1. Structural Bioinformatics
Proteins

2. Structure Prediction Motivation
Understand protein function
Locate binding sites
Broaden homology
Detect similar function where sequence differs
(only ~50% remote homologies can be detected based on sequence)
Explain disease
See effect of amino acid changes
Design suitable compensatory drugs

3. 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.”

4. What are Secondary Structures ??
From the structure we can get information about the secondary and tertiary structure of the protein
What are Secondary Structures ??

5. Secondary Structure
Secondary structure is usually divided into three categories:
Anything else – turn/loop
Alpha helix
Beta strand (sheet)

6. Alpha Helix: Pauling (1951)
A consecutive stretch of 5-40 amino acids (average 10).
A right-handed spiral conformation.
3.6 amino acids per turn.
Stabilized by H-bonds
3.6 residues
5.6 Å

7. Beta Strand: Pauling and Corey (1951)
Different polypeptide chains run alongside each
other and are linked together by hydrogen bonds.
Each section is called β -strand,
and consists of 5-10 amino acids.
β -strand

8. 3.47Å
4.6Å
Beta Sheet
The strands become adjacent to each other, forming beta-sheet.
3.25Å
4.6Å
Antiparallel
Parallel

9. Loops Connect the secondary structure elements.
Have various length and shapes.
Located at the surface of the folded protein and therefore may have important role in biological recognition processes.

10. Tertiary Structure
Describes the packing of alpha-helices, beta-sheets and random coils with respect to each other on the level of one whole polypeptide chain

11. How does the structure relate to the primary protein sequence??

12. SEQUENCE
Each protein has a particular 3D structure that determines its function
Early experiments have shown that the sequence of the protein is sufficient to determine its structure
Protein structure is more conserved than protein sequence , and more closely related to function.
Homologous proteins are of the same evolutionary origin. Despite the differences which have been accumulated in their sequences, the structure and function of these proteins can be remarkably conserved.
STRUCTURE
FUNCTION

13. How (CAN) Different Amino Acid Sequence Determine Similar Protein Structure ??
Lesk and Chothia 1980

14. The Globin Family

15. Different sequences can result in similar structures
1ecd
2hhd

16. We can learn about the important features which determine structure and function by comparing the sequences and structures ?

17. The Globin Family

18. Why is Proline 36 conserved in all the globin family ?

19. Where are the gaps??
The gaps in the pairwise alignment are mapped to the loop regions

20. How are remote homologs related in terms of their structure?
retinol-binding
protein
odorant-binding
apolipoprotein D
RBD
b-lactoglobulin

21. PSI-BLAST alignment of RBP and b-lactoglobulin: iteration 3
Score = 159 bits (404), Expect = 1e-38
Identities = 41/170 (24%), Positives = 69/170 (40%), Gaps = 19/170 (11%)
Query: 3 WVWALLLLAAWAAAERD CRVSSFRVKENFDKARFSGTWYAMAKKDPEGLFLQ 54
V L+ LA A S V+ENFD ++ G WY + K
Sbjct: 1 MVTMLMFLATLAGLFTTAKGQNFHLGKCPSPPVQENFDVKKYLGRWYEIEKIPASFE-KG 59
Query: 55 DNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTDTEDPAKFKMKYWGVASFLQ 114
+ I A +S+ E G + K V PAK
Sbjct: 60 NCIQANYSLMENGNIEVLNKELSPDGTMNQVKGE--AKQSNVSEPAKLEVQFFPL
Query: 115 KGNDDHWIVDTDYDTYAVQYSCRLLNLDGTCADSYSFVFSRDPNGLPPEA 164
+WI+ TDY+ YA+ YSC R+P LPPE
Sbjct: 113 MPPAPYWILATDYENYALVYSCTTFFWL--FHVDFFWILGRNPY-LPPET 159

22. The Retinol Binding Protein
b-lactoglobulin

23. So how can we obtain the structure information ???

24. PDB: Protein Data Bank DataBase of molecular structures :
Protein, Nucleic Acids (DNA and RNA),
Structures solved by
X-ray crystallography
NMR
Electron microscopy

