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Hidden unit weights in network model correlations

Hidden unit weights in network model correlations. Compartments in the eukaryotic cell. Protein targeting/localization signals. Signal peptide Mitochondrial targeting peptide Chloroplast targeting peptide LPxTG sorting signal Peroxisomal targeting signal (PTS2) Signal anchor

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Hidden unit weights in network model correlations

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  1. Hidden unit weights in network model correlations

  2. Compartments in the eukaryotic cell

  3. Protein targeting/localization signals • Signal peptide • Mitochondrial targeting peptide • Chloroplast targeting peptide • LPxTG sorting signal • Peroxisomal targeting signal (PTS2) • Signal anchor • Nuclear localization signal • ER/Golgi retention signal • Peroxisomal targeting signal (PTS1) • Transmembrane helices Cleaved Uncleaved

  4. Classical secretory pathway

  5. The secretory signal peptide

  6. Targeting to the ER

  7. Eukaryotic signal peptide logo

  8. Characteristics of signal peptides

  9. Prokaryotic signal peptide logos Gram-negative bacteria Gram-positive bacteria

  10. Positive and negative training data: secreted versus cytoplasmic and nuclear sequences 130 YGIW_ECOLI MAKFAAVIAVMALCSAPVMAAEQGGFSGPSATQSQAGGFQGPNGSVTTVESAKSLRDDTWVTLRGNIVERISDDLYVFKD 80 ASGTINVDIDHKRWNGVTVTPKDTVEIQGEVDKDWNSVEIDVKQIRKVNP 160 SSSSSSSSSSSSSSSSSSSSCMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM 80 MMMMMMMMMMMMMMMMMMM------------------------------- 160 184 PMFA_PROMI MKLSKIALAAALVFGINSVATAENETPAPKVSSTKGEIQLKGEIVNSACGLAASSSPVIVDFSEIPTSALANLQKAGNIK 80 KDIELQDCDTTVAKTATVSYTPSVVNAVNKDLASFVSGNASGAGIGLMDAGSKAVKWNTATTPVQLINGVSKIPFVAYVQ 160 AESADAKVTPGEFQAVINFQVDYQ 240 SSSSSSSSSSSSSSSSSSSSSSCMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM 80 MMMMMMMMMMMMMMMMMMM------------------------------------------------------------- 160 ------------------------ 324 CYSB_KLEAE MKLQQLRYIVEVVNHNLNVSSTAEGLYTSQPGISKQVRMLEDELGIQIFARSGKHLTQVTPAGQEIIRIAREVLSKVDAI 80 KSVAGEHTWPDKGSLYVATTHTQARYALPGVIKGFIERYPRVSLHMHQGSPTQIAEAVSKGNADFAIATEALHLYDDLVM 160 LPCYHWNRSIVVTPEHPLATKASVSIEELAQYPLVTYTFGFTGRSELDTAFNRAGLTPRIVFTATDADVIKTYVRLGLGV 240 GVIASMAVDPVSDPDLVKLDANGIFSHSTTKIGFRRSTFLRSYMYDFIQRFAPHLTRDVVDTAVALRSNEDIEAMFKDIK 320 LPEK 400 MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM 80 MMMMMMMMMMMMMMMMMMM------------------------------------------------------------- 160 -------------------------------------------------------------------------------- 240 -------------------------------------------------------------------------------- 320 ---- 400 157 SBMC_ECOLI MNYEIKQEEKRTVAGFHLVGPWEQTVKKGFEQLMMWVDSKNIVPKEWVAVYYDNPDETPAEKLRCDTVVTVPGYFTLPEN 80 SEGVILTEITGGQYAVAVARVVGDDFAKPWYQFFNSLLQDSAYEMLPKPCFEVYLNNGAEDGYWDIEMYVAVQPKHH 160 MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM 80 MMMMMMMMMMMMMMMMMMM---------------------------------------------------------- 160

  11. Data partitioning for training and test Training Test Remove highly similar sequences from data set, where cleavage site Information reliably can be transferred by alignment. A redundancy reduced data set can be used to make, say five-fold cross-validation. The training set may ideally contain equal amounts of sequences with negative and positive examples.

  12. Sliding window Sequence: MAKFAAVIAVMALCSAPVMAAEQGGFSGPSATQSQAGGFQGPNGSVTTVES ... Window size here is 9 (example) Window 1: MAKFAAVIA Window 2: AKFAAVIAV Window 3: KFAAVIAVM Window 4: FAAVIAVMA ... Window 10: VMALCSAPV ... For signal peptide prediction typically the first 70 aa of positive and negative sequenes are used.

