1. Mechanistic Insights in to the
p53-CYPD interactions
Under the guidance of
Dr. Rajanikant G.K.
Associate Professor
School of Biotechnology, NITC
Mini Project Report by
YANAMALA VIJAY RAJ
MATHEW ALEXANDER CHERIAN
CHRISTINE JAMES MOHAN
3/25/2013
NATIONAL INSTITUTE OF TECHNOLOGY, CALICUT.
2. BACKGROUND
• p53 is a central stress sensor responding to multiple insults, including
oxidative stress
• In response to oxidative stress, p53 accumulates in the mitochondrial
matrix and triggers mitochondrial permeability transition pore (MPTP)
opening by physical interaction with the PTP regulator cyclophilin D
(CypD) (Angelina et al, 2012 )
• CypD is a component of mitochondrial permeability transition (MPT) and
mediates cell death (Schinzel et al, 2005)
• Intriguingly, a robust p53-CypD complex forms during cell death processes
like apoptosis and necrosis (Mihara et al, 2003)
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3. INTRODUCTION:
p53
• p53 (also known as protein 53 or tumor protein 53), is a tumor
suppressor protein that in humans is encoded by the TP53 gene located
on the short arm of chromosome 17
• Human p53 is 393 amino acids long and has seven domains.
FUNCTIONS:
p53 has many mechanisms of anticancer function, and plays a role in
apoptosis, genomic stability, and inhibition of angiogenesis. In its anticancer role, p53 works through several mechanisms:
• It can activate DNA repair proteins when DNA has sustained damage.
• It can induce growth arrest by holding the cell cycle at the G1/S
regulation point on DNA damage recognition (if it holds the cell here for
long enough, the DNA repair proteins will have time to fix the damage
and the cell will be allowed to continue the cell cycle).
• It can initiate apoptosis, the programmed cell death, if DNA damage
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proves to be irreparable.
4. P53 regulations
Activated by
•
•
•
•
DNA damage,
Oxidative stress,
Osmotic stress ,
Ribonucleotide depletion.
Activation marked by two events
• Half life of p53 increased drastically, leading to quick
accumulation ofp53,
• Conformational change of p53 to transcriptional regulation .
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5. Cyclophilin D (CYPD)
• Located on the matrix of mitochondria,
• It modulates opening of mitochondrial permeability transition
pore (MPTP),
• Opening of mitochondrial permeability transition pore
involved in regulating cell death by inducing a sustained and
irreversible loss of inner mitochondrial potential,
• Proteins of MPTP located in between in between inner and
outer mitochondrial membranes ,
• Cyclophilin stands as the only genetically verified component
of MPTP.
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6. OBJECTIVES
• Studying the binding modes and interactions of p53 and CYPD
• Analyzing the effect of CYPD inhibitor, Cyclosporin A (CsA) on
p53-CYPD interactions
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7. Protein structure prediction
• Protein structure prediction is the prediction of the threedimensional structure of a protein from its amino acid sequence
that is, the prediction of its secondary, tertiary, and quaternary
structure from its primary structure.
•
Structure prediction is fundamentally different from the inverse
problem of protein design.
• Protein structure prediction is one of the most important goals
pursued by bioinformatics and theoretical chemistry
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8. Comparative protein modeling
• Comparative protein modeling uses previously solved structures
as starting points, or templates. This is effective because it
appears that although the number of actual proteins is vast,
there is a limited set of tertiary structural motifs to which most
proteins belong. It has been suggested that there are only
around 2,000 distinct protein folds in nature, though there are
many millions of different proteins.
• These methods may also be split into two groups
1) Homology modeling
2) Protein threading
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9. 1)Homology modeling
•
It is based on the reasonable assumption that
two homologous proteins will share very similar structures.
• Because a protein's fold is more evolutionarily conserved than
its amino acid sequence, a target sequence can be modeled with
reasonable accuracy on a very distantly related template,
provided that the relationship between target and template can
be discerned through sequence alignment.
• Unsurprisingly, homology modeling is most accurate when the
target and template have similar sequences.
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10. 2)Protein threading
• Protein threading, also known as fold recognition, is a method of
protein modeling (i.e. computational protein structure
prediction) which is used to model those proteins which have
the same fold as proteins of known structures, but do not
have homologous proteins with known structure.
