Protein Subcellular Localization

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Protein Subcellular Localization

• Shan Sundararaj
• ss23_at_ualberta.ca
• June 24, 2005

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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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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, Biochemistry
Wiley-VCH 1992)
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Yet more categories
Chloroplast
Mitochondrion
Yeast specific
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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 Swiss-Prot 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
• Yeast Protein Localization Server
  (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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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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Confirmation by Co-localization (GFP/RFP merging)
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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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Subcellular Fractionation

• Fractionate cells into organelles and other
  compartments using differential solubilization
  and centrifugation
• 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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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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Computational methods for predicting localization

• Motif based methods
• 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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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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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 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

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Summary

• The data set of experimentally validated protein
  localizations is ever increasing, especially with
  high-throughput methods
• Many localization signals are still unknown,
  except for simple sequence motifs
• Prediction methods are very accurate, especially
  for bacteria and using machine learning
  techniques, but many motifs and other signals
  have yet to be discovered

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Future Directions

• Predict proteins with multiple localization
  sites, or with localization that changes over
  time
• Integrate structural information into
  localization prediction