Transcript Structure Prediction

An Introduction to Bioinformatics
Protein Structure Prediction
Aims
• Understand the use of algorithms
• Recognize different approaches
• Understand the limitations
Objectives
• Predict occurrence of aspects of structure
• To select appropriate tools
Introduction
• Structure has several levels
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1 primary
2 secondary
3 tertiary
4 quaternary
1 primary
• Amino acid sequence
NH2-MRLSWYDPDFQARLTRSNSKCQGQLEV YLKDGWHMVC
SQSWGRSSKQWEDPSQASKVCQRLNCGVPLSLGPFLVTYTP
QSSIICYGQLGSFSNCSHSRNDMCHSLGLTCLE-COOH
2 secondary
• Localized organisation -helices and sheets
3 tertiary
Three-dimensional
organisation
4 quaternary
Multi protein
assembly
The problem…..
• The best way is by X-ray crystallography or NMR etc…
• Structure databases only hold about 10,000 + structures
• Therefore devise programs to deduce structural solutions
• Complex!
Secondary Structure prediction
• Signal peptides
• Intracellular targeting
•Trans-membrane -helices
• -helices and -sheets
•Super-secondary structure (motifs)
Signal peptides
• Short N-terminal amino acid sequences
• Direct to membrane
• Cleaved after translocation
• SignalP
– Nobel Prize 1999 Günter Blobel
SignalP predicts
signal peptide
cleavage sites
Only first 50-70 
Using neural
networks
Is the sequence a signal peptide?
# Measure Position Value Cutoff Conclusion
max. C
25
0.910 0.37
YES
max. Y
25
0.861 0.34
YES
max. S
12
0.960 0.88
YES
mean S
1-24 0.892 0.48
YES
# Most likely cleavage site between pos. 24 and 25: SRA-LE
Intracellular targeting
• TargetP
• Predict subcellular location of eukaryotic
protein
• Presequences
– Chloroplasts
– Mitochondria
– signal peptide
Transmembrane Domains
• Lots of programs
• TMHMM
-helices
hydrophobic  
helix topology
R or K +ve charge
cytoplasmic side
– Hidden Markov Modelling
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Paste as FASTA file
e.g Serotonin Receptor
Predicts the transmembrane
domains and orientation
-helices and -sheets
• GOR algorithim
• Assigns each residue
to one conformational
state of -helix,
extended chain, reverse
turn or coil
• 64.4% accurate
• Many other sites
• most use multiple
alignments
-helices and sheets
10
20
30
40
50
60
70
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MKFSWRTALLWSLPLLVVGFFFWQGSFGGADANLGSNTANTRMTYGRFLEYVDAGRITSVDLYENGRTAI
cccceeeeeecccceeeeeeeeccccccccccccccccccchhhhcceeeeccccceeeeeeccccceee
VQVSDPEVDRTLRSRVDLPTNAPELIARLRDSNIRLDSHPVRNNGMVWGFVGNLIFPVLLIASLFFLFRR
eeccccccchhhhccccccccchhhhhhhhhccccccccceecccceeeeecccccchhhhhhhhheeec
SSNMPGGPGQAMNFGKSKARFQMDAKTGVMFDDVAGIDEAKEELQEVVTFLKQPERFTAVGAKIPKGVLL
cccccccccchhhhcchhhhhhhhccceeeecchhhhhhhhhhhhhhhhhhcccchhhhhcccccceeee
VGPPGTGKTLLAKAIAGEAGVPFFSISGSEFVEMFVGVGASRVRDLFKKAKENAPCLIFIDEIDAVGRQR
ecccccchhhhhhhhhcccccceeecccccceeeeeecccchhhhhhhhhcccccceeeecchhhhcccc
GAGIGGGNDEREQTLNQLLTEMDGFEGNTGIIIIAATNRPDVLDSALMRPGRFDRQVMVDAPDYSGRKEI
ccccccccchhhhhhhhhhhhhcccccccceeeeeeccccchhhhhhccccccceeeeecccccccchhh
LEVHARNKKLAPEVSIDSIARRTPGFSGADLANLLNEAAILTARRRKSAITLLEIDDAVDRVVAGMEGTP
hhhhhhhhccccccchhhhccccccccchhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhheeecccccc
LVDSKSKRLIAYHEVGHAIVGTLLKDHDPVQKVTLIPRGQAQGLTWFTPNEEQGLTTKAQLMARIAGAMG
cccccccchhhhhcccceeeeeecccccccceeeecccccccceeccccccccchhhhhhhhhhhhhhhh
GRAAEEEVFGDDEVTTGAGGDLQQVTEMARQMVTRFGMSNLGPISLESSGGEVFLGGGLMNRSEYSEEVA
hhhhhhhcccccceeeccccchhhhhhhhhhhhhhhccccccccccccccceeeecccccccccchhhhh
TRIDAQVRQLAEQGHQMARKIVQEQREVVDRLVDLLIEKETIDGEEFRQIVAEYAEVPVKEQLIPQL
hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhcccccchhhhhhhhhhcccccccccccc
Super-secondary Structure
• Secondary structure elements
combined into specific geometric
arrangements known as motifs
Beta corner
Super-secondary Structure
Several programs/websites for specific
domains e.g.
• PAIRCOIL and MULTICOIL - detect coiledcoiled regions
– regions separating domains
• TRESPASSER - detects Leucine Zippers
– Leu-X6-Leu-X6-Leu-X6-Leu protein interaction
domain
• NPS@nalysis Helix-Turn-Helix
– Protein interaction/DNA binding
Integrated stucture prediction
• One stop shop!
• Predict Protein at EBI
– secondary structure
– solvent accessibility
globular regions
– transmembrane helices
coiled-coil regions
– a multiple sequence alignment
ProSite sequence motifs
– low-complexity retions
– ProDom domain assignments
Tertiary Structure Prediction
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Homology modelling
Fold recognition
Threading
Model building
Protein sequence
(primary structure)
Database searching
for homologues
No homologue of
known structure
Homologue of
known structure
Fold prediction,
ab initio methods etc.
Comparative
modelling
3D-structure
Homology Modelling
• Method of choice following BLAST
search
• SWISS
Model is a
good WWW
Interface
URL: http://www.expasy.ch/swissmod/SWISS-MODEL.html
Homology Modelling
• Requires at least one sequence of known 3D-structure
with significant similarity to the target sequence.
• Compare the target sequence with database - FastA and
BLAST.
• Sequences with a FastA score 10.0 standard deviations
above the mean of the random scores or a P(N) lower than
10-5 (BLAST) considered for the model building
• Restrict to those which share at least 30% residue
identity
Homology Modelling
• Framework construction
– compare atom positions - Cs
• Build non-conserved loops
• Complete backbone - add other atoms
• Add side chains
• Refine
Insulin like gene from C.elegans
Red = Insulin
Blue = ILGF1
What if I have no homologue?
Ab initio methods - Threading
• Sequence of unknown structure
• Thread through a through a sequence of known
structure
• Move query sequence through residue by resudue
and compare computationally
– include thermodynamic criteria, solvent accessibility,
secondary structure information
• Computing intensive
http://www.cs.bgu.ac.il/~bioinbgu/form.html