Shan Sundararaj

1
Protein Subcellular Localization

• Shan Sundararaj
• University of Alberta
• Edmonton, AB
• ss23_at_ualberta.ca

2
Why is Localization Important?

• Function is dependent on context
• Co-localization of proteins of related function
• Valuable annotation for new proteins
• Design of proteins with specific targets
• Drug targeting
• Accessibility
• Membrane-bound gt cytoplasmic gt nuclear

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Why is Localization Important?

• 1974 Nobel Prize in Physiology/Medicine
• George Palade
• for discoveries concerning the structural and
  functional organization of the cell
• 1999 Nobel Prize in Physiology/Medicine
• Günter Blobel
• for the discovery that proteins have intrinsic
  signals that govern their transport and
  localization in the cell

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Bacteria
Gram Positive (3-4 states)
Gram Negative (5 states)
Extracellular
cytoplasm
cytoplasm
periplasm
cytoplasmic membrane
cytoplasmic membrane
cell wall
outer membrane
Extracellular
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Eukaryotic Cell

• Compartmentalized
• Diverse range of specific organelles
• Plants chloroplasts, chromoplasts, other
  plastids
• Muscle sarcoplasm
• Various endosomes, vesicles

(modified from Voet Voet, Biochemystry
Wiley-VCH 1992)
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Yet more categories
Chloroplast
Mitochondrion
Yeast specific
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Level of Annotation

• As simple as two states
• membrane protein vs. non-membrane protein
• secreted protein vs. non-secreted protein
• Gross compartments
• cytoplasm, inner membrane, periplasm, cell wall,
  outer membrane, extracellular
• nucleus, mitochondria, peroxisome, vacuole
• Fine compartments
• Mitochondrial matrix, bud neck, spindle pole
• Any of 1425 GO cellular compartments

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Localization signaling

• Proteins must have intrinsic signals for their
  localization a cellular address
• E.g. N-terminal signal sequences

321 Nuclear Inner Membrane Lane Nucleus,
Intracellular county Eukaryotic Cell CL34V3M3
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Localization signaling

• Some signals are easily recognizable
• Signal peptidase cleavage site, consensus
  sequence for secretion ? extracellular
• Address printed neatly, postal code
• Others are difficult to understand
• Outer membrane b-barrel proteins, no consensus
  sequence, few sequence restraints
• Sloppy address, different kind of code that we
  dont understand yet

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Experimental determination

• Since dont fully understand the language of
  proteins, our knowledge must often come from
  inference
• Predicting localization is like sorting mail
  based only on examples of where some mail has
  gone before
• Important to have good data sets of proteins with
  known localizations

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Datasets

• Organelle_DB (http//organelledb.lsi.umich.edu/)
• 25095 eukaryotic proteins from subcellular
  proteomics studies
• DBSubLoc (http//www.bioinfo.tsinghua.edu.cn/guot
  ao/download.html)
• Combines SwissProt and PIR annotations (64051
  proteins)
• PSORTDB (http//db.psort.org/)
• Bacterial. 1591 Gram ve proteins, 574 Gram ve
  proteins
• SignalP (http//www.cbs.dtu.dk/ftp/signalp/)
• 940 plant and 2738 human proteins
• YPL (http//bioinfo.mbb.yale.edu/genome/localize/)
  
• 2956 yeast proteins

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Experimental Methods

• Electron microscopy
• GFP tagging / fluorescence microscopy
• Subcellular fractionation detection
• Western blotting
• Mass spectrometry

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Electron Microscopy

• Highest resolution, can work at the level of a
  single protein complex
• Immunolabel proteins of interest in conjunction
  with colloidal gold, and visualize
• Combined with electron tomography, can even
  visualize unlabeled complexes

(from Koster and Klumperman, Nat Rev Mol Cell
Biol, Sep 2003, S6-10)
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Fluorescence Microscopy

• Tag gene at either 3 or 5 end
• Using GFP (or RFP, YFP, CFP, etc.)
• Using an epitope tag and a fluorescently labeled
  antibody
• Careful of removing signal peptides!
• Also use a subcellular-specific marker or stain
• Visualize with confocal fluorescence microscopy
  and analyze images for co-localization

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Specific co-labeling (yeast)

• Early GolgiCop1
• Endosome Snf7
• ER to Golgi Sec13
• Golgi apparatus Anp1
• Late Golgi Chc1
• Lipid particle Erg6
• Mitochondrion MitoTracker
• Nucleus DAPI
• Nucleolus Sik1
• Nuclear periphery Nic96
• Peroxisome Pex3
• Vacuole FM4-64

