Protein Feature Identification

1
Protein Feature Identification

• Microbiology 343
• David Wishart
• david.wishart_at_ualberta.ca

2
Objectives

• To show that almost everything you do in the lab
  or what you need to do to work with a protein can
  be done on a computer
• Learning methods and algorithms for predicting
  composition and sequence features
• Learning when to use these tools

3
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 targets are proteins
• Favorite topic of post-genomic era

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The Post-genomic Challenge

• How to rapidly identify a protein?
• How to rapidly purify a protein?
• How to identify post-trans modification?
• How to find information about function?
• How to find information about activity?
• How to find information about location?
• How to find information about structure?

Answer Look at Protein Features
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Protein Features
ACEDFHIKNMF SDQWWIPANMC ASDFDPQWERE LIQNMDKQERT QA
TRPQDS...

Sequence View Structure View
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Different Types of Features

• Composition Features
• Mass, pI, Absorptivity, Rg, Volume
• Sequence Features
• Active sites, Binding Sites, Targeting, Location,
  Property Profiles, 2o structure
• Structure Features
• Supersecondary Structure, Global Fold, ASA,
  Volume

7
Where To Go
http//www.expasy.org/tools/
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Compositional Features

• Molecular Weight
• Amino Acid Frequency
• Isoelectric Point
• UV Absorptivity
• Solubility, Size, Shape
• Radius of Gyration
• Free Energy of Folding

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Molecular Weight
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Molecular Weight

• Useful for SDS PAGE and 2D gel analysis
• Useful for deciding on SEC matrix
• Useful for deciding on MWC for dialysis
• Essential in synthetic peptide analysis
• Essential in peptide sequencing (classical or
  mass-spectrometry based)
• Essential in proteomics and high throughput
  protein characterization

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Molecular Weight

• Crude MW calculation MW 110 X Numres
• Exact MW calculation MW SAAi x MWi
• Remember to add 1 water (18.01 amu) after adding
  all res.
• Note isotopic weights
• Corrections for CHO, PO4, Acetyl, CONH2

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Amino Acid versus Residue
R
R
C
C
CO
N
COOH
H2N
H
H
H
Amino Acid Residue
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Protein Identification via MW

• MOWSE
• http//srs.hgmp.mrc.ac.uk/cgi-bin/mowse
• CombSearch
• http//ca.expasy.org/tools/CombSearch/
• Mascot
• http//www.matrixscience.com/search_form_select.ht
  ml
• AACompSim/AACompIdent
• http//ca.expasy.org/tools/

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Molecular Weight Proteomics
2-D Gel QTOF Mass Spectrometry
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Amino Acid Frequency

• Deviations greater than 2X average indicate
  something of interest
• High K or R indicates possible nucleoprotein
• High Cs indicate stable but hard-to-fold protein
• High G, P, Q, or N says lack of stable structure

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Isoelectric Point (pI)

• The pH at which a protein has a net charge0
• Q S Ni/(1 10pH-pKi)

Transcendental equation
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Isoelectric Point

• Calculation is only approximate (/- 1 pH)
• Does not include 3o structure interactions
• Can be used in developing purification protocols
  via ion exchange chromatography
• Can be used in estimating spot location for
  isoelectric focusing gels
• Can be used to decide on best pH to store or
  analyze protein

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UV Spectroscopy
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UV Absorptivity

• UV (Ultraviolet light) has a wavelength of 200 to
  400 nm
• Most proteins and peptides (and all nucleic
  acids) absorb UV light quite strongly
• UV spectroscopy is the most common form of
  spectroscopy performed today
• UV spectra can be used to identify or classify
  some proteins or protein classes

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UV Absorptivity

• OD280 (5690 x W 1280 x Y)/MW x Conc.
• Conc. OD280 x MW/(5690 X W 1280 x Y)

OH
N
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Hydrophobicity

• Indicates Solubility
• Indicates Stability
• Indicates Location (membrane or cytoplasm)
• Indicates Globularity or tendency to form
  spherical structure

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Hydrophobicity

• Average Hydrophobicity AH S AAi x Hi
• Hydrophobic Ratio RH S H(-)/S H()
• Hydrophobic Ratio RHP philic/phobic
• Linear Charge Density LIND (KRDEH2)/
• Solubility SOL RH LIND - 0.05AH

• Average AH 2.5 2.5 Insol gt 0.1 Unstrc lt
  -6
• Average RH 1.2 0.4 Insol lt 0.8 Unstrc gt
  1.9
• Average RHP 0.9 0.2 Insol lt 0.7 Unstrc gt 1.4
• Average LIND 0.25 Insol lt 0.2 Unstrc gt 0.4
• Average SOL 1.6 0.5 Insol lt 1.1 Unstrc gt 2.5

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Different Types of Features

• Composition Features
• Mass, pI, Absorptivity, Hydrophobicity
• Sequence Features
• Active sites, Binding Sites, Targeting, Location,
  Property Profiles, 2o structure
• Structure Features
• Supersecondary Structure, Global Fold, ASA,
  Volume

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Sequence Features
AHGQSDFILDEADGMMKSTVPN HGFDSAAVLDEADHILQWERTY
GGGNDEYIVDEADSVIASDFGH LIVMLIVMDEADLIVM
LIVM (EIF 4A ATP DEPENDENT HELICASE)
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Sites that Support Pattern Queries

• OWL Database
• http//umber.sbs.man.ac.uk/dbbrowser/OWL/
• PIR Website
• http//pir.georgetown.edu/pirwww/search/patmatch.h
  tml
• SCNPSITE at EXPASY
• http//ca.expasy.org/tools/scanprosite/
• PattinProt
• http//npsa-pbil.ibcp.fr/cgi-bin/npsa_automat.pl?p
  agenpsa_pattinprot.html/

