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Secondary structure prediction
Lalit Samant
Neural Network Method
•Prediction is done by utilizing the
information of different
DATABASE
•Linear sequence  3D structure of
Polypeptide
Neural network
Input signals are summed
andturned into zero or one
3.
Feed-forward multilayernetwork
Input layer Hidden layer Output
layer
J1 J2 J3 J4
neurons
NeuralNetworks
• Neural networks are rather trained then programmed to carry out
chosen information processing tasks
• Training neural network involves adjusting the network so that is
able to produce specific output for each of given set of input
patterns
• Since the desired data are known in advance, training a feed
forward network is a supervised learning.
• Back propagation algorithm – Each error in recognition on output
effects with reaction of back correction in parameters of activation
function.
Training a feed forwardnet
• Training was performed using SNNS (Stuttgart Neural Network
System) package
• Network architecture and weights were exported using ssns2c
program from SNNS package
• Own Perl programs was used to preparing data and
benchmarking network
Neuralnetwork
Input signals are summed
andturned into zero or one
3.
Feed-forward multilayernetwork
Input layer Hidden layer Output
layer
J1 J2 J3 J4
neurons
Entersequences
Compare Prediction to Reality
AdjustWeights
Neural networktraining
Algorithm
• A binary encoding scheme is used for network input. In this
scheme each amino acid at each window position is encoded
by a group of 21 inputs,
• one for each possible amino acid type at that position and
one to provide a null input used when the moving window
overlaps the amino- or carboxyl-terminal end of the protein.
• In each group of 21 inputs, the input corresponding to the amino
acid type at that window position is set to 1 and all other inputs are
set to 0.
• Thus, the input layer consists of 17 groups of 21 inputs each and
for any given 17 amino acid window, 17 network inputs are set to 1
and the rest are set to 0.
• The hidden layer consists of two units. The output layer also
consists of two units. Secondary structure is encoded in these output
units as follows: (1,0) = helix, (0,1) = sheet, and (0,0) = coil. Actual
computed output values are in the range 0.0-1.0
Network Architecture
Input Hidden Output Q3
300 20 3 71.277
260 20 3 72.742
220 20 3 73.428
180 20 3 70.083
Q3 – Corelation Coeficient -
Percentage of correctly predicted
residues
MSE = Mean Square Errror.
2 21
n
k k
i 1MSE(t)  f (x t)   pi (xi t)
i1

i1
MSE
Epoch
220
300
260
180
Network Architecture
Input Hidden Output Q3
220 40 3 71.567
220 30 3 71.466
220 20 3 73.428
220 10 3 70.345
20
40
30
10
Strategies to increaseaccuracy
• Adding new types of biological information
• Change the way that information is presented to the network
• Post process the network predictions
• Change the network architecture
Strategies to increaseaccuracy
Biological Information:
•Hydrophobicity, charge, backbone
properties
•Length of chain – additional input
•Distance to N & C terminal aa
•Non local information. (all alpha, all beta
etc.)
Secondary structure prediction
Secondary structure prediction
Secondary structure prediction
Secondary structure prediction
Secondary structure prediction
Secondary structure prediction

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Secondary structure prediction

  • 2. Neural Network Method •Prediction is done by utilizing the information of different DATABASE •Linear sequence  3D structure of Polypeptide
  • 3. Neural network Input signals are summed andturned into zero or one 3. Feed-forward multilayernetwork Input layer Hidden layer Output layer J1 J2 J3 J4 neurons
  • 4. NeuralNetworks • Neural networks are rather trained then programmed to carry out chosen information processing tasks • Training neural network involves adjusting the network so that is able to produce specific output for each of given set of input patterns • Since the desired data are known in advance, training a feed forward network is a supervised learning. • Back propagation algorithm – Each error in recognition on output effects with reaction of back correction in parameters of activation function.
  • 5. Training a feed forwardnet • Training was performed using SNNS (Stuttgart Neural Network System) package • Network architecture and weights were exported using ssns2c program from SNNS package • Own Perl programs was used to preparing data and benchmarking network
  • 6. Neuralnetwork Input signals are summed andturned into zero or one 3. Feed-forward multilayernetwork Input layer Hidden layer Output layer J1 J2 J3 J4 neurons
  • 7. Entersequences Compare Prediction to Reality AdjustWeights Neural networktraining
  • 8. Algorithm • A binary encoding scheme is used for network input. In this scheme each amino acid at each window position is encoded by a group of 21 inputs, • one for each possible amino acid type at that position and one to provide a null input used when the moving window overlaps the amino- or carboxyl-terminal end of the protein.
  • 9. • In each group of 21 inputs, the input corresponding to the amino acid type at that window position is set to 1 and all other inputs are set to 0. • Thus, the input layer consists of 17 groups of 21 inputs each and for any given 17 amino acid window, 17 network inputs are set to 1 and the rest are set to 0.
  • 10. • The hidden layer consists of two units. The output layer also consists of two units. Secondary structure is encoded in these output units as follows: (1,0) = helix, (0,1) = sheet, and (0,0) = coil. Actual computed output values are in the range 0.0-1.0
  • 11. Network Architecture Input Hidden Output Q3 300 20 3 71.277 260 20 3 72.742 220 20 3 73.428 180 20 3 70.083 Q3 – Corelation Coeficient - Percentage of correctly predicted residues MSE = Mean Square Errror. 2 21 n k k i 1MSE(t)  f (x t)   pi (xi t) i1  i1 MSE Epoch 220 300 260 180
  • 12. Network Architecture Input Hidden Output Q3 220 40 3 71.567 220 30 3 71.466 220 20 3 73.428 220 10 3 70.345 20 40 30 10
  • 13. Strategies to increaseaccuracy • Adding new types of biological information • Change the way that information is presented to the network • Post process the network predictions • Change the network architecture
  • 14. Strategies to increaseaccuracy Biological Information: •Hydrophobicity, charge, backbone properties •Length of chain – additional input •Distance to N & C terminal aa •Non local information. (all alpha, all beta etc.)