Literature DB >> 16793527

Software for computational peptide identification from MS-MS data.

Changjiang Xu1, Bin Ma.   

Abstract

Protein identification in biological samples is an important task in drug discovery research. Protein identification is nowadays regularly performed by tandem mass spectrometry (MS-MS). Because of the difficulty of measuring intact proteins using MS-MS, typically a protein is enzymically digested into peptides and the MS-MS spectrum of each peptide is measured. Computational methods are then invoked to identify the peptides, which are later combined together to identify the protein. The most recognized peptide identification software packages can be classified into four categories: database searching, de novo sequencing, sequence tagging and consensus of multiple engines.

Mesh:

Substances:

Year:  2006        PMID: 16793527     DOI: 10.1016/j.drudis.2006.05.011

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  16 in total

1.  Investigation of VUV Photodissociation Propensities Using Peptide Libraries.

Authors:  Xiaohui Liu; Yong Fuji Li; Brian C Bohrer; Randy J Arnold; Predrag Radivojac; Haixu Tang; James P Reilly
Journal:  Int J Mass Spectrom       Date:  2011-12-01       Impact factor: 1.986

2.  Enhanced electron transfer dissociation of peptides modified at C-terminus with fixed charges.

Authors:  Byoung Joon Ko; Jennifer S Brodbelt
Journal:  J Am Soc Mass Spectrom       Date:  2012-08-16       Impact factor: 3.109

3.  An empirical strategy for characterizing bacterial proteomes across species in the absence of genomic sequences.

Authors:  Joshua E Turse; Matthew J Marshall; James K Fredrickson; Mary S Lipton; Stephen J Callister
Journal:  PLoS One       Date:  2010-11-12       Impact factor: 3.240

4.  Improved sequence tag generation method for peptide identification in tandem mass spectrometry.

Authors:  Xia Cao; Alexey I Nesvizhskii
Journal:  J Proteome Res       Date:  2008-09-12       Impact factor: 4.466

5.  A new probabilistic database search algorithm for ETD spectra.

Authors:  Rovshan G Sadygov; David M Good; Danielle L Swaney; Joshua J Coon
Journal:  J Proteome Res       Date:  2009-06       Impact factor: 4.466

Review 6.  Proteomics and the analysis of proteomic data: 2013 overview of current protein-profiling technologies.

Authors:  Can Bruce; Kathryn Stone; Erol Gulcicek; Kenneth Williams
Journal:  Curr Protoc Bioinformatics       Date:  2013-03

7.  Aligning the proteome and genome of the silkworm, Bombyx mori.

Authors:  Yaozhou Zhang; Qingyou Xia; Jie Xu; Jian Chen; Zuoming Nie; Dan Wang; Wenping Zhang; Jianqing Chen; Qingliang Zheng; Qing Chen; Lingying Kong; Xiaoyuan Ren; Jiang Wang; Zhengbing Lv; Wei Yu; Caiying Jiang; Lili Liu; Qing Sheng; Yongfeng Jin; Xiangfu Wu
Journal:  Funct Integr Genomics       Date:  2009-06-16       Impact factor: 3.410

8.  A critical appraisal of techniques, software packages, and standards for quantitative proteomic analysis.

Authors:  Faviel F Gonzalez-Galarza; Craig Lawless; Simon J Hubbard; Jun Fan; Conrad Bessant; Henning Hermjakob; Andrew R Jones
Journal:  OMICS       Date:  2012-07-17

9.  Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry.

Authors:  Sven H Giese; Ludwig R Sinn; Fritz Wegner; Juri Rappsilber
Journal:  Nat Commun       Date:  2021-05-28       Impact factor: 17.694

10.  Integration of proteomics, bioinformatics, and systems biology in traumatic brain injury biomarker discovery.

Authors:  J D Guingab-Cagmat; E B Cagmat; R L Hayes; J Anagli
Journal:  Front Neurol       Date:  2013-05-31       Impact factor: 4.003

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.