Neural Networks for Protein Structure Prediction Brown, JMB CS 466 Saurabh Sinha

Transcription

1 Neural Networks for Protein Structure Prediction Brown, JMB 1999 CS 466 Saurabh Sinha

2 Outline Goal is to predict secondary structure of a protein from its sequence Artificial Neural Network used for this task Evaluation of prediction accuracy

3 What is Protein Structure?

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6 Protein Structure An amino acid sequence folds into a complex 3-D structure Finding out this 3-D structure is a crucial and challenging task Experimental methods (e.g., X-ray crystallography) are very tedious Computational predictions are a possibility, but very difficult

7 What is secondary structure?

8 Strand Helix

9 Helix Strand

10 Secondary structure prediction Well, the whole 3-D tertiary protein structure may be hard to predict from sequence But can we at least predict the secondary structural elements such as strand, helix or coil? This is what this paper does.. and so do many other papers (it is a hard problem!)

11 A survey of structure prediction The most reliable technique is comparative modeling Find a protein P whose amino acid sequence is very similar to your target protein T Hope that this other protein P does have a known structure Predict a similar structure similar to that of P, after carefully considering how the sequences of P and T differ

12 A survey of structure prediction Comparative modeling fails if we don t have a suitable homologous template protein P for our protein T Ab initio tertiary methods attempt to predict the structure without using a protein structure Incorporate basic physical and chemical principles into the structure calculation Gets very hairy, and highly computationally intensive The other option is prediction of secondary structure only (i.e., making the goal more modest) These may be used to provide constraints for tertiary structure prediction

13 Secondary structure prediction Early methods were based on stereochemical principles Later methods realized that we can do better if we use not only the one sequence T (our sequence), but also a family of related sequences Search for sequences similar to T, build a multiple alignment of these, and predict secondary structure from the multiple alignment of sequence

14 What s multiple alignment doing here? Most conserved regions of a protein sequence are either functionally important or buried in the protein core More variable regions are usually on surface of the protein, there are few constraints on what type of amino acids have to be here (apart from bias towards hydrophilic residues) Multiple alignment tells us which portions are conserved and which are not

15 hydrophobic core

16 What s multiple alignment doing here? Therefore, by looking at multiple alignment, we could predict which residues are in the core of the protein and which are on the surface ( solvent accessibility ) Secondary structure then predicted by comparing the accessibility patterns associated with helices, strands etc. This approach (Benner & Gerloff) mostly manual Today s paper suggest an automated method

17 The PSI-PRED algorithm Given an amino-acid sequence, predict secondary structure elements in the protein Three stages: 1. Generation of a sequence profile (the multiple alignment step) 2. Prediction of an initial secondary structure (the neural network step) 3. Filtering of the predicted structure (another neural network step)

18 Generation of sequence profile A BLAST-like program called PSI-BLAST used for this step We saw BLAST earlier -- it is a fast way to find high scoring local alignments PSI-BLAST is an iterative approach an initial scan of a protein database using the target sequence T align all matching sequences to construct a sequence profile scan the database using this new profile Can also pick out and align distantly related protein sequences for our target sequence T

19 The sequence profile looks like this Has 20 x M numbers The numbers are log likelihood of each residue at each position

20 Preparing for the second step Feed the sequence profile to an artificial neural network But before feeding, do a simply scaling to bring the numbers to 0-1 scale x " 1 1+ e #x

21 Intro to Neural nets (the second and third steps of PSIPRED)

22 Artificial Neural Network Supervised learning algorithm Training examples. Each example has a label class of the example, e.g., positive or negative helix, strand, or coil Learns how to predict the class of an example

23 Artificial Neural Network Directed graph Nodes or units or neurons Edges between units Each edge has a weight (not known a priori)

24 Layered Architecture Input here is a four-dimensional vector. Each dimension goes into one input unit

25 Layered Architecture (units)

26 What a unit (neuron) does Unit i receives a total input x i from the units connected to it, and produces an output y i = f i (x i ) where f i () is the transfer function of unit i x i = $ j "N#{i} w ij y j + w i % y i = f i (x i ) = f i ' & $ j "N#{i} ( w ij y j + w i * ) w i is called the bias of the unit

27 Weights, bias and transfer function Unit takes n inputs Each input edge has weight w i Bias b Output a Transfer function f() Linear, Sigmoidal, or other

28 Weights, bias and transfer function Weights w ij and bias w i of each unit are parameters of the ANN. Parameter values are learned from input data Transfer function is usually the same for every unit in the same layer Graphical architecture (connectivity) is decided by you. Could use fully connected architecture: all units in one layer connect to all units in next layer

29 Where s the algorithm? It s in the training of parameters! Given several examples and their labels: the training data Search for parameter values such that output units make correct predictions on the training examples Back-propagation algorithm Read up more on neural nets if you are interested

30 Back to PSIPRED

31 Step 2 Feed the sequence profile to the input layer of an ANN Not the whole profile, only a window of 15 consecutive positions For each position, there are 20 numbers in the profile (one for each amino acid) Therefore ~ 15 x 20 = 300 numbers fed Therefore, ~ 300 input units in ANN 3 output units, for strand, helix, coil each number is confidence in that secondary structure for the central position in the window of 15

32 e.g., 15 helix strand coil Input layer Hidden layer

33 Step 3 Feed the output of 1st ANN to the 2nd ANN Each window of 15 positions gave 3 numbers from the 1st ANN Take 15 successive windows outputs and feed them to 2nd ANN Therefore, ~ 15 x 3 = 45 input units in ANN 3 output units, for strand, helix, coil

34 Test of performance

35 Cross-validation Partition the training data into training set (two thirds of the examples) and test set (remaining one third) Train PSIPRED on training set, test predictions and compare with known answers on test set. What is an answer? For each position of sequence, a prediction of what secondary structure that position is involved in That is, a sequence over H/S/C (helix/strand/coil) How to compare answer with known answer? Number of positions that match