1. Class 7: Protein Secondary Structure.

2. Protein Structure
Amino-acid chains can fold to form 3-dimensional structures
Proteins are sequences that have (more or less) stable 3-dimensional configuration

3. Why Structure is Important?
The structure a protein takes is crucial for its function
Forms “pockets” that can recognize an enzyme substrate
Situates side chain of specific groups to co-locate to form areas with desired chemical/electrical properties
Creates firm structures such as collagen, keratins, fibroins

4. Determining Structure
X-Ray and NMR methods allow to determine the structure of proteins and protein complexes
These methods are expensive and difficult
Could take several work months to process one proteins
A centralized database (PDB) contains all solved protein structures
XYZ coordinate of atoms within specified precision
~11,000 solved structures

5. Structure is Sequence Dependent
Experiments show that for many proteins, the 3-dimensional structure is a function of the sequence
Force the protein to loose its structure, by introducing agents that change the environment
After sequences put back in water, original conformation/activity is restored
However, for complex proteins, there are cellular processes that “help” in folding

6. Secondary Structure
-helix
-strands

7. a Helix Single protein chain
Shape maintained by intramolecular H bonding between -C=O and H-N-

8. Hydrogen Bonds in -Helixes

9. These sheets hold together by hydrogen bonds across strands
-Strands form Sheets
parallel
Anti-parallel
These sheets hold together by hydrogen bonds across strands

10. Angular Coordinates
Secondary structures force specific angles between residues

11. Ramachandran Plot
We can related angles to types of structures

12. Define "secondary structure"
3D protein coordinates may be converted to a 1D secondary structure representation using DSSP or STRIDE
DSSP
EEEE_SS_EEEE_GGT__EE_E_HHHHHHHHHHHHHHHGG_TT
DSSP= Database of Secondary Structure in Proteins 2

13. DSSP symbols
H = helix backbone angles (-50,-60) and H-bonding pattern (i-> i+4)
E = extended strand backbone angles (-120,+120) with beta-sheet H-bonds (parallel/anti-parallel are not distinguished)
S= beta-bridge (isolated backbone H-bonds)
T=beta-turn (specific sets of angles and 1 i->i+3 H-bond)
G=3-10 helix or turn (i,i+3 H-bonds)
I=Pi-helix (i,i+5 Hbonds) (rare!)
_= unclassified. None-of-the-above. Generic loop, or beta-strand with no regular H-bonding. L 3

14. Prediction of Secondary Structure
Input:
amino-acid sequence
Output:
Annotation sequence of three classes:
alpha
beta
other (sometimes called coil/turn)
Measure of success:
Percentage of residues that were correctly labeled

15. Accuracy of 3-state predictions
True SS: EEEE_SS_EEEE_GGT__EE_E_HHHHHHHHHHHHHHHGG_TT Prediction: EEEELLLLHHHHHHLLLLEEEEEHHHHHHHHHHHHHHHHHHLL
Q3-score = % of 3-state symbols that are correct
Measured on a "test set"
Test set == An independent set of cases (protein) that were not used to train, or in any way derive, the method being tested.
Best methods:
PHD (Burkhard Rost) % Q3
Psi-pred (David T. Jones) % Q3 5

16. Prediction Accuracy Three-state prediction accuracy: Q3 Other measures
Per-segment accuracy(SOV), Mathews’ coefficient

17. What can you do with a secondary structure prediction?
(1) Find out if a homolog of unknown structure is missing any of the SS (secondary structure) units, i.e. a helix or a strand.
(2) Find out whether a helix or strand is extended/shortened in the homolog.
(3) Model a large insertion or terminal domain
(4) Aid tertiary structure prediction 9

18. Statistical Methods
From PDB database, calculate the propensity for a given amino acid to adopt a certain ss-type
Example:
#Ala=2,000, #residues=20,000, #helix=4,000, #Ala in helix=500
P(a,aa) = 500/20,000, p(a) = 4,000/20,000, p(aa) = 2,000/20,000
P = 500 / (4,000/10) = 1.25
Used in Chou-Fasman algorithm (1974)

20. Chou-Fasman: Initiation
Identify regions where 4/6 have a P(H) >1.00 “alpha-helix nucleus”

21. Chou-Fasman: Propagation
Extend helix in both directions until a set of four residues have an average P(H) <1.00.

22. Chou-Fasman Prediction
Predict as -helix segment with
E[P] > 1.03
E[P] > E[P]
Not including proline
Predict as  -strand segment with
E[P] > 1.05
E[P] > E[P]
Others are labeled as turns.
(Various extensions appear in the literature)

23. Achieved accuracy: around 50%
Shortcoming of this method: ignoring the context of the sequence when predicting using amino-acids
We would like to use the sequence context as an input to a classifier
There are many ways to address this.
The most successful to date are based on neural networks