  13. Graphical output from SignalP

  14. Alternative start codon “prediction”

  15. Symmetric and asymmetric neural network window sizes • SignalP uses two different networks for signal peptide prediction: • Cleavage site prediction network (C-score) • Signal peptide vs. non-signal peptide discrimination • network (S-score) • An asymmetric window is used for cleavage site prediction (more information are found upstream of the cleavage site (see logo)) • A symmetric window is used for discrimination between signal peptide windows and mature protein windows

  16. Neural network windows in SignalP

  17. Performance calculation tp: true positive tn: true negative fp: false positive fn: false negative

  18. Optimization of window sizes Optimization of window sizes for SignalP version 3.0

  19. NN window sizes for SignalP 3.0 Window sizes used in the final method An asymmetric window is best for the cleavage site prediction, whereas symmetric windows is best for discrimination.

  20. SignalP 3.0 architecture In addition to sequence input, composition (entire sequence) and position of the sliding window was used in the neural network of SignalP 3.0

  21. Implementation of position neuron Input to NN Fortran code RLAV = 24 IF (LET .LT. RLAV) THEN X = REAL(LET)/REAL(RLAV) ELSEIF (REAL(LET) .GT. 2.0*RLAV) THEN X = 0.0 ELSE X = 1.0 - ((REAL(LET)-RLAV)/REAL(RLAV)) ENDIF 1 0 0 Position in sequence 24 48 MKLLQRGVALALLTTFTLASETALAYEQDKTYKITVLHTNDHHGHFWRNEYGEYGLAAQK

  22. Composition of secretory vs. non-secretory proteins

  23. Composition weights

  24. Data set From SWISS-PROT rel. 40.0 Highly curated Cleaned for spurious residues at pos. -1 Length and composition improves the performance significantly Length improves both discrimination and cleavage performance Composition improves discrimination D-score Average of mean-S score and Y-max score Better discrimination What is new in SignalP version 3.0!

  25. Some of the manually curated databases contain obvious errors that can be eliminated General ``SIGNAL´´ errors Signal peptide include propeptide Wrong signal peptide cleavage site The secreted protein is processed by proteases Wrong start codon used Signal peptide of different class, ie. TAT or bacteriocin (prokaryote) Database annotation errors

  26. Signal peptide or propeptide

  27. Signal peptide or propeptide Signal peptide cleavage Propeptide cleavage

  28. Isoelectric point calculations

  29. Improvement by length and composition

  30. Performance of three different SignalP versions SignalP paper now has more than 2500 citations.

  31. Exons and introns: discontinous protein coding regions in eukaryotes

  32. Two ways to solve the problem Predict splice sites (GT-donor and AG-acceptor) or Predict coding versus non-coding (at least in non-UTRs)

