Lecture 6.3: From DNA to Protein

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Lecture 6.3 From DNA to Protein

• Dr. Joanne Fox
• Day 6 Saturday February 21st, 2004
• 1345 1515pm

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From DNA to Protein
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Objectives

• Review protein sequence features and databases
• Review the structural diversity of amino acids
  and protein sequences
• Highlight several physiochemical and structural
  features which can be calculated from protein
  sequences
• Show how proteomics utilizes methods and
  techniques for measuring, comparing and assessing
  protein features

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Outline

• Protein sequence features
• Databases of protein sequences
• Basics of protein structure
• 1o structure, prediction of Mw and pI
• 2o structure, prediction methods
• 3o structure, methods for predicting folds
• Proteomics
• Current methods
• Cutting edge technology

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Amino Acids
amino group
alpha carbon

• The general formula for an amino acid
• R is commonly one of 20 different side chains
• At pH 7 both the amino and carboxyl groups are
  ionized

carboxyl group
side chain group
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Peptide Bonds

• Amino acids are joined together by an amide
  linkage called a peptide bond.
• The two bonds on either side of the rigid planar
  peptide unit exhibit a high degree rotation

peptide bonds
rotation occurs here
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Families of Amino Acids

• The common amino acids are grouped according to
  whether their side chains are
• acidic D, E
• basic K, R, H
• uncharged polar N, Q, S, T, Y
• nonpolar G, A, V, L, I, P, F, M, W, C
• Hydrophilic amino acids (uncharged polar) are
  usually on the outside of a protein whereas
  nonpolar residues cluster on the inside of
  protein
• Basic or acidic amino acids are very polar and
  are generally found on the outside of protein
  molecules

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Protein Sequence Features

• Proteins exhibit far more sequence and chemical
  complexity than DNA or RNA
• Properties and structure are defined by the
  sequence and side chains of their constituent
  amino acids
• The engines of life
• gt95 of all drugs target proteins
• Favorite topic of post-genomic era

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Protein Sequence Databases

• Where does protein sequence information reside?
• Entrez Cross Database Search
• http//www.ncbi.nlm.nih.gov/gquery/gquery.fcgi
• Swissprot TrEMBL
• http//ca.expasy.org/sprot/
• PIR
• http//pir.georgetown.edu/
• As of December 2003, all of this information is
  integrated into unified protein database called
  Uniprot.
• Uniprot
• http//www.pir.uniprot.org/

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Entrez Cross Database Search

• Protein sequence database gives access to
  translated protein sequences from
  Genbank/EMBL/DDBJ
• Complete set of deduced protein sequences
• Redundancy problem

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Swissprot TrEMBL

• Swissprot is an expert curated database
• Function, domain structure, post-translational
  modifications, variants, reactions, similarities
• TrEMBL (translated EMBL)
• Computer annotated supplement to Swissprot

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PIR Protein Information Resource

• Annotated database which includes protein family
  classification information

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The Uniprot Knowledgebase

• Contains all of the information in Swiss-Prot,
  TrEMBL, and PIR. This new unified database was
  launched in December 2003.

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Basics of Protein Structure

• Primary
• Secondary
• Tertiary

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Molecular Weight

• Quick formula 110 X number of residues
• Accurate determination of mass by mass
  spectrometry
• Tools exist for accurately calculating mass of
  peptides based on amino acid composition

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Molecular Weight Proteomics
2-D Gel QTOF Mass Spectrometry
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Isoelectric Point

• The pH at which a protein has a net charge0

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Basics of Protein Structure

• Primary
• Secondary
• Tertiary

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Common Secondary Structure Elements

• The Alpha Helix

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Common Secondary Structure Elements

• The Beta Sheet

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Secondary StructurePhi Psi Angles Defined

• Rotational constraints emerge from interactions
  with bulky groups (ie. side chains).
• Phi Psi angles define the secondary structure
  adopted by a protein.

