Mass Spectrometry

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Mass Spectrometry
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What are mass spectrometers?

• They are analytical tools used to measure the
  molecular weight of a sample.
• Accuracy 0.01 of the total molecular weight
  of a large sample (biomolecule) which is enough
  to identify substitutions, post translational
  modifications.

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Use to BioChemists

• Accurate molecular weight measurements
• Determine sample purity
• Verify amino acid substitutions, post
  translational modifications.
• Amino acids sequencing
• Oligonucleotide structure
• Protein structure determination (protein folding,
  macromolecular structure determination)

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Use in the Industry

• Biotechnology (Analysis of proteins, peptides,
  oligonucleotides)
• Pharmaceuticals (Drug discovery,
  pharmacokinetics, drug metabolism)
• Clinical (neonatal screening, haemoglobin
  analysis)
• Geological (Oil composition)
• Environmental (Water quality, food contamination)

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Mass Spectrometer has

• 3 parts
• Ionization source
• Analyzer
• Detector

http//www.astbury.leeds.ac.uk/Facil/MStut/mstutor
ial.htm
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Matrix-Assisted Laser Desorption/Ionization
(MALDI)
From lectures by Vineet Bafna,UCSD
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Protein to peptide to fragment ion

• Proteins are digested by using a protease like
  Trypsin.
• Trypsin breaks the protein backbone at L and R
  which are basic residues and form positive ions.
• The mass spectrometer further breaks these
  peptides into fragment ions.

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Peptide Cleavage
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Glycan Cleavage
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Mass Spectrum
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Experimental Spectrum
Theoretical Spectrum
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Peptide sequencing problem

• Goal Find a peptide whose theoretical spectrum
  matches the given experimental spectrum the best.

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How can it be done?

• Database search
• De Novo search

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Database search

• Given a experimental spectrum and the parent mass
  of the experimental peptide, find candidate
  peptides with the same parent mass in the
  database that match the experimental peptide the
  best

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De novo Search

• Build a spectrum graph from the masses to create
  the nodes
• Use mass differences to create the edges
• Find the best path

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Database vs De Novo Search

• Database search is very successful in
  identification of already known proteins.
• De novo helps in identification of proteins not
  in the database.
• Database search is not as fast as De novo.
• De novo needs good quality spectra and without
  any modifications to work with.
• De novo is not very accurate.
• Database SEQUEST (Yates et al)
• De Novo PepNovo (Frank and Pevzner)

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Our Project

• Database search tool for Peptide Identification
  from Mass Spectrometry data using a Machine
  Learning approach

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What makes it different from the traditional
search tools?

• Traditional search tools use ad-hoc rules and
  or unified probabilistic models
• Our tool is based on the Machine learning
  approach

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What is Machine Learning?

• An area of artificial intelligence concerned
  with the development of techniques which allow
  computers to learn from data.
• The researcher feeds a set of training examples
  to a computer program that aims to learn the
  connection between features of the examples and a
  specified target concept.

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Examples of Machine learning techniques

• Linear Regression
• Decision Tree learning
• Artificial Neural Networks
• Bayesian Learning
• Analytical Learning
• Reinforcement Learning
• etc.....

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Our Choice....

• Artificial Neural Networks
• Reason?
• Peptide fragmentation is a non-linear problem
  governed by complex rules.

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A brief overview of Neural Networks
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Brain Cells
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From Human Neurons to Artificial Neurons....
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Feed Forward Networks
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Work flow of the project

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Data Preparation

• Protein Samples were isolated from rat brains
• Samples were digested with trypsin and passed
  through LCQ Deca Xp ion-trap mass spectrometer
  and spectra of peptide ions were recorded.
• All spectra were searched against protein
  sequences for Rattus in Swiss-Prot database using
  Mascot

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• Precursor peptides were divided into double and
  triple charged sets.
• Peak intensities were estimated for the following
  ion types
• precursor-H2O
• b, b-H2O, b-NH3, b-H2O-NH3
• y, y-H2O, y-NH3, y-H2O-NH3

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Network Training

• Features for each ion were extracted.
• Target is the peak intensity for each ion.
• Seperate ensembles of two layer feed-forward
  networks were constructed for each ion type and
  trained on the data.

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Prediction of Spectrum

• The predicted fragment spectrum was constructed
  combining the outputs of individual predictors
  for each ion type.
• A blackbox was constructed from the trained
  Neural Network models
• The blackbox when presented with a peptide as
  input will output the predicted spectrum

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Thank You

• Questions?