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The role of Artificial Neural
Networks in Phage Research
Mike Arnoult
9/30/2010
What is an Artificial Neural Network?
Mathematical and computational model
Motivated by biological neurons
Trained by using features to learn patterns and commonalities
Uses values of its neuron connections to classify an example
Why Apply Artificial Neural Networks to
Phage Research?
The neural network can be trained to recognize features of
phage proteins, and distinguish between them.
I have trained ANNs to recognize and classify phage major
capsid proteins
What is a Bacteriophage?
A virus that infects bacteria
The most common biological entity on earth
A major impact on any environment with Bacteria
A type of virus with a highly unique structure, which
injects its genome into a host, through its tail
A possible alternative to Antibiotics in medicine
How the ANN works:
Why Apply Artificial Neural
Networks to Bioinformatics?
The Neural Network can be trained to recognize features of
proteins, and distinguish between them.
In my research, I will train Neural Networks to recognize
phage major capsid or tail proteins.
What I’ve done so far:
I’ve collected Positive and Negative Data sets from NCBI
Positive data sets included Phage Major Capsid Proteins
and synonyms:
Major Shell Protein
Major Head Protein
Major Coat Protein
Major Procapsid Protein
Major Prohead Protein…
Negative data sets included phage proteins unrelated to
Major capsid proteins
Packaging proteins
Spike proteins
DNA and RNA Polymerase
Assembly proteins
Contractile Sheath proteins
What I’ve done so far:
I have written and used Perl scripts to filter
the Training Data
Any sequences with conspicuously incorrect
GenPept annotations were removed from the
positive data-set.
All sequences with Major Capsid Protein
related annotations were removed from the
negative data-set.
What I’ve done so far:
I’ve turned the sequences into percent
compositions of Amino Acids and
side-chain groups, to Train Neural
Networks
The positive entries are labeled with a
1 and the negative entries are labeled
with a –1.
Using a Matlab Script, a random 20%
of the positive data-set is set aside and
used as a test set against the other
80%.
What I’m doing now:
To find which criteria are best
suited to Training the Neural
Network to recognize Phage Major
Capsid Proteins…
I am training neural networks
using different characteristics of
Amino Acid side-chains (Polar,
Nonpolar, Aromatic, Positive and
Negative)
Adjusting parameters of the way
the Matlab script trains Neural
Networks.
Classification of Known Sequences:
The values are average percentages of correctly classified
sequences, of 1000 separately trained Neural Networks .
Amino Acid and Sidechain Percent
Compositions used as
features
92.9233%
Amino Acid Percent
Compositions used as
features
No Side chains
What I’m going to do Soon:
Test The Neural Networks using other Phage
Major Capsid Proteins
Ramy’s curated Phage Major Capsid Proteins
Eventually verify the Neural Network predictions
in the lab.
THE END