Neural Networks for Protein Structure PredictionBrown, JMB 1999

CS 466

Saurabh Sinha

Outline

• Goal is to predict “secondary structure” of a protein from its sequence

• Artificial Neural Network used for this task

• Evaluation of prediction accuracy

What is Protein Structure?

http://academ

ic.brooklyn.cuny.edu/biology/bio4fv/page/3d_prot.htm

http://ma

tcmadison.edu/bio

tech/resources/proteins/labManua

l/image

s/220_04_11

4.png

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

What is “secondary structure”?

http://www.wiley.com/college/pratt/0471393878/student/structure/secondary_structure/secondary_structure.gif

“Strand” “Helix”

http://www.npaci.edu/features/00/Mar/protein.jpg

“Strand”

“Helix”

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 !)

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

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

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

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

http://bio.nagaokaut.ac.jp/~mbp-lab/img/hpc.png

hydrophobic core

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

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)

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

The sequence profile looks like this

• Has 20 x M numbers• The numbers are log likelihood of each residue at each position

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

Intro to Neural nets (the second and third steps of

PSIPRED)

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

Artificial Neural Network

• Directed graph

• Nodes or “units” or “neurons”

• Edges between units

• Each edge has a weight (not known a priori)

Layered Architecture

Input here is a four-dimensional vector. Each dimension goesinto one input unit

http://www.akri.org/cognition/images/annet2.gif

Layered Architecturehttp://www.geocomputation.org/2000/GC016/GC016_01.GIF

(units)

What a unit (neuron) does

• Unit i receives a total input xi from the units connected to it, and produces an output yi = fi(xi) where fi() is the “transfer function” of unit i

€

x i = wij y j + wij∈N−{i}

∑

y i = f i(x i) = f i wij y j + wij∈N−{i}

∑ ⎛

⎝ ⎜ ⎜

⎞

⎠ ⎟ ⎟

wi is called the “bias” of the unit

Weights, bias and transfer function

Unit takes n inputsEach input edge has weight wi

Bias bOutput a

Transfer function f()Linear, Sigmoidal, or other

Weights, bias and transfer function

• Weights wij and bias wi 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

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

Back to PSIPRED …

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

15

Input layer Hidden layer

helix

strand

coil

e.g.,

0.18

0.09

0.67

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”

Test of performance

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