K Abelak MDO Poster lq .pdf
Original filename: K_Abelak_MDO_Poster_lq.pdf
This PDF 1.3 document has been generated by PowerPoint / Mac OS X 10.11.2 Quartz PDFContext, and has been sent on pdf-archive.com on 02/10/2016 at 13:23, from IP address 50.255.x.x.
The current document download page has been viewed 1572 times.
File size: 2.1 MB (1 page).
Privacy: public file
Download original PDF file
K_Abelak_MDO_Poster_lq.pdf (PDF, 2.1 MB)
Share on social networks
Link to this file download page
Molecular Dynamics as a Tool to Investigate Fatty
Acid Binding in Cytochrome P450 2J2
Kavin Abelak1,2, David Bishop-Bailey1, Irilenia Nobeli2
Royal Veterinary College; 2 Birkbeck, University of London
• CYP2J2 is the main enzyme in human cardiovasculature responsible for epoxidation of arachidonic acid (AA).
• These metabolites, known as epoxyeicosatrienoic acids (EETs), have roles in prevention of apoptosis and inflammation,
as well as stimulation of angiogenesis (Fig. 1B).
• Crystal structure of CYP2J2 is unavailable, thus its study requires homology modelling.
• Other groups have modelled CYP2J2 using templates of varying resolutions and with different ligands bound2-5.
• Cong et al. (2013) described interaction of AA carboxylate group to a main-chain carbon atom.
• We concentrate on homology modelling and docking analysis of CYP2J2 specific to binding and metabolism of AA.
Fig. 1: Eicosanoid Pathway
A) Pathway for epoxidation of membrane-bound
ARA by CYP. Phospholipase A2 (PLA2) is activated
by Ca2+, leading to AA release from phospholipids.
AA is converted into one of the four possible
regioisomers by CYP, a reaction requiring NADPH
B) Pathways for the action of EETs, using 11,12-EET
• 10 homology models produced and evaluated using QMEAN, ERRAT, Verify3D and Ramachandran plots; Best scoring model selected.
• Post-minimisation score similar in QMEAN; better in ERRAT (64.5 vs 85.6); Verify3D profile improved.
• AutoDock VINA poses reliably fill active site cavity (Fig. 2) and channel but binding energies not overly favourable (≈-6kcal/mol).
• SSR/SST elbow point ranged from 7-10 clusters; 7 clusters used during clustering and most populated cluster picked for further analysis.
• MM-PBSA energies most favourable for poses 1, 2 and 6 (Table 1); However only in poses 2 and 6 was the AA tail close to the heme.
• Hydrogen bonds analysis showed Arg111 and/or Arg117 interacted with carboxylate group of AA in all independent simulations, except pose 3.
• Mutation of Arg117 to Ala changed hydrogen bond profile and worsened binding energies (Tables 1 and 2).
Fig. 2: AutoDock VINA predicted poses
CYP2J2 model shown in pink transparency with heme in black. Six AA binding poses are shown (pose 1 –
black, pose 2 – red, pose 3 – green, pose 4 – blue, pose 5 – yellow, pose 6 – brown).
Table 1: Binding Energies
Table 2: Hydrogen Bonds
Fig. 4: Pose 6 Analysis
Fig. 3: Pose 2 Analysis
Fig. 1 A and 1 B adapted from Spector & Kim (2015)
Materials and Methods
Representative structure for main cluster. Wildtype CYP2J2
binding AA shown in red; R117A mutant shown in pink.
Representative structure for main cluster. Wildtype CYP2J2
binding AA shown in brown; R117A mutant shown in yellow.
• Homology modelling using MODELLER 9v14 and multiple sequence alignment from ClustalW2; Heme added from 1SUO
structure file; Side-chain orientations and protonation states refined using MolProbity’s Reduce function.
• Parameters for heme in penta-coordinated high-spin Fe3+ form obtained from Shahrokh et al. (2012); Deprotonated AA
parameters from Automatic Topology Builder, charges derived using Antechamber module of AMBER.
Fraction – Proportion of frames out of the total number
within the analysed cluster that the contact is present.
• 1st Molecular Dynamics (MD) run to minimise model – 3 sequential runs restraining: solute > heavy atoms > backbone.
