Membrane Protein Identification: N-Terminal Labeling of Nontryptic Membrane Protein Peptides Facilitates Database Searching

2008 ◽  
Vol 7 (2) ◽  
pp. 659-665 ◽  
Author(s):  
Maria Jansson ◽  
Kristofer Wårell ◽  
Fredrik Levander ◽  
Peter James
Keyword(s):  
Membrane Protein ◽  
Protein Identification ◽  
Database Searching

scholarly journals Prediction of structural features and application to outer membrane protein identification

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Renxiang Yan ◽  
Xiaofeng Wang ◽  
Lanqing Huang ◽  
Feidi Yan ◽  
Xiaoyu Xue ◽  
...  
Keyword(s):  
Membrane Protein ◽  
Outer Membrane ◽  
Outer Membrane Protein ◽  
Protein Identification ◽  
Structural Features

Protein Identification with a Single Accurate Mass of a Cysteine-Containing Peptide and Constrained Database Searching

2000 ◽  
Vol 72 (6) ◽  
pp. 1112-1118 ◽  
Author(s):  
David R. Goodlett ◽  
James E. Bruce ◽  
Gordon A. Anderson ◽  
Beate Rist ◽  
Ljiljana Pasa-Tolic ◽  
...  
Keyword(s):  
Protein Identification ◽  
Accurate Mass ◽  
Database Searching

Membrane Protein Identification via Multiple Kernel Fuzzy SVM

2021 ◽  
pp. 634-646
Author(s):  
Weizhong Lu ◽  
Jiawei Shen ◽  
Yuqing Qian ◽  
Hongjie Wu ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Membrane Protein ◽  
Protein Identification ◽  
Multiple Kernel

Enhancing Membrane Protein Identification Using a Simplified Centrifugation and Detergent-Based Membrane Extraction Approach

2018 ◽  
Vol 90 (4) ◽  
pp. 2434-2439 ◽  
Author(s):  
Yanting Zhou ◽  
Jing Gao ◽  
Hongwen Zhu ◽  
Jingjing Xu ◽  
Han He ◽  
...  
Keyword(s):  
Membrane Protein ◽  
Protein Identification ◽  
Membrane Extraction

AN ITERATIVE ALGORITHM TO QUANTIFY FACTORS INFLUENCING PEPTIDE FRAGMENTATION DURING TANDEM MASS SPECTROMETRY

2007 ◽  
Vol 05 (02a) ◽  
pp. 297-311 ◽  
Author(s):  
CHUNGONG YU ◽  
YU LIN ◽  
SHIWEI SUN ◽  
JINJIN CAI ◽  
JINGFEN ZHANG ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Iterative Algorithm ◽  
Protein Identification ◽  
Peptide Fragmentation ◽  
Peptide Sequence ◽  
Experimental Result ◽  
Theoretical Spectrum ◽  
Database Searching ◽  
Tandem Mass

In protein identification by tandem mass spectrometry, it is critical to accurately predict the theoretical spectrum for a peptide sequence. To date, the widely-used database searching methods adopted simple statistical models for predicting. For some peptide, these models usually yield a theoretical spectrum with a significant deviation from the experimental one. In this paper, in order to derive an improved predicting model, we utilized a non-linear programming model to quantify the factors impacting peptide fragmentation. Then, an iterative algorithm was proposed to solve this optimization problem. Upon a training set of 1803 spectra, the experimental result showed a good agreement with some known principles about peptide fragmentation, such as the tendency to cleave at the middle of peptide, and Pro's preference of the N-terminal cleavage. Moreover, upon a testing set of 941 spectra, comparison of the predicted spectra against the experimental ones showed that this method can generate reasonable predictions. The results in this paper can offer help to both database searching and de novo methods.

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