Structure,
17,
489-498.
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M.J.Levesque,
K.Ichikawa,
S.Date,
and
J.H.Haga
(2009).
Design of a grid service-based platform for in silico protein-ligand screenings.
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Comput Methods Programs Biomed,
93,
73-82.
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Y.Chen,
and
B.K.Shoichet
(2009).
Molecular docking and ligand specificity in fragment-based inhibitor discovery.
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Nat Chem Biol,
5,
358-364.
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PDB codes:
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T.Pencheva,
D.Lagorce,
I.Pajeva,
B.O.Villoutreix,
and
M.A.Miteva
(2008).
AMMOS: Automated Molecular Mechanics Optimization tool for in silico Screening.
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BMC Bioinformatics,
9,
438.
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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.
Where a reference describes a PDB structure, the PDB
codes are
shown on the right.
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