1. Proteins Secondary Structure Predictions

3. Specific databases of protein sequences and structures
Swissprot
PIR
TREMBL (translated from DNA)
PDB (Three Dimensional Structures)

4. Protein Structure Primary Secondary Tertiary Quaternary Amino acid
sequence
Alpha helices &
Beta sheets,
loops.
Packing of
secondary
elements.
Packing of several
polypeptide chains

5. Symbols for the 20 amino acids
A ala alanine M met methionine
C cys cysteine N asn aspargine
D asp aspartic acid P pro proline
E glu glutamic acid Q gln glutamine
F phe phenylalanine R arg arginine
G gly glycine S ser serine
H his histidine T thr threonine
I ile isoleucine V val valine
K lys lysine W trp tryptophane
L leu leucine Y tyr tyrosine

6. The 20 Amino Acids

7. Grouping amino acids to physio-chemical properties

8. Myoglobin – the first high resolution protein structure
Solved in 1958 by Max Perutz John Kendrew of Cambridge University.
Won the 1962 and Nobel Prize in Chemistry.
“ Perhaps the most remarkable features of the molecule are its complexity and its lack of symmetry. The arrangement seems to be almost totally lacking in the kind of regularities which one instinctively anticipates.”

9. Alpha Helices Right-handed spiral 5 to 40 amino acids (10 average)
3.6 amino acids per turn
Some a.a. are more frequent than others in helices.

10. Beta Sheets Parallel – Strands run in the same direction (C to N)
Anti-parallel- Strands run in opposite directions
Each strand has 5-10 amino acids (6 average)
Some a.a. are more frequent than others N C C C C N

11. Loop Regions All other protein regions Irregular shape and size
Connect the secondary structure elements

12. Structure Presentation
Ribbon diagram:
Alpha helix
Beta Sheet

13. Structure Presentation
TOPS cartoon:
beta sheets are triangles
alpha helices are circles.
the peptide chain runs from N terminus to C terminus.

14. Structure Prediction: Motivation
Hundreds of thousands of gene sequences translated to proteins (genbanbk, SW, PIR)
Only about solved structures (PDB)
Goal: Predict protein structure based
on sequence information

15. Structure Prediction: Motivation
Understand protein function
Locate binding sites
Broaden homology
Detect similar function where sequence differs
Explain disease
See effect of amino acid changes
Design suitable compensatory drugs

16. Prediction Approaches
Primary (sequence) to secondary structure
Sequence characteristics
Secondary to tertiary structure
Fold recognition
Threading against known structures
Primary to tertiary structure
Ab initio modelling

17. Can we predict the secondary structure from sequence ?
a-helix
b-sheet
non-
polar
polar
polar
polar
Non-polar
Secondary structures have an amphiphilic nature : one face polar and the other non polar

18. Secondary Structure Prediction
Why is it complex?
A huge space of possible structures
Assume a 100 aa chain
only 2 possible conformations for each residue
2100~1030 different conformations for the chain as a whole.
Infer secondary structure from sequence is problematic:
Similar sequences may result in different structures (mutations, different environments).
Different sequences may result in similar structures (the Globin fold).

19. Secondary Structure Prediction Methods
Chou-Fasman / GOR Method
Based on amino acid frequencies
No more than 60% accurate
Artificial Neural Network (ANN) methods
PHDsec and PSIpred
Use multiple sequences
Secondary structure based on family
Best accuracy now ~78%

20. PHDsec and PSIpred PHDsec PSIpred
Rost & Sander, 1993
Based on sequence family alignments
PSIpred
Jones, 1999
Based on Position Specific Scoring Matrix
Generated by PSI-BLAST
Both consider long-range interactions

21. Brain Neurons Outgoing signal determined by incoming
Connected together in networks
Learns from experience

22. SS prediction using ANNF G H I K L M N P Q R S T V W Y .
Inputs for one position
Amino acid at position

23. Position-Specific Scoring Matrix

24. Inputs for one position
PHDsec Neural Net A C D E F G H I K L M N P Q R S T V W Y .
Inputs for one position
Amino acid at position
Outputs
H= helix
E= strand
C= Coil
Confidence
0=low,9=high
Hidden layer

25. Secondary structure prediction
AGADIR - An algorithm to predict the helical content of peptides
APSSP - Advanced Protein Secondary Structure Prediction Server
GOR - Garnier et al, 1996
HNN - Hierarchical Neural Network method (Guermeur, 1997)
Jpred - A consensus method for protein secondary structure prediction at University of Dundee
JUFO - Protein secondary structure prediction from sequence (neural network)
nnPredict - University of California at San Francisco (UCSF)
PredictProtein - PHDsec, PHDacc, PHDhtm, PHDtopology, PHDthreader, MaxHom, EvalSec from Columbia University
Prof - Cascaded Multiple Classifiers for Secondary Structure Prediction
PSA - BioMolecular Engineering Research Center (BMERC) / Boston
PSIpred - Various protein structure prediction methods at Brunel University
SOPMA - Geourjon and Delיage, 1995
SSpro - Secondary structure prediction using bidirectional recurrent neural networks at University of California
DLP - Domain linker prediction at RIKEN