Literature DB >> 28019094

Open-pNovo: De Novo Peptide Sequencing with Thousands of Protein Modifications.

Hao Yang1,2, Hao Chi1, Wen-Jing Zhou1,2, Wen-Feng Zeng1,2, Kun He1,2, Chao Liu1, Rui-Xiang Sun1, Si-Min He1,2.   

Abstract

De novo peptide sequencing has improved remarkably, but sequencing full-length peptides with unexpected modifications is still a challenging problem. Here we present an open de novo sequencing tool, Open-pNovo, for de novo sequencing of peptides with arbitrary types of modifications. Although the search space increases by ∼300 times, Open-pNovo is close to or even ∼10-times faster than the other three proposed algorithms. Furthermore, considering top-1 candidates on three MS/MS data sets, Open-pNovo can recall over 90% of the results obtained by any one traditional algorithm and report 5-87% more peptides, including 14-250% more modified peptides. On a high-quality simulated data set, ∼85% peptides with arbitrary modifications can be recalled by Open-pNovo, while hardly any results can be recalled by others. In summary, Open-pNovo is an excellent tool for open de novo sequencing and has great potential for discovering unexpected modifications in the real biological applications.

Entities:  

Keywords:  de novo peptide sequencing; dynamic programming; tandem mass spectrometry; unexpected modifications

Mesh:

Substances:

Year:  2017        PMID: 28019094     DOI: 10.1021/acs.jproteome.6b00716

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  7 in total

1.  Precision De Novo Peptide Sequencing Using Mirror Proteases of Ac-LysargiNase and Trypsin for Large-scale Proteomics.

Authors:  Hao Yang; Yan-Chang Li; Ming-Zhi Zhao; Fei-Lin Wu; Xi Wang; Wei-Di Xiao; Yi-Hao Wang; Jun-Ling Zhang; Fu-Qiang Wang; Feng Xu; Wen-Feng Zeng; Christopher M Overall; Si-Min He; Hao Chi; Ping Xu
Journal:  Mol Cell Proteomics       Date:  2019-01-08       Impact factor: 5.911

Review 2.  Strategies in 'snake venomics' aiming at an integrative view of compositional, functional, and immunological characteristics of venoms.

Authors:  Bruno Lomonte; Juan J Calvete
Journal:  J Venom Anim Toxins Incl Trop Dis       Date:  2017-04-28

3.  pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework.

Authors:  Hao Yang; Hao Chi; Wen-Feng Zeng; Wen-Jing Zhou; Si-Min He
Journal:  Bioinformatics       Date:  2019-07-15       Impact factor: 6.937

4.  A high-speed search engine pLink 2 with systematic evaluation for proteome-scale identification of cross-linked peptides.

Authors:  Zhen-Lin Chen; Jia-Ming Meng; Yong Cao; Ji-Li Yin; Run-Qian Fang; Sheng-Bo Fan; Chao Liu; Wen-Feng Zeng; Yue-He Ding; Dan Tan; Long Wu; Wen-Jing Zhou; Hao Chi; Rui-Xiang Sun; Meng-Qiu Dong; Si-Min He
Journal:  Nat Commun       Date:  2019-07-30       Impact factor: 14.919

5.  Novel Cyclic Peptides from Lethal Amanita Mushrooms through a Genome-Guided Approach.

Authors:  Shengwen Zhou; Xincan Li; Yunjiao Lüli; Xuan Li; Zuo H Chen; Pengcheng Yuan; Zhu L Yang; Guohong Li; Hong Luo
Journal:  J Fungi (Basel)       Date:  2021-03-11

6.  SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions.

Authors:  Muhammad Usman Tariq; Fahad Saeed
Journal:  PLoS One       Date:  2021-10-29       Impact factor: 3.240

7.  Quick and clean: Cracking sentences encoded in E. coli by LC-MS/MS, de novo sequencing, and dictionary search.

Authors:  Lili Niu; Matthias Mann
Journal:  EuPA Open Proteom       Date:  2019-07-29
  7 in total

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