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PDBsum entry 2rbr

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protein ligands links
Hydrolase PDB id
2rbr
Jmol
Contents
Protein chain
162 a.a.
Ligands
PO4
268
Waters ×286
PDB id:
2rbr
Name: Hydrolase
Title: 2-phenoxyethanol in complex with t4 lysozyme l99a/m102q
Structure: Lysozyme. Chain: a. Synonym: lysis protein. Muramidase. Endolysin. Engineered: yes. Mutation: yes
Source: Enterobacteria phage t4. Organism_taxid: 10665. Gene: e. Expressed in: escherichia coli. Expression_system_taxid: 562.
Resolution:
1.43Å     R-factor:   0.181     R-free:   0.200
Authors: A.P.Graves,S.E.Boyce,B.K.Shoichet
Key ref:
A.P.Graves et al. (2008). Rescoring docking hit lists for model cavity sites: predictions and experimental testing. J Mol Biol, 377, 914-934. PubMed id: 18280498 DOI: 10.1016/j.jmb.2008.01.049
Date:
19-Sep-07     Release date:   18-Mar-08    
PROCHECK
Go to PROCHECK summary
 Headers
 References

Protein chain
Pfam   ArchSchema ?
P00720  (LYS_BPT4) -  Endolysin
Seq:
Struc:
164 a.a.
162 a.a.*
Key:    PfamA domain  Secondary structure  CATH domain
* PDB and UniProt seqs differ at 4 residue positions (black crosses)

 Enzyme reactions 
   Enzyme class: E.C.3.2.1.17  - Lysozyme.
[IntEnz]   [ExPASy]   [KEGG]   [BRENDA]
      Reaction: Hydrolysis of the 1,4-beta-linkages between N-acetyl-D-glucosamine and N-acetylmuramic acid in peptidoglycan heteropolymers of the prokaryotes cell walls.
 Gene Ontology (GO) functional annotation 
  GO annot!
  Cellular component     host cell cytoplasm   1 term 
  Biological process     metabolic process   6 terms 
  Biochemical function     catalytic activity     4 terms  

 

 
DOI no: 10.1016/j.jmb.2008.01.049 J Mol Biol 377:914-934 (2008)
PubMed id: 18280498  
 
 
Rescoring docking hit lists for model cavity sites: predictions and experimental testing.
A.P.Graves, D.M.Shivakumar, S.E.Boyce, M.P.Jacobson, D.A.Case, B.K.Shoichet.
 
  ABSTRACT  
 
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.
 
  Selected figure(s)  
 
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.
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.
 
  The above figures are reprinted by permission from Elsevier: J Mol Biol (2008, 377, 914-934) copyright 2008.  
  Figures were selected by an automated process.  

Literature references that cite this PDB file's key reference

  PubMed id Reference
21349700 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.
  Curr Opin Struct Biol, 21, 150-160.  
21053052 M.A.Lill, and M.L.Danielson (2011).
Computer-aided drug design platform using PyMOL.
  J Comput Aided Mol Des, 25, 13-19.  
20708922 R.J.Woods, and M.B.Tessier (2010).
Computational glycoscience: characterizing the spatial and temporal properties of glycans and glycan-protein complexes.
  Curr Opin Struct Biol, 20, 575-583.  
19382204 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.
  Proteins, 77, 52-61.  
19368882 D.L.Mobley, and K.A.Dill (2009).
Binding of small-molecule ligands to proteins: "what you see" is not always "what you get".
  Structure, 17, 489-498.  
18771812 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.
  Comput Methods Programs Biomed, 93, 73-82.  
19305397 Y.Chen, and B.K.Shoichet (2009).
Molecular docking and ligand specificity in fragment-based inhibitor discovery.
  Nat Chem Biol, 5, 358-364.
PDB codes: 3g2y 3g2z 3g30 3g31 3g32 3g34 3g35
18925937 T.Pencheva, D.Lagorce, I.Pajeva, B.O.Villoutreix, and M.A.Miteva (2008).
AMMOS: Automated Molecular Mechanics Optimization tool for in silico Screening.
  BMC Bioinformatics, 9, 438.  
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.