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Ramachandran Plot
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Supersecondary Structure
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Secondary Structure Protein Folding

• Understanding the forces of hydrophobicity

Hydrogen bonds can form with polar side
chains on outside of the protein
nonpolar side chains
polar side chains
hydrophobic core contains nonpolar side chains
unfolded or partially folded polypeptide
folded conformation
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Hydrophobicity is a property which can be
calculated for protein sequences

• Hydrophobicity Scales
• Used to calculate hydrophobicity
• Based on experimental evidence indicating
  hydrophobic/hydrophilic properties of each aa
• Solubility, Stability, Location and/or
  Globularity of protein sequences can be predicted

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Hydrophobicity Profile

• Moving segment approach
• Correlation of this technique with 3D structure

exterior
interior residues
hydrophobic hydrophilic -
score
NH2 protein sequence
COOH
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The a-helix is a common secondary structure
element
acidic

• A helical wheel is a representation of the 3D
  structure of the a-helix.
• Projection of aa side chains onto a plane
  perpendicular to axis of helix
• Hydrophobic arcs stabilize helical interactions
• Amphipathic helices are common

nonpolar
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Secondary Structure Prediction

• The presence of secondary structure elements can
  be predicted.
• Current algorithms rely on
• statistics (Chou-Fasman, GOR)
• homology or nearest neighbor comparisons (Levin)
• physico-chemical properties (Lim, Eisenberg)
• pattern matching (Cohen, Rooman)
• neural networks (Qian Sejnowski, Karplus)
• evolutionary methods (Barton, Niemann)
• and combined approaches (Rost, Levin, Argos)

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Chou-Fasman Algorithm

• Assign each residue a Pa, Pb, Pc value
• Take a window of 7 residues and calculate a
  window-averaged value for all Pa, Pb, Pc
• Assign the average value for each of the
  secondary structures to the middle residue
• Move down one residue and repeat steps 2 thru 3
  until finished
• Scan and assign SS to the highest P/residue

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Chou-Fasman Statistics
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The PhD Approach
PRFILE...
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The PhD Algorithm

• Search the SWISS-PROT database and select high
  scoring homologues
• Create a sequence profile from the resulting
  multiple alignment
• Include global sequence info in the profile
• Input the profile into a trained two-layer neural
  network to predict the structure and to
  clean-up the prediction

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Predicting via Neural Nets PSSM

• PHDhtm
• http//www.embl-heidelberg.de/predictprotein/
• TMAP
• http//www.mbb.ki.se/tmap/index.html
• TMPred
• http//www.ch.embnet.org/software/TMPRED_form.html

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Prediction Performance
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Best of the Best

• PredictProtein-PHD (72)
• http//cubic.bioc.columbia.edu/predictprotein
• Jpred (73-75)
• http//www.compbio.dundee.ac.uk/www-jpred/
• PREDATOR (75)
• http//www.hgmp.mrc.ac.uk/Registered/Option/predat
  or.html
• PSIpred (77)
• http//bioinf.cs.ucl.ac.uk/psipred/

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Basics of Protein Structure

• Primary
• Secondary
• Tertiary

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Tertiary Structure
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Protein Structure Databases

• Where does protein structural information reside?
• PDB
• http//www.rcsb.org/pdb/
• MMDB
• http//www.ncbi.nlm.nih.gov/Structure/
• FSSP
• http//www.ebi.ac.uk/dali/fssp/
• SCOP
• http//scop.mrc-lmb.cam.ac.uk/scop/
• CATH
• http//www.biochem.ucl.ac.uk/bsm/cath_new/

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Structural Proteomics

• Aim to delineate total repertoire of protein
  folds
• Provide 3D portraits for all proteins in an
  organism
• Goal Use structure to infer function.
• Compare structure of unknown protein to known set
  of structures
• More sensitive than primary sequence comparisons

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The Protein Fold Universe
500? 2000? 10000?
How Big Is It???
8
?
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Structures in PDB
PDB 19860 structures Jan 03 PDB 23997
structures Jan 04 structural genomics search
156 structures Jan 03 search 478 structures Jan
04
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Structural Proteomics
100000
90000
80000
70000
60000
50000
Sequences
Structures
40000
30000
20000
10000
0
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Unique folds in PDB
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Prediction Methods for 3D structure