25. RCSB PDB – Protein Data Bank

26. How Many Structures ?
March 2008 – Structures

27. Structure Prediction: Motivation
Hundreds of thousands of gene sequences translated to proteins (genbanbk, SW, PIR)
Only about ~40000 solved protein structures
Experimental methods are time consuming and not always possible
Goal: Predict protein structure based
on sequence information

28. 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

29. Secondary Structure Prediction
Given a primary sequence
ADSGHYRFASGFTYKKMNCTEAA
what secondary structure will it adopt ?

30. RBP
RBP (Retinol Binding Protein)
Globin

31. According to the most simplified model:
In a first step, the secondary structure is predicted based on the sequence.
The secondary structure elements are then arranged to produce the tertiary structure, i.e. the structure of a protein chain.
For molecules which are composed of different subunits, the protein chains are arranged to form the quaternary structure.

32. Secondary Structure Prediction Methods
Chou-Fasman / GOR Method
Based on amino acid frequencies
Machine learning methods
PHDsec and PSIpred
HMM (Hidden Markov Model)
Best accuracy nowadays ~80%

33. Chou and Fasman (1974) Success rate of 50%
Name P(a) P(b) P(turn) Alanine
Arginine
Aspartic Acid
Asparagine
Cysteine
Glutamic Acid
Glutamine
Glycine
Histidine
Isoleucine
Leucine
Lysine
Methionine
Phenylalanine
Proline
Serine
Threonine
Tryptophan
Tyrosine
Valine
The propensity of an amino acid to be part of a certain secondary structure (e.g. – Proline has a low propensity of being in an alpha helix or beta sheet  breaker)
Success rate of 50%

34. Secondary Structure Method Improvements
‘Sliding window’ approach
Most alpha helices are ~12 residues long Most beta strands are ~6 residues long
Look at all windows of size 6/12
Calculate a score for each window. If >threshold  predict this is an alpha helix/beta sheet
TGTAGPOLKCHIQWMLPLKK

35. Improvements since 1980’s Adding information from conservation in MSA
Smarter algorithms (e.g. HMM, neural networks).
Success -> 75%-80%

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

37. How does secondary structure prediction work?
Query
SwissProt
Step 1:
Generating a multiple sequence alignment
Query
Subject
Subject
Subject
Subject

38. Steps in secondary structure prediction:
Additional sequences are added using a profile:
A PSI-BLAST PSSM.
A conservation profile (MaxHom).
We end up with a MSA which represents the protein family.
Query
seed
MSA
Query
Subject
Subject
Subject
Subject

39. Steps in secondary structure prediction:
The sequence profile of the protein family is compared (by machine learning methods) to sequences with known secondary structure.
Query
seed
Machine
Learning
Approach
MSA
Known
structures
Query
Subject
Subject
Subject
Subject

40. SS prediction using Neural NetworkF G H I K L M N P Q R S T V W Y .
Sequence
Profile

41. Hidden layer (known ss)
PHDsec Neural Net A C D E F G H I K L M N P Q R S T V W Y .
Output prediction
H= helix
E= strand
C= Coil
Confidence
0=low,9=high
Hidden layer (known ss)

42. HMM TGTAGPOLKCHIQWML p = ? HHHHHHHLLLLBBBBB
HMM enables us to calculate the probability of assigning a sequence of hidden states to the observation
observation
TGTAGPOLKCHIQWML
HHHHHHHLLLLBBBBB
p = ?
Hidden state
(known ss)

43. Beginning with an α-helix
α-helix followed by α-helix
The probability of observing Alanine as part of a β-sheet
The probability of observing a residue which belongs to an α-helix followed by a residue belonging to a turn = 0.15
Table built according to large database of known secondary structures

44. HMM
The above table enables us to calculate the probability of assigning secondary structure to a protein
Example
TGQ
HHH
p = 0.45 x x 0.8 x x 0.8x =

45. 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