24. A Neuron

25. … Artificial Neuron Input Output a1 a2 akW1 a2 W2 … Wk ak
A neuron is a multiple-input -> single output unit
Wi = weights assigned to inputs; b = internal “bias”
f = output function (linear, sigmoid)

26. Artificial Neural Network
Input
Hidden
Output a1 W1 o1 W2 a2 … … … Wk a3 om
Neurons in hidden layers compute “features” from outputs of previous layers
Output neurons can be interpreted as a classifier

27. Example: Fruit Classifer
Shape Texture Weight Color
Apple ellipse hard heavy red
Orange round soft light yellow

28. Qian-Sejnowski Architecture
... o o oo
Hidden
Input
Output Si
Si-w
Si+w

29. Neural Network Prediction
A neural network defines a function from inputs to outputs
Inputs can be discrete or continuous valued
In this case, the network defines a function from a window of size 2w+1 around a residue to a secondary structure label for it
Structure element determined by max(o, o, oo)

30. Training Neural Networks
By modifying the network weights, we change the function
Training is performed by
Defining an error score for training pairs <input,output>
Performing gradient-descent minimization of the error score
Back-propagation algorithm allows to compute the gradient efficiently
We have to be careful not to overfit training data

31. Smoothing Outputs
The Qian-Sejnowski network assigns each residue a secondary structure by taking max(o, o, oo)
Some sequences of secondary structure are impossible: 
To smooth the output of the network, another layer is applied on top of the three output units for each residue:
Neural network
Markov model

32. Success Rate
Variants of the neural network architecture and other methods achieved accuracy of about 65% on unseen proteins
Depending on the exact choice of training/test sets

33. Breaking the 70% Threshold
A innovation that made a crucial difference uses evolutionary information to improve prediction
Key idea:
Structure is preserved more than sequence
Surviving mutations are not random
Suppose we find homologues (same structure) of the query sequence
The type of replacements at position i during evolution provides us with information about the use of the residue i in the secondary structure

34. Nearest Neighbor Approach
Select a window around the target residues
Perform local alignment to sequences with known structure
Choice of alignment weight matrix to match remote homologies
Alignment weight takes into account the secondary structure of aligned sequence
Use max (na, nb, nc) or max(sa, sb, sc)
Key: Scoring measure of evolutionary similarity.

35. PHD Approach Multi-step procedure:
Perform BLAST search to find local alignments
Remove alignments that are “too close”
Perform multiple alignments of sequences
Construct a profile (PSSM) of amino-acid frequencies at each residue
Use this profile as input to the neural network
A second network performs “smoothing”

36. PHD Architecture

37. Psi-pred : same idea
(Step 1) Run PSI-Blast --> output sequence profile
(Step 2) 15-residue sliding window = 315 weights, multiplied by hidden weights in 1st neural net. Output is 3 weights (1 weight for each state H, E or L) per position.
(Step 3) 45 input weights, multiplied by weights in 2nd neural network, summed. Output is final 3-state prediction.
Performs slightly better than PHD 7

38. Other Classification Methods
Neural Networks were used as a classifier in the described methods.
We can apply the same idea, with other classifiers. E.g.: SVM
Advantages: Effectively avoid overfitting
Supplies prediction confidence

39. SVM based approach Suggested by S. Hua and Z. Sun, (2001).
Multiple sequence alignment from HSSP database (same as PHD)
Sliding window of w  21  w input dimension
Apply SVM with RBF kernel
Multiclass problem:
Training: one-against-others (e.g. H/~H, E/~E, L/~L), binary (e.g. H/E)
maximum output score
Decision tree method
Jury decision method

40. Decision tree H E C E C H C H E H / ~H E / C Yes No E / ~E C/ H Yes No
C / ~C
H / E
Yes No C H E

41. Accuracy on CB513 set Classifier Q3 QH QE QC SOV Max 72.9 74.8 58.6
79.0
75.4
Tree1
68.9
73.5
54.0
73.1
72.1
Tree2
68.2
72.0
61.0
69.0
71.4
Tree3
67.5
69.5
46.6
77.0
70.8 NN
74.7
57.7
77.4
75.0
Vote
70.7
73.0
76.6
73.2
Jury
75.2
60.3
79.5
76.2

42. State of the Art
Both PHD and Nearest neighbor get about 72%-74% accuracy
Both predicted well in CASP2 (1996)
PSI-Pred slightly better (around 76%)
Recent trend: combining classification methods
Best predictions in CASP3 (1998)
Failures:
Long term effects: S-S bonds, parallel strands
Chemical patterns
Wrong prediction at the ends of helices/strands