• It differs from the homology modeling method of structure
prediction as it (protein threading) is used for proteins which do
not have their homologous protein structures deposited in
the Protein Data Bank (PDB), whereas homology modeling is
used for those proteins which do.
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11. •. Homology modeling is for those targets which have homologous
proteins with known structure(usually/maybe of same family),
while protein threading is for those targets with only fold-level
homology found
• Threading works by using statistical knowledge of the relationship
between the structures deposited in the PDB and the sequence of
the protein which one wishes to model.
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12. Macromolecular docking
• Macromolecular docking is the computational modeling of
the quaternary structure of complexes formed by two or more
interacting biological macromolecule
• Protein–ligand docking is a molecular modeling technique. The
goal of protein–ligand docking is to predict the position and
orientation of a ligand (a small molecule) when it is bound to
a protein receptor or enzyme
• Computational capacity has increased dramatically over the last
decade making possible the use of more sophisticated and
computationally intensive methods in computer-assisted drug
design
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13. METHODOLOGY
a) Modeling of CYPD and p53
• No data is available regarding the p53-CYPD interactions and binding
conformations
• To study the interactions, complete structures of p53 and CYPD are
required
• CYPD structure is predicted through Homology Modeling using bovine CYPD
(PDB Id 1IHG) as template (94% identical)
• p53 doesn’t contain templates with 100% sequence coverage. So, structure
is predicted through Threading using I-Tasser
• The modeled proteins were verified using PROCHECK and Ramachandran
plot
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14. METHODOLOGY
b) Docking of CYPD and p53
• Protein-Protein docking of CYPD and p53 modeled structures was carried
out using Hex 6.3 software
• The proteins were docked based on the correlation of shape and
electrostatics
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18. DISCUSSION
• p53 binds close to the binding site of CYPD,
• CYPD-p53 interaction may be inhibited by CYPD inhibitors,
• CsA has been reported to inhibit the CYPD-p53 interaction (Liu J et al,
2008),
• CsA binds at the binding site of CYPD.
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19. RESULTS
CsA bound to CYPD (PDB Id 2Z6W) was modeled in to the complete CYPD protein
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22. CONCLUSIONS
• p53 binds close to the active site of CYPD
• Due to this, CsA is able to inhibit CYPD-p53 interaction
• Therefore, CYPD inhibitors may act in a similar way and inhibit CYPD-p53
interactions
• There are no inhibitors of CYPD apart from its natural inhibitor, CsA
• Thus, design & discovery of novel inhibitors targeting the CYPD binding
site holds great therapeutic promise
• Similar studies can be carried out to check whether the novel CYPD
inhibitors inhibit CYPD-p53 interaction
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23. REFERENCES
•
Vaseva AV, Marchenko ND, Ji K, Tsirka SE, Holzmann S, Moll UM. “p53 opens the mitochondrial
permeability transition pore to trigger necrosis.” Cell, 2012, 149, 1536-1548.
•
Schinzel AC, Takeuchi O, Huang Z, Fisher JK. “Cyclophilin D is a component of mitochondrial
permeability transition and mediates neuronal cell death after focal cerebral ischemia.” Proc Natl Acad Sci
U S A. 2005, 102, 12005-12010.
•
Mihara M, Erster S, Zaika A, Petrenko O. “p53 has a direct apoptogenic role at the mitochondria.” Mol
Cell. 2003, 11, 577-590.
•
Jacobson MP, Friesner RA, Xiang Z, Honig B. "On the Role of Crystal Packing Forces in Determining
Protein Sidechain Conformations." J. Mol. Biol., 2002, 320, 597-608.
•
Roy A, Kucukural A, Zhang Y. “I-TASSER: a unified platform for automated protein structure and function
prediction.” Nat Protoc, 2010 , 5, 725-738.
•
Ritchie DW, Kozakov D, Vajda S. “Accelerating and focusing protein-protein docking correlations using
multi-dimensional rotational FFT generating functions.” Bioinformatics. 2008, 24, 1865-1873.
•
Liu J, St Clair DK, Gu X, Zhao Y. “Blocking mitochondrial permeability transition prevents p53
mitochondrial translocation during skin tumor promotion.” FEBS Lett. 2008, 582, 1319-1324.
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