Transcript Introduction of GFP

Supervised By:
Dr.Habib Bokhari (Ass.Professor)
Presented By:
Shazia Sabir (FA04-BSB-005)
• Introduction of GFP.
• Strategy used for Modeling of HriGFP and
HriCFP.
• Tour of Database of GFP.
• Tour of web-Interface of developed
Database & developed tool.
Introduction of GFP
• Existed for more than one hundred and sixty million years.
• Found in one species of jellyfish, Aequorea victoria
(monomeric)
• First time clone in 1992.
• Fluorescent GFP has been expressed in bacteria, yeast, slime
mold, plants, , drosophila, zebrafish, and in mmmalian cells.
• Since 1999, numerous GFP homologues
have been discovered in Anthozoa, Hydrozo species
• GFP as the microscope of the twenty-first century.
• GFP found in Aqurea Victoria is comprised of
238 amino acids with mol.wt of w25 kDa.
• Its wild-type absorbance/ excitation peak is at
395 nm with a minor peak at 475 nm.
• The emission peak is at 508 nm.
• Analysis of a hexapeptide derived by proteolysis
of purified GFP led to the prediction that the
fluorophore originates from an internal Ser-TyrGly sequence.
Florophore
Ref: Fan Yang, Larry G. Moss, and George N. Phillips, Jr. The Molecular Structure of
Green Fluorescent Protein
Applications
Monitoring of gen expression
•The gene encoding a FP is
cloned under the control of
the target promoter, whereby
activity of the promoter can be
monitored by the magnitude
of the fluorescent signal
•split FP->the fluorescent
signal occurs only when both
promoters are active.
Ref: Dmitriy M. Chudakov, Sergey Lukyanov and Konstantin A. Lukyanov,
Fluorescent proteins as a toolkit for in vivo imaging
HriGFP& HriCFP
• Recently discovered proteins in
Hydnophora rigida with emission maxima
of 527nm & 495nm.
• Have high similarity with each other
while have very low similarity with
related members.
• Deletion of only one nucleotide in HriGFP
at 446th position causes the shift in
emission from green to cyan.
Goals of Project
• Structural Modeling of HriGFP and
HriCFP.
• Development of Database of Green
Flourescent Proteins.
• Development of web-Interface of
developed Database.
• Development of Tool for prediction of
Fluorophore in the protein sequence.
Strategies for Modeling of
HriGFP& HriCFP
Introduction of Homology
Modeling
• Predicts the three-dimensional structure of
a given protein sequence (TARGET) based
on an alignment to one or more known
protein structures (TEMPLATES)
• Homology models are of great interest for
planning and analyzing biological
molecules when no experimental three
dimensional structure is available.
Continue….
• The number of structurally characterized
proteins (20,000) is small compared with
the number of known protein sequences
(1,000,000).(Ref Eswar et all, 2003)
• Protein structures are more conserved
than protein sequences, detectable levels
of sequence similarity usually imply
significant structural similarity
Steps In Homology Modeling
Step I & II
i) Three regimes of the sequence-structure
relationship
• The easily detected relationships,
characterized by >30% sequence identity
• The “twilight zone”(with identities 10% to
30%) , corresponds to relationships with
statistically significant sequence similarity
• The “midnight zone” (less than 10%),
corresponds to statistically insignificant
sequence similarity,
Continue….
ii) Fold Recognition Approaches
• Pairwise sequence alignment methods
• Profile-Sequence alignment methods
• Profile-Profile alignment methods
• Sequence-Structure threading methods
iii) Template Selection & Alignment
Step I & II
Fold Assignment & alignment
with template
Ref: Sali, A., and Blundell, T.L. (1993). Comparative protein modelling by satisfaction of
spatial restraints, J. Mol. Biol.
Step III (Model Building)
• i) Modeling by Satisfaction of Spatial
Restraints
• Homology-derived restraints
• Stereochemical restraints
• Optimization of the objective function
• Restraints derived from experimental
data
• Loop Modeling
• Loops play an important role in defining
the functional specificity of a given
protein (e.g. forming active & Binding
site).