• Docking using AutoDock VINA after optimisation of grid box; charges imported from MD topology file; top 6 most
energetically favourable poses selected for complex creation.
• Further MD: Minimised complexes (4x10,000 steps); Heated to 310K (200ps); Equilibrated at 1.0bar while restraining
complex (20ps) and backbone (20ps); Equilibrated without restraints (100ps); Production run (1μs) recording energies
and coordinates every 10ps, repeated 3 more times.
• R117A mutants of docked complexes created using UCSF Chimera’s swapaa command; MD as above.
• All MD carried out using AMBER14 with AmberTools15. Settings: ff14SB and gaff; truncated octahedron box; PBCs;
TIP3P water; 10Å non-bond cut-off; SHAKE; Langevin dynamics with random seed for velocities; 2fs time-step.
• Sum of squares regression (SSR)/Total sum of squares (SST) ratio calculated to optimise number of clusters;
Hierarchical agglomerative clustering on mass-weighed RMSD of heavy atoms within 7Å of AA in the complex.
• 3D structure created here used better resolution templates than previously published models.
• Presence of Arg residues close to AA carboxylic acid head stabilise the binding.
• In poses 1, 2 and 6 interactions occur more frequently and could explain favourable binding energy.
• Degradation in binding energy in R117A mutants shows importance of Arg117 residue in active site.
• MM-PBSA binding energy calculated on subset of each trajectory corresponding to most populated cluster.
• Mutation of Arg111 and double R111A/R117A mutants will improve understanding of active site interactions.
• Free energy, 𝐺 = 𝐸%&' + 𝐸)'* + 𝐸+, + 𝐺-., + 𝐺&- − 𝑇𝑆 ; Binding energy, ∆𝐺%3&' = 𝐺45 − 𝐺4 − 𝐺5
• Overall MD provides valuable starting information on the potential effects of mutations prior to designing
• Hydrogen bonds determined using hbonds analysis in AMBER; Only main clusters considered.
Scan the QR code for
an electronic version
of the poster as well
as contact information
resource-intensive confirmatory in vitro experiments.
The authors would like to acknowledge that
the work presented here made use of
Emerald, a GPU-accelerated High
Performance Computer, made available by the
Science & Engineering South
Consortium operated in partnership with the
STFC Rutherford-Appleton Laboratory.
Spector, A.A. & Kim, H.-Y., 2015. Cytochrome P450 epoxygenase pathway of polyunsaturated fatty acid metabolism. Biochimica et biophysica acta, 1851(4), pp.356–365.
Lafite, P., André, F. & Zeldin, D., 2007. Unusual regioselectivity and active site topology of human cytochrome P450 2J2. Biochemistry, 46(36), pp.10237–10247.
Li, W. et al., 2008. Probing ligand binding modes of human cytochrome P450 2J2 by homology modeling, molecular dynamics simulation, and flexible molecular docking. Proteins, 71(2), pp.938–49.
Lee, C., Neul, D. & Clouser-Roche, A., 2010. Identification of Novel Substrates for Human Cytochrome P450 2J2. Drug Metabolism and Disposition, 38(2), pp.347–356.
Cong, S. et al., 2013. Structural basis for the mutation-induced dysfunction of human CYP2J2: a computational study. Journal of chemical information and modeling, 53(6), pp.1350–7.
Kirchmair, J. et al., 2012. Computational prediction of metabolism: Sites, products, SAR, P450 enzyme dynamics, and mechanisms. Journal of Chemical Information and Modeling, 52(3), pp.617–648.
Shahrokh, K. et al., 2012. Quantum mechanically derived AMBER-compatible heme parameters for various states of the cytochrome P450 catalytic cycle. Journal of Computational Chemistry, 33(2), pp.119–133.
Chen, V. et al., 2010. MolProbity: all –atom structure validation for macromolecular crystallography. Acta Crystallographica, D66, pp.12-21.
Link to this page
Use the permanent link to the download page to share your document on Facebook, Twitter, LinkedIn, or directly with a contact by e-Mail, Messenger, Whatsapp, Line..
Use the short link to share your document on Twitter or by text message (SMS)
Copy the following HTML code to share your document on a Website or Blog