NPS: scoring and evaluating the statistical significance of peptidic natural product-spectrum matches.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
15 07 2019
Historique:
entrez: 13 9 2019
pubmed: 13 9 2019
medline: 13 6 2020
Statut: ppublish

Résumé

Peptidic natural products (PNPs) are considered a promising compound class that has many applications in medicine. Recently developed mass spectrometry-based pipelines are transforming PNP discovery into a high-throughput technology. However, the current computational methods for PNP identification via database search of mass spectra are still in their infancy and could be substantially improved. Here we present NPS, a statistical learning-based approach for scoring PNP-spectrum matches. We incorporated NPS into two leading PNP discovery tools and benchmarked them on millions of natural product mass spectra. The results demonstrate more than 45% increase in the number of identified spectra and 20% more found PNPs at a false discovery rate of 1%. NPS is available as a command line tool and as a web application at http://cab.spbu.ru/software/NPS. Supplementary data are available at Bioinformatics online.

Identifiants

pubmed: 31510666
pii: 5529154
doi: 10.1093/bioinformatics/btz374
pmc: PMC6612854
doi:

Substances chimiques

Biological Products 0
Peptides 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

i315-i323

Informations de copyright

© The Author(s) 2019. Published by Oxford University Press.

Références

J Comput Biol. 1999 Fall-Winter;6(3-4):327-42
pubmed: 10582570
Chem Rev. 1997 Nov 10;97(7):2651-2674
pubmed: 11851476
Anal Chem. 2003 Feb 1;75(3):435-44
pubmed: 12585468
Bioinformatics. 2004 Jun 12;20(9):1466-7
pubmed: 14976030
Nucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4
pubmed: 15608248
Anal Chem. 2005 Feb 15;77(4):964-73
pubmed: 15858974
Anal Chem. 2005 Jul 15;77(14):4626-39
pubmed: 16013882
J Proteome Res. 2005 Sep-Oct;4(5):1687-98
pubmed: 16212422
Nat Methods. 2007 Mar;4(3):207-14
pubmed: 17327847
J Proteome Res. 2008 Aug;7(8):3354-63
pubmed: 18597511
Mol Cell Proteomics. 2009 Jan;8(1):53-69
pubmed: 18703573
Bioinformatics. 2009 Jan 15;25(2):218-24
pubmed: 19015140
Nat Methods. 2009 Aug;6(8):596-9
pubmed: 19597502
Comb Chem High Throughput Screen. 2011 Jul;14(6):548-458
pubmed: 21521149
J Am Soc Mass Spectrom. 2011 Jul;22(7):1111-20
pubmed: 21953092
Chem Biol. 2012 Jan 27;19(1):85-98
pubmed: 22284357
Nat Prod Rep. 2013 Jan;30(1):108-60
pubmed: 23165928
Nucleic Acids Res. 2013 Jan;41(Database issue):D1130-6
pubmed: 23193280
J Am Soc Mass Spectrom. 2013 Feb;24(2):301-4
pubmed: 23292976
J Proteome Res. 2013 Apr 5;12(4):1560-8
pubmed: 23343606
J Am Soc Mass Spectrom. 1994 Nov;5(11):976-89
pubmed: 24226387
ACS Chem Biol. 2014 Jul 18;9(7):1545-51
pubmed: 24802639
Nature. 2014 May 29;509(7502):575-81
pubmed: 24870542
J Nat Prod. 2014 Aug 22;77(8):1902-9
pubmed: 25116163
Nat Commun. 2014 Oct 31;5:5277
pubmed: 25358478
Nature. 2015 Jan 22;517(7535):455-9
pubmed: 25561178
Chem Biol. 2015 Apr 23;22(4):460-471
pubmed: 25865308
Nat Chem Biol. 2015 Sep;11(9):625-31
pubmed: 26284661
Chem Biol. 2015 Sep 17;22(9):1259-69
pubmed: 26364933
Proc Natl Acad Sci U S A. 2015 Oct 13;112(41):12580-5
pubmed: 26392543
Proc Natl Acad Sci U S A. 2015 Oct 13;112(41):12549-50
pubmed: 26430243
J Cheminform. 2016 Feb 01;8:5
pubmed: 26839597
Anal Chem. 2016 Aug 16;88(16):7946-58
pubmed: 27419259
Nat Biotechnol. 2016 Aug 9;34(8):828-837
pubmed: 27504778
Nat Microbiol. 2016 Oct 31;2:16197
pubmed: 27798598
Nat Chem Biol. 2017 Jan;13(1):30-37
pubmed: 27820803
Nat Methods. 2018 Jan;15(1):53-56
pubmed: 29176591
Nat Microbiol. 2018 Mar;3(3):319-327
pubmed: 29358742

Auteurs

Azat M Tagirdzhanov (AM)

Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia.
Department of Higher Mathematics, St. Petersburg Electrotechnical University "LETI", St. Petersburg, Russia.

Alexander Shlemov (A)

Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia.

Alexey Gurevich (A)

Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

Yoan Martínez-López, Paulina Phoobane, Yanaima Jauriga et al.
1.00
Blood-Brain Barrier Machine Learning Humans Support Vector Machine Software
Cephalometry Humans Anatomic Landmarks Software Internet
Humans Algorithms Software Artificial Intelligence Computer Simulation

Classifications MeSH