• Intermediate Steps
• Predict secondary structure
• Calculate solvent accessibility
• Methods for 3D structure prediction based on
• Threading, Homology Modeling or Fold recognition
• Similarity in amino acid sequence implies similar
  structure/function
• Ab Initio Techniques
• Numerical methods designed to simulate the
  structure and dynamics of marcromolecules

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Proteomics

• The study of the expression, location,
  interaction, function and structure of all the
  proteins in a given cell or organism
• Expressional Proteomics
• Functional Proteomics
• Structural Proteomics

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Proteomics

• Expressional Proteomics
• 2D or Capillary Electrophoresis, protein chips
• Mass Spectrometry, Laser induced fluorescence
• Functional Proteomics
• Mass Spectrometry, micro-assays, protein chips
• Yeast or Bacterial 2-hybrid systems
• Structural Proteomics
• High throughput X-ray crystallography
• High throughput NMR spectroscopy

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2D Gel Principles
SDS PAGE
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Mass Spec Principles
Sample

_
Detector
Ionizer
Mass Filter
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Ionization Methods
370 nm UV laser
Fluid (no salt)

_
Gold tip needle
cyano-hydroxy cinnamic acid
MALDI
ESI
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Protein ID Protocol
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Computational Tools for Protein Identification

• PeptIdent
• http//us.expasy.org/tools/peptident.html
• Mascot
• http//www.matrixscience.com/search_form_select.ht
  ml
• ProteinProspector
• http//prospector.ucsf.edu/
• MOWSE
• http//srs.hgmp.mrc.ac.uk/cgi-bin/mowse
• PeptideSearch
• http//www.mann.embl-heidelberg.de/
  GroupPages/PageLink/peptidesearchpage.html
• AACompSim/AACompIdent
• http//www.expasy.ch/tools

Covered in Lab 6.4
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Proteomics

• Human proteome estimated to contain 500,000
  proteins
• The next big wave in bioinformatics
• How to deal with so much data?
• How to link structure to function to sequence?
• How to show or store temporal and spatial data?
• How to use it in drug discovery development?

Proteomics Workshop July 19 24th, 2004
Calgary, Alberta
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The Cutting Edge of Proteomics

• Evolution of Proteomes
• Structural Genomics
• Quantitative Mass Spectrometry and Protein
  Chip Technology
• Chemical Proteomics
• Proteome Scale Analysis of Networks, i.e., signal
  transduction, Y2H experiments

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Global Proteome Interaction Mapping in C. elegans
Science 23 January 2004 303 540
see also
Science 7 January 2000 287 116
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Yeast Two Hybrid (Y2H) on the genomic scale

• Global interaction map of C. elegans
• Use proteome as bait in Y2H experiment
• Detect all pairwise interactions
• Create global proteinprotein interaction network

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ProteinProtein Interaction Networks
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DNA vs Protein Chip Technology

• DNA microtechnology
• Can successfully read 1000s of side by side
  measurements of RNA levels
• BUT RNA ? protein function
• Protein Microarray Technology
• Goal develop protein chip with proteins in
  active state.
• Proteins more challenging to prepare than DNA/RNA
• Protein functionality depends on state,
  modifications, binding partners, localization
  etc.

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Protein Chip - Methods

• Attachment Methods
• Diffusion
• Absorption
• nitrocellulose
• Covalent Crosslinking
• Reactive surfaces
• Affinity Attachment
• Affinity tags

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Protein Chip - Applications

• Antibody Chip
• Detect Ag-Ab interactions
• Protein Chip
• Proteinprotein
• Proteindrug
• Enzymesubstrate
• Ligand Chip
• And more.

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Protein Chips
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Summary

• Protein sequence, and subsequently protein
  sequence databases, are much more complex than
  DNA
• Prediction of protein structure is a complex
  problem at both the 2D and 3D levels
• Proteomics initiatives based on different
  technologies are making inroads into the study of
  protein structure and function on a global level