  33. C C TGGACCGGGTGA 0.12 0.11 0.10

  34. C TGGACCGGGTGA C 0.12 0.11 0.10 0.14

  35. TGGACCGGGTGA C G 0.12 0.11 0.10 0.14 0.23

  36. Splice site networks overpredict a lot

  37. Combination of splice site and coding/non-coding networks

  38. Combinationof splice siteand coding/non-codingnetworks

  39. 1 HUMA1ATP TACATCTTCTTTAAAGGTAAGGTTGCTCAACCA 1 HUMA1ATP CCTGAAGCTCTCCAAGGTGAGATCACCCTGACG 1 HUMACCYBA CCACACCCGCCGCCAGGTAAGCCCGGCCAGCCG 1 HUMACCYBA CGAGAAGATGACCCAGGTGAGTGGCCCGCTACC 1 HUMACTGA GCGCCCCAGACACCAGGTGAGTGGATGGCGCCG 1 HUMACTGA AGAGAAGATGACTCAGGTGAGGCTCGGCCGACG 1 HUMACTGA CACCATGAAGATCAAGGTGAGTCGAGGGGTTGG 1 HUMADAG TCTTATACTATGGCAGGTAAGTCCATACAGAAG 1 HUMALPHA CGTGGCTCTGTCCAAGGTAAGTGCTGGGCTACC 1 HUMALPI CCTGGCTCTGTCCAAGGTAAGGGCTGGGCCACC 1 HUMALPPD TGTGGCTCTGTCCAAGGTAAGTGCTGGGCTACC 1 HUMAPRTA CCTGGAGTACGGGAAGGTAAGAGGGCTGGGGTG 1 HUMCAPG GAAGGCTGCCTTCAAGGTAAGGCATGGGCATTG 1 HUMCFVII GGAGTGTCCATGGCAGGTAAGGCTTCCCCTGGC 1 HUMCP21OH CACCTTGGGCTGCAAGGTGAGAGGCTGATCTCG 1 HUMCP21OHC CACCTTGGGCTGCAAGGTGAGAGGCTGATCTCG 1 HUMCS1 GTGGCAATGGCTCCAGGTAAGCGCCCCTAAAAT 1 HUMCSFGMA AATGTTTGACCTCCAGGTAAGATGCTTCTCTCT 1 HUMCSPB AAAGACTTCCTTTAAGGTAAGACTATGCACCTG 1 HUMCSFGMA AATGTTTGACCTCCAGGTAAGATGCTTCTCTCT 1 HUMCSPB AAAGACTTCCTTTAAGGTAAGACTATGCACCTG 1 HUMCYC1A GCTACGGACACCTCAGGTGAGCGCTGGGCCGGG ... 2 HUMA1ATP CCTGGGACAGTGAATCGTAAGTATGCCTTTCAC 2 HUMA1ATP AAAATGAAGACAGAAGGTGATTCCCCAACCTGA 2 HUMA1GLY2 CGCCACCCTGGACCGGGTGAGTGCCTGGGCTAG 2 HUMA1GLY2 GAGAGTACCAGACCCGGTGAGAGCCCCCATTCC 2 HUMA1GLY2 ACCGTCTCCAGATACGGTGAGGGCCAGCCCTCA 2 HUMA1GLY2 GGGCTGTCTTTCTATGGTAGGCATGCTTAGCAG 2 HUMA1GLY2 CACCGACTGGAAAAAGGTAAACGCAAGGGATTG 2 HUMACCYBA GCGCCCCAGGCACCAGGTAGGGGAGCTGGCTGG 2 HUMACCYBA CAGCCTTCCTTCCTGGGTGAGTGGAGACTGTCT 2 HUMACCYBA CACAATGAAGATCAAGGTGGGTGTCTTTCCTGC 2 HUMACTGA TCGCGTTTCTCTGCCGGTGAGCGCCCCGCCCCG 2 HUMADAG CTTCGACAAGCCCAAAGTGAGCGCGCGCGGGGG 2 HUMADAG TGTCCAGGCCTACCAGGTGGGTCCTGTGAGAAG 2 HUMADAG CGAAGTAGTAAAAGAGGTGAGGGCCTGGGCTGG ... 11 HUMCS1 AACGCAACAGAAATCCGTGAGTGGATGCCGTCT 11 HUMGHN AACACAACAGAAATCCGTGAGTGGATGCCTTCT 52 HUMHSP90B CTCTAATGCTTCTGATGTAGGTGCTCTGGTTTC 80 HUMMETIF1 ACCTCCTGCAAGAAGAGTGAGTGTGAGGCCATC 112 HUMHSP90B ATACCAGAGTATCTCAGTGAGTATCTCCTTGGC 113 HUMHST GCGGACACCCGCGACAGTGAGTGGCGCGGCCAG 113 HUMLACTA GACATCTCCTGTGACAGTGAGTAGCCCCTATAA 151 HUMKAL2 ATCGAACCAGAGGAGTGTACGCCTGGGCCAGAT 157 HUMCS1 CACCTACCAGGAGTTTGTAAGTTCTTGGGGAAT 157 HUMGHN CACCTACCAGGAGTTTGTAAGCTCTTGGGGAAT 164 HUMALPHA CAACATGGACATTGATGTGCGACCCCCGGGCCA 622 HUMCFVII CTGATCGCGGTGCTGGGTGGGTACCACTCTCCC 636 HUMADAG CCTGGAACCAGGCTGAGTGAGTGATGGGCCTGG 895 HUMAPOCIB TCCAGCAAGGATTCAGGTTGTTGAGTGCTTGGG 970 HUMALPHA CGGGCCAAGAAAGCAGGTGGAGCTGGGGCCCGG 2114 HUMAPRTA ATCGACTACATCGCAGGCGAGTGCCAGTGGCCG

  40. Neural network weight analysis: reading frame detection

  41. Exon-intron transistion detection units

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