• important aspect of comparative modeling
for 30% to 50% sequence identity
Step III (Model Building)
Ref: Sali, A., and Blundell, T.L. (1993). Comparative protein modelling by satisfaction
of spatial restraints, J. Mol. Biol.
Step IV(Predicting Model Errors)
i) Errors in side-chain packing
ii) Distortions and shifts in correctly
aligned regions
iii)Errors in regions without a template
iv) Errors due to misalignments
Ref: http://www.ncbi.nlm.nih.gov/gorf/orfig.cgi
Sequence of Template (1EMA)
with 2D Structure
Ref: http://www.rcsb.org/pdb/explore/explore.do?structureId=1EMA
3D Structure of Template
(1EmA)
Ref:http://www.rcsb.org/pdb/explore
/explore.do?structureId=1EMA
SWISS-MODEL
1. First Approach Mode
- more than 25% seq similarity.
- automatic template selection.
2. Alignment Mode
- less than 25% seq similarity.
- input alignment with template.
3. Project Mode
- input file is deep view project file.
Alignment of HriGFP with
Template
Ref: http://www.ebi.ac.uk/Tools/clustalw2/index.html?
Alignment of HriCFP with
Template
Ref: http://www.ebi.ac.uk/Tools/clustalw2/index.html?
Alignment w.r.t structure
Ref: http://swissmodel.expasy.org/workspace/index.php?func=modelling_align1
Model Generated by SWISSMODEL
MODELLER
•
1.
2.
3.
•
1.
2.
Input Files
Atom file (.pdb, .ent, .atm)
Alignment file (.ali)
Script file (.py)
Output Files
Log file (text file)
Output of Script
Atom file (Input file)
Alignment file (Input file)
Script file (Input file)
Log file (output file)
Alignment file (Output file)
Alignment Generated by
MODELLER for HriGFP
Pap file (Output file)
Alignment Generated by
MODELLER for HriCFP
Pap file (Output file)
Phase III (Model Generation)
Five Models were
generated
1.
2.
3.
4.
5.
DOPE score
DOPE score
DOPE score
DOPE score
DOPE score
:
:
:
:
:
-6536.567383
-6770.957031
-6814.932129
-7199.573242
-6945.205078
DOPE, or Discrete Optimized
Protein Energy, is a statistical
potential used to assess
homology models in protein
structure prediction.
Phase IV (Evaluation of Model)
• The recognition of errors in experimental
and theoretical models of protein
structures is a major problem in structural
biology.
• If this score is outside a range
characteristic for native proteins the
structure probably contains errors.
• A plot of local quality scores points to
problematic parts of the model
GNUPLOT for HriCFP
GNLPLOT for HriCFP along
with Template
Z-Score
(Evaluation Criteria)
• The z-score indicates overall model
quality.
• value is displayed in a plot that contains
the z-scores of all experimentally
determined protein chains in current PDB.
• Can be used to check the input structure is
within the range of scores typically found
for native proteins of similar size.
Z-Score
Ref: https://prosa.services.came.sbg.ac.at
Molecular Interactive View &
Plot Generation
Ref: https://prosa.services.came.sbg.ac.at
Ref: http://laboheme.df.ibilce.unesp.br/cluster/parmodel_mpi/
Ref: http://laboheme.df.ibilce.unesp.br/cluster/parmodel_mpi/
Root Mean Square & Root
Mean Square Deviation
Conclusion
• Both HriGFP and HriCFP are capable of
forming only five beta-sheets that are not
enough for formation of complete beta-can
and hence may not provide full protection
to fluorophore. So these gaps must be
filled in order to give complete protection
to fluorophore.
Future Prospects
• Development of a Tool that would
determine change in fluorescence upon
mutagenesis.
Tour of
• Database
• Web-Interface
• Tool
Thank You!