Nuclear-specific DAPI staining
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Subcellular Fractionation
transfer supernatant
transfer supernatant
transfer supernatant
1000 g
10,000 g
100,000 g
Pellet microsomal Fraction (ER,
golgi, lysosomes, peroxisomes)
Pellet unbroken cells nuclei chloroplast
Pellet mitochondria
Super. Cytosol, Soluble enzymes
tissue homogenate
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Detergent Fractionation
Cells
Extraction with Digitonin/EDTA
supernatant
pellet
Extraction with TritonX100/EDTA
Cytoplasmic Fraction
Extraction with SDS/EDTA
Organelle Membranes
Nuclear
Cytoskeletal (in SDS)
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Fractionation ? Identification

• Once fractionated, take compartment of interest
  and separate proteins
• 2D gel or chromatography
• Identify separated proteins
• Mass spectrometry for high-throughput
• Western blot for specific proteins

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Fractionation in proteomics
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High-Throughput Experiments

• Kumar et al., Genes Dev 2002, 16707-719
• Epitope-tagged gt60 of ORFs, visualized with
  fluorescently labeled antibody
• 2744 localizations (44 of S. cerevisiae genes)
• Huh et al., Nature 2003, 425686-691
• GFP tagged all ORFs, RFP tagged compartments
• 4156 localizations (75 of S. cerevisiae genes)
• Combined, now nearly 87 of yeast proteins have a
  localization annotation

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High-Throughput Experiments

• Lopez-Campistrous et al, Mol Cell Proteomics,
  2005
• Subcellular fractionation of E. coli, 2D-gel
  separation, MS-MS
• 2,160 localizations to cytoplasm, inner membrane,
  periplasm, and outer membrane

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Predictions from known data

• Enough experimental data exists to build highly
  accurate computational predictors of localization

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Predictions from known data

• Different information used for predictions
• Sequence motifs
• N-terminal secretory signal peptides,
  mitochondrial targeting peptide, chloroplast
  transit peptide
• C-terminal peroxisome import signal, ER
  retention signal
• Mid-sequence nuclear localization signals
• Amino acid composition
• AA frequency, dipeptide composition.
• Homology
• - Sequence comparison to proteins of known
  localization

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N-terminal signal peptides

• Common structure of signal peptides
• positively charged n-region, followed by a
  hydrophobic h-region and a neutral but polar
  c-region.

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N-terminal signal peptides
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More work to do

• Multiple bacterial secretion pathways
• C-terminal signal peptides
• Internal mitochondrial transit peptides
• Structural aspects of targeting
• Gene re-localization
• Still a lot to discover in how signaling works!

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Computational methods for predicting localization

• Expert rule based methods
• Artificial Neural Nets (ANN)
• Hidden Markov Models (HMM)
• Naïve Bayes (NB)
• Support Vector Machines (SVM)
• Combination of above methods

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Naïve Bayes

• Assumption
• Features are conditionally
• independent, given class labels
• Structure
• 1 level tree
• Class labels root
• Features leaf nodes
• Prediction
• class(f) argmax P(Cc)P(Ff Cc)
• c

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Artificial Neural Network

• Excellent for modeling non-linear input/output
  relationships
• Robust to noise in training data
• Widely used in bioinformatics

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Support Vector Machines

• Input vectors are separated into positive vs.
  negative instance
• Map to new feature space
• Find hyperplane that best separates the two
  classes by distance

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Evaluating Predictors - Precision
Predicted
True

• of proteins correctly labeled as cyt divided
  by the total of proteins labeled as cyt
• How often the label is correct
• If there are 90 proteins correctly labeled as
  cyt, and 10 proteins incorrectly labeled as
  cyt, then the precision is 90/100 0.90.

