Proteomics

1
Proteomics

• Dr. Caroline Clower
• Assistant Professor of Chemistry
• Department of Natural Sciences
• Clayton State University
• ITFN 4800
• April 23, 2009

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Bioinformatics
Statistics
Evolutionary Biology
Genetics
Mathematics
Biochemistry/ Molecular Biology
Bioinformatics
Computer Science
Chemistry
Medicine
Physics
3
Bioinformatics vs. Computational Biology

• Used interchangeably
• Actually different terms
• Distinction made by National Institutes of Health
• Bioinformatics
• Refers to the creation and advancement of
  algorithms
• Computational and statistical techniques to solve
  problems arising from management and analysis of
  biological data
• Computational Biology
• Refers to hypothesis-driven investigation of a
  specific problem using computers, using
  experimental or simulated data to advance
  scientific knowledge

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Questions and Answers

• Fundamental questions in science and medicine
• What are the evolutionary origins of this
  protein?
• What gene does this DNA sequence code for?
• What does this gene do?
• How does this enzyme work and what does it look
  like?
• When is this gene expressed?
• What genes are expressed before the onset of
  cancer?
• What drugs can be used to treat this disease?
• What mutations are responsible for this genetic
  disorder?
• Analytical tools
• Experimental (electrophoresis, spectroscopy,
  etc.)
• Computational (programs, databases, internet,
  etc.)
• No single comprehensive database exists for
  accessing all the information needed to manage
  data

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Applications

• Locate mutations responsible for genetic diseases
  and disorders
• Aids in treatment and diagnosis
• Pharmacogenomics
• Designer drugs and therapies
• Biotechnology
• Discover and exploit new enzymes
• Environmental clean-up
• Antibiotics and other chemotherapeutic agents
• Useful products

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The Omics

• Genomics
• DNA Sequence homology locations of genes and
  functional sites phylogeny mapping infer
  function
• Transcriptomics
• mRNA sequence and structure
• Metabolomics
• Proteins and enzymatic pathways involved in cell
  metabolism
• Glycomics
• Carbohydrates of a cell
• Interactomics
• Complex interactions of protein networks in a
  cell
• Nutrigenomics
• Interactions between diet and genes
• Proteomics

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Introduction to Proteomics

• Proteome
• Sum total of an organisms proteins
• Essential to understanding how organisms work
  (more so than genomics)
• Characterization difficult
• Protein structure is complicated
• Chemical modification can occur
• Proteins must be isolated and purified
• Proteins must be crystallized
• Structure predictions (bioinformatics) are
  difficult and often inaccurate

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Proteomics

• Large scale analysis of proteins
• Amino acid sequence and protein structure
• Named in 1995 development began in 1960s
• Macromolecules carry information
• Availability of computers
• Development of algorithms
• Information
• Protein sequences
• Primary sequence (SWISS-PROT and PIR databases)
• Direct submissions - protein sequencing
• Secondary sequence (GenPept and TrEMBL databases)
• Translations - putative proteins resulting from
  modifying (i.e. intron splicing) nucleic acid
  sequence
• Predicted structure (many algorithms)
• Solved structure (Protein Data Bank)

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Protein Data Bank

• Archive of 3D structural data of biological
  macromolecules
• Based on experimental data
• Managed by the Research Collaboratory for
  Structural Bioinformatics (RCSB)
• Rutgers, UCSD, UW-Madison
• As of March 31, 2009 contained 56751 structures

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Applications of Information

• Structure reveals function
• Porins
• Enzymes
• Knowledge of structure and function allow other
  applications
• Pharmacotherapy, etc.

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Protein Structure and Proteomics
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Levels of Protein Structure

• Primary
• Linear sequence of amino acids
• Secondary
• Local structure certain motifs are common
• Tertiary
• Complete 3D shape
• Quaternary
• gt1 peptide chain

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Primary Structure

• Linear sequence of amino acids
• Ala-Glu-Val-Thr-Asp-Pro-Gly or AEVTDPG
• Cannot be predicted by any algorithm
• Experimental
• Protein sequencing
• First protein insulin
• Approach
• Denature protein
• Break into small segments
• Determine sequences of segments
• Animation

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Example
Margaret Dayhoffs FORTRAN programs
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Secondary Structure

• Regular repeating structure
• Helices (coil)
• Sheets (extended zig-zag)
• Turns (short loops)
• Rotation of backbone
• Only some allowed angles
• Ramachandran diagram

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Classification of Proteins by Secondary Structure

• Fibrous
• High composition of single secondary structure
• Strong and flexible
• Collagen (connective tissue, skin, tendons,
  cartilage)
• Triple helix
• Silk fibroin
• b-sheet
• a-Keratin (hair, wool, skin, nails)
• Coiled coil
• b-Keratin (scales, feathers)
• b-sheet

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Classification of Proteins by Secondary Structure

• Globular
• Majority of all proteins
• Contain several types of secondary structure
• Percentage of protein (on average)
• 31 a-helix
• 28 b-sheet
• 13 turns/bends
• 28 loops and random coil
• Compact spherical shapes

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Secondary Structure Prediction

• Based on observed frequencies in known structures
• Chou-Fasman algorithm
• P (a), P (b), P (turn)
• Probability of an AA participating in various
  secondary structures
• Uses a window of 6 residues

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Secondary Structure Prediction

• PELE Results from many different algorithms
• H a helix
• E b strand
• T b turn
• C random coil
• Algorithms listed to the right (initials indicate
  authors)
• JOI Joint prediction
• Assigns the structure using a "winner takes all"
  procedure

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Tertiary Structure

• 3D structure (overall shape) of an entire
  polypeptide
• Interactions between secondary structural
  components
• No algorithm predicts the 3D shape with high
  accuracy
• Can predict cellular location and sequence motifs
• Experimentally determined by
• X-ray crystallography (85 of structures in the
  PDB)
• 2D Nuclear Magnetic Resonance (15)
• Computational modeling/Homology modeling (lt1)

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X-ray crystallography

• Precise positions of atoms
• Mathematical analysis of film
• Fourier transform
• Creates electron density map
• Combine with principles of chemical bonding to
  create structure

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2D-NMR NOESY

• Graphically displays atoms in close proximity
• Calculates structure

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Computational Modeling

• Sequence alignment to find homologous protein
• BLAST algorithm
• Searches for maximal local alignments
• Inserts gaps to optimize alignment
• Breaks sequence down into subsequences (words)
• Searches for those words in database
• Extends sequence on either side of word to look
  for certain score (threshold)

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Scoring Alignments

• Scoring matrices based on similarity and
  probability of substitutions/mutations
• Example
• Alignment of mouse and crayfish trypsin
• Raw score 30
• Evaluate alignment with
• Alignment score (S)
• E score expected number of sequences that have
  scores S that would be found randomly
• Low value for E indicates search result is not
  random and sequences are likely to be related

Mouse I V G G Y N C E E N S V P Y
Q 5 4 5 5 -3 2 -2 2 3 0 0 -1 6
10 4 Crayfish I V G G T D A V L G E
F P Y Q
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Protein Modeling Programs

• Investigate and manipulate structure
• Structure overlays and alignment
• Pharmacophores
• Drug design from protein structure

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Websites of Interest

• ExPASy proteomics tools (http//us.expasy.org/tool
  s/)
• NCBI (http//www.ncbi.nlm.nih.gov/)
• PDB (www.pdb.org)
• Protein Explorer (www.proteinexplorer.org)