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Regular Expressions

• CACGT - Matches CAT, CCT and CGT only
• C . T - Matches CAT, CaT, C1T, CXT, not CT
• CA?T - Matches CT or CAT only
• CT - Matches CT, CCT, CCCT, CCCCT
• C(HE)?ATP - Matches CHEAT, CAT, CHEAP, CAP
• SA-I,L-Q,T-Z?LKA-I,L-Q,T-Z?A - Matches SLKA

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PROSITE Pattern Expressions
C - ACG - T - Matches CAT, CCT and CGT only C -
X -T - Matches CAT, CCT, CDT, CET, etc. C - A
-T - Matches every CXT except CAT C - (1,3) - T -
Matches CT, CCT, CCCT C - A(2) - TP - Matches
CAAT, CAAP LIV - VIC - X(2) - G - DENQ - X
- LIVFM (2) -G
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Sequence Feature Databases

• PROSITE - http//ca.expasy.org/prosite/
• BLOCKS - http//www.blocks.fhcrc.org/
• DOMO - http//www.infobiogen.fr/services/domo/
• PFAM - http//pfam.wustl.edu
• PRINTS - http//www.bioinf.man.ac.uk/dbbrowser/PRI
  NTS/
• SEQSITE - PepTool

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Phosphorylation Sites
pY
pT
pS
PO4
PO4
CH3
PO4
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Phosphorylation Sites
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Signaling Sites
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Protease Cut Sites
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Binding Sites
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Family Signature Sequences
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Enzyme Active Sites
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Better Methods for Sequence Feature ID

• Sequence Profiles/Scoring Matrices
• Neural Networks
• Hidden Markov Models
• Bayesian Belief Nets
• Reference Point Logistics

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What Can Be Predicted?

• O-Glycosylation Sites
• Phosphorylation Sites
• Protease Cut Sites
• Nuclear Targeting Sites
• Mitochondrial Targ Sites
• Chloroplast Targ Sites
• Signal Sequences
• Signal Sequence Cleav.
• Peroxisome Targ Sites

• ER Targeting Sites
• Transmembrane Sites
• Tyrosine Sulfation Sites
• GPInositol Anchor Sites
• PEST sites
• Coil-Coil Sites
• T-Cell/MHC Epitopes
• Protein Lifetime
• A whole lot more.

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Cutting Edge Sequence Feature Servers

• Membrane Helix Prediction
• http//www.cbs.dtu.dk/services/TMHMM-2.0/
• T-Cell Epitope Prediction
• http//syfpeithi.bmi-heidelberg.com/scripts/MHCSer
  ver.dll/home.htm
• O-Glycosylation Prediction
• http//www.cbs.dtu.dk/services/NetOGlyc/
• Phosphorylation Prediction
• http//www.cbs.dtu.dk/services/NetPhos/
• Protein Localization Prediction
• http//psort.nibb.ac.jp/

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Subcellular Localization
http//www.cs.ualberta.ca/bioinfo/PA/Sub/
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Profiles Motifs are Useful

• Helped identify active site of HIV protease
• Helped identify SH2/SH3 class of STPs
• Helped identify important GTP oncoproteins
• Helped identify hidden leucine zipper in HGA
• Used to scan for lectin binding domains
• Regularly used to predict T-cell epitopes

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Amino Acid Property Profiles
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Amino Acid Property Profiles

• Intent is to predict proteins physical
  properties directly from sequence as opposed to
  composition or wet chemistry
• Offers a more detailed, graphical view of
  sequence-specific properties than compositional
  analysis (more powerful?)
• Underlying assumption is amino acid properties
  are additive

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Common Property Profiles

• Hydrophobicity (Watch Scales!)
• Helical Wheel (Not a True Profile)
• Hydrophobic Moments (Helix Beta sheet)
• Flexibility (Thermal B Factors)
• Surface Accessibility (ASA)
• Antigenicity (B-cell epitopes/T-cell epitopes)

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Hydrophobicity Profile

• Plotted using ltHgti S Hn/(2k 1)
• Shows location of membrane spanning regions,
  epitopes, surface exposed AAs, etc.

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Flexibility

• B factors from X-ray crystallography
• Potentially identifies antigenic and active sites
  from sequence data alone

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Membrane Spanning Regions
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Predicting via Hydrophobicity
Bacteriorhodoposin OmpA
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Predicting via Hydrophobicity
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Predicting via Neural Nets

• PHDhtm http//cubic.bioc.columbia.edu/predictpro
  tein/submit_adv.html
• TMAP http//www.mbb.ki.se
  /tmap/index.html
• TMPred http//www.ch.embnet.org/software/TMPRED
  _form.html

ACDEGF...
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Secondary Structure
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Secondary Structure Prediction
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Secondary Structure Prediction

• Statistical (Chou-Fasman, GOR)
• Homology or Nearest Neighbor (Levin)
• Physico-Chemical (Lim, Eisenberg)
• Pattern Matching (Cohen, Rooman)
• Neural Nets (Qian Sejnowski, Karplus)
• Evolutionary Methods (Barton, Niemann)
• Combined Approaches (Rost, Levin, Argos)

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Chou-Fasman Statistics
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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/
• PSIpred (77)
• http//bioinf.cs.ucl.ac.uk/psipred/
• Proteus (88)
• http//129.128.185.1848080/proteus/

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Sample Exam Questions

• Here is the sequence for protein X, calculate its
  molar absorptivity
• Here is the sequence for protein Y, try to locate
  the likely membrane spanning regions explain
  your reasoning
• Here is the sequence for protein Z, show the
  tryptic cleavage points