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Evaluating Predictors - Sensitivity
Predicted
True

• of proteins correctly labeled as cytoplasmic
  divided by the total of proteins that are
  cytoplasmic
• How many of the true results were retrieved
  (also called recall or accuracy)

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Predictions from known data

• Different information used for predictions
• Sequence motifs
• N-terminal secretory signal peptides,
  mitochondrial targeting peptide, chloroplast
  transit peptide
• C-terminal peroxisome import signal, ER
  retention signal
• Mid-sequence nuclear localization signals
• Amino acid composition
• AA frequency, dipeptide composition,
  hydrophobicity
• Homology
• - Sequence comparison to proteins of known
  localization

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TargetP, SignalP, Phttp//www.cbs.dtu.dk/service
s/

• Sequence-based methods
• TargetP (85-90 recall)
• Predicts mitochondria/chloroplast/secreted
• Contains SignalP and ChloroP
• LipoP
• lipoproteins and signal peptides in Gram negative
  bacteria
• SecretomeP
• non-classical secretion in eukaryotes

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SignalP result

• Common structure of signal peptides
• positively charged n-region, followed by a
  hydrophobic h-region and a neutral but polar
  c-region.

Cleavage site
Prediction Signal peptide Signal peptide
probability 0.945 Signal anchor probability
0.000 Max cleavage site probability 0.723
between pos. 28 and 29
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Organellar Prediction

• Predotar (http//www.inra.fr/predotar/) (80
  recall)
• Mitochondrial and plastid sequences N-terminal
  sequences
• MitoPred (http//mitopred.sdsc.edu/) (82 recall)
• Mitochondrial PFAM domains, AA composition
• MitoProteome (http//www.mitoproteome.org/)
• Database of experimentally predicted human
  mitochondrial
• MitoP (http//ihg.gsf.de/mitop2/)
• Combines data from multiple experimental and
  computational sources to give a consensus score
  for each mitochondrial protein in yeast and
  human

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The PSORT Family

• PSORT plant sequences
• Expert rule-based system
• PSORT II eukaryotic sequences
• Probabilistic tree
• iPSORT eukaryotic N-term. signal sequences
• ANN
• PSORT-B bacterial sequences
• WoLF PSORT eukaryotic
• Updated (2005) version of PSORTII

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PSORT-Bhttp//www.psort.org/psortb/
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PSORT-B - methods

• Signal peptides Non-cytoplasmic
• AA composition/patterns
• SVMs trained for each location vs. all other
  locations
• Transmembrane helices Inner membrane
• HMMTOP
• PROSITE motifs all localizations
• Outer membrane motifs Outer membrane
• Homology to proteins of known localization
• SCL-BLAST

Integration with a Bayesian network
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PSORT-B results

• SeqID Unannotated_bacterial2
• Analysis Report
• CMSVM- Unknown No details
• CytoSVM- Cytoplasmic No details
• ECSVM- Unknown No details
• HMMTOP- Unknown No internal
  helices found
• Motif- Unknown No motifs
  found
• OMPMotif- Unknown No motifs
  found
• OMSVM- Unknown No details
• PPSVM- Unknown No details
• Profile- Unknown No matches
  to profiles found
• SCL-BLAST- Cytoplasmic matched
  118438 Cyto. protein
• SCL-BLASTe- Unknown No matches
  against database
• Signal- Unknown No signal
  peptide detected
• Localization Scores
• Cytoplasmic 9.97
• CytoplasmicMembrane 0.01
• Periplasmic 0.01
• OuterMembrane 0.00

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Proteome Analysthttp//www.cs.ualberta.ca/bioinf
o/PA/Sub/
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Proteome Analyst - Method
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Proteome Analyst - Feature Extraction
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Proteome Analyst Feature Extraction

• TOP 3 Homologs
• ? AFP1_ARATH
• AFP1_BRANA
• AFP2_ARATH
• KW
• Plant defense Fungicide
• Signal Multigene Family
• Pyrrolidone carboxylic acid
• DR InterPro
• IPR002118 IPR003614
• CC Subcellular location
• Secreted
• Token Set

Plant defense Fungicide Signal Multigene
Family Pyrrolidone carboxylic acid IPR002118
IPR003614 Secreted
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PASub - Results
Contribution of each token
Log scale
Features
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PASub - Interpretation

• Bars represent -log probability, so a little
  difference is a lot!
• Naïve Bayes chosen as classifier because of
  transparency of method
• Each token gives a probability that can be summed
  and shown graphically
• Neural network actually has higher recall
• Can change token set, ask to explain with
  different features

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Save Time Pre-computed Genomes

• PSORTDB
• http//db.psort.org
• Browse, search, BLAST, download
• 103 Gram ve bacteria, 45 Gram ve bacteria
• Proteome Analyst (PA-GOSUB)
• http//www.cs.ualberta.ca/bioinfo/PA/GOSUB/
• Browse, search, BLAST, download
• 15 bacterial and 8 eukaryotic