Back

A comprehensive reference database to support untargeted metabolomics in Pseuudomonas putida

Ross, D. H.; Chang, C.; Vasquez, J.; Overstreet, R.; Schultz, K.; Metz, T.; Bade, J.

2026-03-24 bioinformatics
10.64898/2026.03.20.713193 bioRxiv
Show abstract

Pseudomonas putida strain KT2440 is a crucial model organism for synthetic biology and bioengineering applications, yet there currently exists no comprehensive metabolomics database comparable to those available for other model organisms. This gap hinders the use of untargeted metabolomics for exploratory analyses in this system. We developed the P. putida metabolome reference database (PPMDB v1) to address this limitation by consolidating metabolite information from multiple sources and expanding coverage through computational predictions. The database was constructed by curating metabolites from BioCyc, BiGG, and other literature sources, then computationally expanding this collection using BioTransformer environmental transformation predictions to generate additional predicted metabolites. We enhanced the databases utility for molecular annotation in metabolomics studies by incorporating analytical properties including collision cross-sections, tandem mass spectra, and gas-phase infrared spectra. These analytical properties were gathered from existing measurement data or predicted using computational tools. We further augmented the database through inclusion of reaction information and pathway annotations, facilitating biological interpretation of metabolomics data. This publicly available resource fills a critical gap in P. putida research infrastructure, supporting metabolite annotation and biological interpretation in untargeted metabolomics studies and enabling in-depth exploratory analyses of this important synthetic biology platform at the molecular level. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/713193v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@c8828forg.highwire.dtl.DTLVardef@1f3a5c5org.highwire.dtl.DTLVardef@1084535org.highwire.dtl.DTLVardef@1f7ca4a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 13 journals account for 50% of the predicted probability mass.

1
Water Research
74 papers in training set
Top 0.4%
6.5%
2
Metabolic Engineering
68 papers in training set
Top 0.1%
6.5%
3
Plant Direct
81 papers in training set
Top 0.3%
6.4%
4
Computational and Structural Biotechnology Journal
216 papers in training set
Top 0.5%
6.4%
5
ACS Synthetic Biology
256 papers in training set
Top 0.7%
4.9%
6
mSystems
361 papers in training set
Top 3%
3.7%
7
Environmental Science & Technology
64 papers in training set
Top 0.9%
3.1%
8
Frontiers in Microbiology
375 papers in training set
Top 3%
2.8%
9
Journal of Hazardous Materials
19 papers in training set
Top 0.3%
2.6%
10
Scientific Reports
3102 papers in training set
Top 47%
2.5%
11
Nature Communications
4913 papers in training set
Top 48%
1.9%
12
PLOS ONE
4510 papers in training set
Top 50%
1.9%
13
Metabolites
50 papers in training set
Top 0.4%
1.8%
50% of probability mass above
14
RSC Advances
18 papers in training set
Top 0.6%
1.7%
15
npj Systems Biology and Applications
99 papers in training set
Top 1%
1.7%
16
Science of The Total Environment
179 papers in training set
Top 3%
1.7%
17
Frontiers in Plant Science
240 papers in training set
Top 3%
1.7%
18
Journal of Chemical Information and Modeling
207 papers in training set
Top 2%
1.7%
19
Metabolomics
11 papers in training set
Top 0.2%
1.7%
20
Frontiers in Bioengineering and Biotechnology
88 papers in training set
Top 2%
1.5%
21
Analytical Chemistry
205 papers in training set
Top 2%
1.5%
22
iScience
1063 papers in training set
Top 21%
1.2%
23
ACS Omega
90 papers in training set
Top 2%
1.2%
24
Synthetic and Systems Biotechnology
10 papers in training set
Top 0.3%
1.1%
25
Scientific Data
174 papers in training set
Top 2%
1.0%
26
PLOS Computational Biology
1633 papers in training set
Top 21%
1.0%
27
Microbiology Spectrum
435 papers in training set
Top 4%
0.9%
28
Algal Research
20 papers in training set
Top 0.3%
0.9%
29
Bioinformatics
1061 papers in training set
Top 9%
0.9%
30
Journal of the American Society for Mass Spectrometry
33 papers in training set
Top 0.4%
0.8%