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Eukaryotic Secretome Prediction and Knowledge-Base Development

Eukaryotic Secretome Prediction and Knowledge-Base Development. Xiang-Jia “Jack” Min Ph.D., Assistant Professor. 2 nd International Conferences on Proteomics & Bioinformatics. Las Vegas, July 2 - 4, 2012 . DNA. RNA. protein. phenotype. Genome. Transcription. mRNA

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Eukaryotic Secretome Prediction and Knowledge-Base Development

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  1. Eukaryotic Secretome Prediction and Knowledge-Base Development Xiang-Jia “Jack” Min Ph.D., Assistant Professor 2nd International Conferences on Proteomics & Bioinformatics. Las Vegas, July 2 - 4, 2012

  2. DNA RNA protein phenotype

  3. Genome Transcription mRNA (protein-coding DNA sequences) Transcriptome Translation Protein sequences Proteome Secretion Proteins with secretory signal peptide Secretome

  4. Günter Blobel

  5. Fungi Yeasts Moulds Mushrooms secreted enzymes Small molecules Biomaterials Enzymes Bio-fuels Biomaterials

  6. Genome Transcription Transcriptome Translation Proteome Secretion Secretome How to identify secreted proteins? • Direct identification using proteomics methods (Tsang et al. 2009) • Computational prediction from predicted proteome • EST data mining

  7. Secreted Proteins • Classical secreted proteins have a signal peptide at N-terminus; • Not all proteins have a signal peptide are secreted: • Signal peptide = secreted protein

  8. SignalP:a program to predict if a protein contains a signal peptide. Phobius:signal peptide and transmembrane domain predicton. WolfPsort: a multiple subcellular location predictor TargetP: detect proteins targeted to mitochondria. TMHMM:transmembrane domain prediction. PS-Scan: detection ER-retention signals

  9. Human cytochrome C oxidase subunit 1 (COX1)

  10. Data Secreted Non-secreted Fungi 241 5,992 Animals 5,568 19,048 Plants 216 7,528 Protists 32 1,979

  11. Method • Sensitivity (%) = TP/(TP + FN) x 100 • Specificity (%) = TN/(TN + FP) x 100 • Mathews’ Correlation Coefficient (MCC) MCC (%) = (TP x TN – FP x FN) x 100 /((TP + FP) (TP + FN) (TN + FP) (TN + FN))1/2

  12. Table 1. Prediction accuracies of secreted proteins in fungi TP: true positives; FP: false positives; TN: true negatives; FN: false negatives. Sn: sensitivity; Sp:specificity;MCC: Mathews' correlation coefficient. Min XJ (2010) JPB 3:143-147.

  13. Table 2. Prediction accuracies of secreted proteins in animals TP: true positives; FP: false positives; TN: true negatives; FN: false negatives. Sn: sensitivity; Sp:specificity; MCC: Mathews' correlation coefficient. Min XJ (2010) JPB 3:143-147.

  14. Table 3. Prediction accuracies of secreted proteins in plants TP: true positives; FP: false positives; TN: true negatives; FN: false negatives. Sn: sensitivity; Sp:specificity; MCC: Mathews' correlation coefficient. Min XJ (2010) JPB 3:143-147.

  15. Summary • Different prediction tools have different accuracies for prediction of secretomes in different kingdoms of species; • Combining these tools often increases the prediction accuracy. However, differential combination are needed for species in different kingdoms. • Optimal methods are proposed.

  16. Views User Inputs Database External Links gi accession UniProt ID Keywords Species RefSeq UniProt Prediction Tools FunSecKB SignalP Phobius WolfPsort TargetP TMHMM PS-SCAN fragAnchor Subcellular Location Manual Curation Lum G & Min XJ (2011) Database.

  17. Summary of FunSecKB • Currently the database contains a total of 478,073 fungal protein sequences • 23,878 predicted and / or curated secreted proteins • A total of 118 fungal species including 52 fungal species having a complete proteome

  18. Lum G & Min XJ (2011) Database.

  19. Lum G & Min XJ (2011) Database.

  20. Lum G & Min XJ (2011) Database.

  21. Plant secretomes and other subcellular proteins

  22. Acknowledgements Gengkon Lum (M. S. Graduate) Jessica Orr (Undergraduate) Docylyne Shelton (Undergraduate) Braden Walters (Undergraduate)

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