Public Database 의이용 (1) - SignalP (version 4.1)
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1 Public Database 의이용 (1) - SignalP (version 4.1) KIST 이철주 Secretion pathway prediction ProteinCenter (Proxeon Bioinformatics, Odense, Denmark; SignalP (version 4.1) ( Classical secretion pathway 예상 hidden Markov model algorithms 이용 i SignalP uses amino acid sequences to predict the existence and location of signal peptide cleavage sites. The hidden Markov model algorithms calculate the probability of whether the submitted sequence contains a signal peptide or not. A protein is considered classically secreted if it receives a default cutoff values Y (0.5 > D-cutoff for TM networks, 0.45 > D-cutoff for no-tm model networks). ii Fig 1. SignalP homepage
2 Parameter 1. Fasta 형식또는파일준비 한번의 submission에 2,000 entries, 총 200,000 amino acids가능 하나의 entry는 6,000 amino acids를넘지않아야함 ( 넘을경우 N-terminal에서부터 6000번째까지만사용 ) 2. Organism group 선택 3. Output format: data 정리및분석작업을위해서는 short 선택 Fig 2. Output format short Fig3. Output format standard 4. D-cutoff value: default 권장 (0.45이상 SignalP-noTM networks, 0.5이상 SignalP-TM networks) *TM: transmembrane state (sequence의 hydrophobicity에따라 integral transmembrane sequence 예상, 4개이상존재하면 TM으로인지 ) 5. Method: default 권장 (input sequences may include TM regions) 6. Graphics output: 다수의 proteome 분석시 default 권장 (no graphic)
3 Fig 4. Graphics output PNG (inline) 선택시, output 결과해석 Fig 5. SignalP 의 prediction results D (D-cutoff)? (Y: D-cutoff 0.45 SignalP-noTM, 0.5 SignalP-TM, N: 0.45< SignalP-noTM, 0.5 < SignalP-TM)
4 Public Database 의이용 (2) - SecretomeP (version 2.0) KIST 이철주 SecretomeP (version 2.0) ( nonclassical secretion pathway 예상 neural network 이용 SecretomeP uses a neural network that combines six protein characteristics to determine if a protein is nonclassically secreted. These characteristics include the number of atoms, number of positively charged residues, presence of transmembrane helices, presence of low-complexity regions, presence of pro-peptides and subcellular localization. A protein is considered nonclassically secreted if it receives an NN-score 0.5. iii Fig 6. SecretomeP homepage
5 Parameter 1. Fasta 형식또는파일준비 한번의 submission에 100 entries, 총 200,000 amino acids가능 하나의 entry는 40~4,000 amino acids여야함. ( 넘을경우 N-terminal에서부터 4,000번째까지만사용 ) 2. Organism group 선택 결과해석 Fig 7. SecretomeP 의 prediction results SignalP 에의해 signal peptide 가예상된된단백질을제외하고 NN-score 가 0.5 이상인경우, nonclassical secretion pathway 를통해 secretion 되었다고예상
6 Public Database 의이용 (3) - TMHMM (version 2.0) KIST 이철주 TMHMM (version 2.0) ( transmembrane helices in integral membrane proteins 예상 hidden Markov model 이용 The TMHMM program is based on a hidden Markov model approach. The hidden Markov model can incorporate hydrophobicity, charge bias, helix lengths and grammatical constraints into one model for which algorithms for parameter estimate and prediction already exist. iv Fig 8. TMHMM homepage
7 Parameter 1. Fasta 형식또는파일준비 한번의 submission 에 4,000 entries 가능 2. Output format 선택 : one line per protein 추천 결과해석 Fig 9. TMHMM 의 prediction results (Extensive, with graphics)
8 Fig 10. TMHMM 의 prediction results (one line per protein) Length ( len= ): the length of the protein sequence. Number of predicted TMHs: The number of predicted transmembrane helices. Exp number of AAs in TMHs ( ExpAA= ): The expected number of amino acids intransmembrane helices. If this number is larger than 18 it is very likely to be a transmembrane protein (OR have a signal peptide). Exp number, first 60 AAs ( First60= ): The expected number of amino acids in transmembrane helices in the first 60 amino acids of the protein. If this number more than a few, you should be warned that a predicted transmembrane helix in the N- term could be a signal peptide. Total prob of N-in: The total probability that the N-term is on the cytoplasmic side of the membrane. POSSIBLE N-term signal sequence: a warning that is produced when "Exp number, first 60 AAs" is larger than 10. "PredHel=": The number of predicted transmembrane helices by N-best. "Topology=": The topology predicted by N-best. ('i' if the loop is on the inside or 'o' if it is on the outside)
9 i Nielsen H, Krogh A. Prediction of signal peptides and signal anchors by a hidden Markov model. Proc Int Conf Intell Syst Mol Biol. 1998;6: ii Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nature methods. 2011;8: iii Bendtsen JD, Jensen LJ, Blom N, Von Heijne G, Brunak S. Feature-based prediction of nonclassical and leaderless protein secretion. Protein Eng Des Sel. 2004;17: iv Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001;305:
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