鸟枪法蛋白质学的介绍ppt课件

1、ADVANCEMENT IN PROTEIN INFERENCE FROM SHOTGUNPROTEOMICS USING PEPTIDE DETECTABILITY,1,Overview,Shotgun Proteomics Protein Inference Problem Protein Identification Using Peptide Detectability Results Limitations and Improvements,2,3,4,Degenerate Peptides,Rat Sample/Rat IPI Database,60%,Nesvizhskii, A

2、.I. and Aebersold, R. (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell Proteomics, 4, 14191440.,5,Protein Inference Problem,Solution 1 * (A, E),*,Solution 2 * (B, C, D),*,*,*,*,Minimum Protein Set,11 Possible Solutions,Nesvizhskii, A.I. and Aebersold, R. (200

3、5) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell Proteomics, 4, 14191440.,6,Identified Peptides,1 2 3 4 5 6 7 8 9 10,Proteins,1 2 3,4 2 6,1 2 3,5 4 1 7 8,10 9 6,6 9,10,GMPSA,5,3,3,1,3,2,3,2,2,2,0,0,1,Greedy Minimum Protein Set Algorithm,Nesvizhskii, A.I. and Aebe

4、rsold, R. (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell Proteomics, 4, 14191440.,7,Resolving Ambiguity,detectability of a peptide the probability that the peptide will be observed in a standard sample analyzed by a standard proteomics routine,Tang, H., Arn

5、old, R. J., Alves, P., Xun, Z., Clemmer, D. E., Novotny, M. V., Reilly, J. P. & Radivojac, P. (2006). A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics, (2006) 22 (14): e481-e488,8,Factors affecting Peptide Detection,Four classes

6、of factors,Chemical properties of the peptide (and parent protein) Limitations of peptide identification protocol Abundance of the peptide in the sample Presence of other peptides that compete for detection,Mean Accuracy :71% Mean AUC :78%,Synthetic : 30% of peptides identified Real :10% of peptides

7、 identified,Peptide Detectability Prediction,9,Identified Peptides,1 2 3 4 5 6 7 8 9 10,Proteins,Minimum Missed Peptides,12 45 4 2 6,1 4 5 7 8,6 9 27,10 24 53 23,17 55 6 9 10,14 1 17 2 3,1 2 15 3 24,0,1,Missed peptide,MDAP,10,Identified Peptides,1 2 3 4 5 6 7 8 9 10,Proteins,LDFA,12 45 4 2 6,1 4 5 7

8、 8,6 9 27,10 24 53 23,17 55 6 9 10,1 4 5 7 6 2 9 8 10 3,14 1 17 2 3,1 2 15 3 24,2,1,0,2,1,0,11,RESULTS,GMPSA,LDFA,Synthetic Sample with 12 Proteins,7 correct proteins,10 correct proteins,5 tied proteins,1 tied protein,1 incorrect tied protein,12,GMPSA vs LDFAin a R. norvegicus sample,GMPSA,LDFA,Rat

9、Sample/Rat IPI Database,2346,94,Indistinguishable pairs,13,GMPSA vs LDFA,GMPSA,LDFA,247,275,Total proteins identified,62%,81%,Percent of proteins assigned with no ties,153,224,Total assignments with no ties,149,Proteins assigned due to unique peptides,4,75,Total unambiguous assignments excluding the

10、 proteins with unique peptides,Identified Proteins,Unambiguously Identified Proteins,14,Limitations and Improvements,Include missed-cleavage peptides Include lower scoring peptides to aid in the differentiation of tied proteins Include peptides identified with charges +1 and +3 Train on other analyt

11、ical platforms Study the effects of detectability prediction on algorithm results,15,Publications,PSB 2007 Alves, P. , Arnold, R. , Novotny, M. , Radivojac, P. , Reilly, J. , Tang, H. (2007). Advancement in Protein Inference from Shotgun Proteomics Using Peptide Detectability. Pac. Symp. Biocomput., (2007) 12: 409-420 ISMB 2006 Tang, H., Arnold, R. J., Alves, P., Xun, Z., Clemmer, D. E., Novotny, M. V., Reilly, J. P. & Radivojac, P. (2006). A computational approach toward label-free protein quantification using