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Published on October 15, 2007

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Protein Subcellular Localization:  Shan Sundararaj University of Alberta Edmonton, AB ss23@ualberta.ca Protein Subcellular Localization Why is Localization Important?:  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 > cytoplasmic > nuclear Why is Localization Important?:  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” Bacteria:  Bacteria cytoplasm cytoplasm cytoplasmic membrane cytoplasmic membrane outer membrane periplasm cell wall Extracellular Gram Positive (3-4 states) Gram Negative (5 states) Extracellular Eukaryotic Cell:  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) Yet more categories…:  Yet more categories… Chloroplast Mitochondrion Yeast “specific” Level of Annotation:  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 Localization signaling:  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 Localization signaling:  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 don’t understand yet Experimental determination:  Experimental determination Since don’t 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 Datasets:  Datasets Organelle_DB (http://organelledb.lsi.umich.edu/) 25095 eukaryotic proteins from subcellular proteomics studies DBSubLoc (http://www.bioinfo.tsinghua.edu.cn/~guotao/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 Experimental Methods:  Experimental Methods Electron microscopy GFP tagging / fluorescence microscopy Subcellular fractionation + detection Western blotting Mass spectrometry Electron Microscopy:  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) Fluorescence Microscopy:  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 Specific co-labeling (yeast):  Early Golgi:Cop1 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 Specific co-labeling (yeast) Nuclear-specific DAPI staining Subcellular Fractionation:  Subcellular Fractionation tissue homogenate 1000 g Pellet unbroken cells nuclei chloroplast transfer supernatant transfer supernatant transfer supernatant 10,000 g 100,000 g Pellet mitochondria Pellet microsomal Fraction (ER, golgi, lysosomes, peroxisomes) Super. Cytosol, Soluble enzymes Detergent Fractionation:  Detergent Fractionation Cells Extraction with Digitonin/EDTA Cytoplasmic Fraction Extraction with TritonX100/EDTA supernatant pellet Extraction with SDS/EDTA Organelle Membranes Nuclear Cytoskeletal (in SDS) Fractionation  Identification:  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 Fractionation in proteomics:  Fractionation in proteomics High-Throughput Experiments:  High-Throughput Experiments Kumar et al., Genes Dev 2002, 16:707-719 Epitope-tagged >60% of ORFs, visualized with fluorescently labeled antibody 2744 localizations (44% of S. cerevisiae genes) Huh et al., Nature 2003, 425:686-691 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 High-Throughput Experiments:  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 Predictions from known data:  Predictions from known data Enough experimental data exists to build highly accurate computational predictors of localization Predictions from known data:  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 N-terminal signal peptides:  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. N-terminal signal peptides:  N-terminal signal peptides More work to do:  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! Computational methods for predicting localization:  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 Naïve Bayes:  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(C=c)P(F=f | C=c) c Artificial Neural Network:  Artificial Neural Network Excellent for modeling non-linear input/output relationships Robust to noise in training data Widely used in bioinformatics Support Vector Machines:  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 Evaluating Predictors - Precision:  # 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. Evaluating Predictors - Precision True Predicted Evaluating Predictors - Sensitivity:  Evaluating Predictors - Sensitivity # 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”) True Predicted Predictions from known data:  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 TargetP, SignalP, *P http://www.cbs.dtu.dk/services/:  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 TargetP, SignalP, *P http://www.cbs.dtu.dk/services/ SignalP result:  SignalP result Prediction: Signal peptide Signal peptide probability: 0.945 Signal anchor probability: 0.000 Max cleavage site probability: 0.723 between pos. 28 and 29 Cleavage site Common structure of signal peptides: positively charged n-region, followed by a hydrophobic h-region and a neutral but polar c-region. Organellar Prediction:  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 The PSORT Family:  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 PSORT-B http://www.psort.org/psortb/:  PSORT-B http://www.psort.org/psortb/ PSORT-B - methods:  PSORT-B - methods Signal peptides: Non-cytoplasmic AA composition/patterns SVM’s 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 PSORT-B results:  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 Extracellular 0.00 Final Prediction: Cytoplasmic 9.97 Proteome Analyst http://www.cs.ualberta.ca/~bioinfo/PA/Sub/:  Proteome Analyst http://www.cs.ualberta.ca/~bioinfo/PA/Sub/ Proteome Analyst - Method:  Proteome Analyst - Method Proteome Analyst - Feature Extraction:  Proteome Analyst - Feature Extraction Slide44:  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} Proteome Analyst: Feature Extraction PASub - Results:  PASub - Results Features Log scale Contribution of each token PASub - Interpretation:  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 Save Time: Pre-computed Genomes:  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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