Next Article in Journal
Co-Expression Network and Integrative Analysis of Metabolome and Transcriptome Uncovers Biological Pathways for Fertility in Beef Heifers
Previous Article in Journal
Human Hepatocyte 4-Acetoxy-N,N-Diisopropyltryptamine Metabolite Profiling by Reversed-Phase Liquid Chromatography Coupled with High-Resolution Tandem Mass Spectrometry
Previous Article in Special Issue
Convolutional Neural Network-Based Compound Fingerprint Prediction for Metabolite Annotation
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Metabolites
Volume 12
Issue 8
10.3390/metabo12080706
Review Report
metabolites-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
Article
Peer-Review Record

NPvis: An Interactive Visualizer of Peptidic Natural Product–MS/MS Matches

Metabolites 2022, 12(8), 706; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12080706
by Olga Kunyavskaya, Alla Mikheenko and Alexey Gurevich *
Reviewer 1:
Kun Qian
Reviewer 2: Anonymous
Metabolites 2022, 12(8), 706; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12080706
Submission received: 16 June 2022 / Revised: 22 July 2022 / Accepted: 27 July 2022 / Published: 29 July 2022
(This article belongs to the Special Issue Computational Methods for Secondary Metabolite Discovery Volume 2)

Round 1

Reviewer 1 Report

This article provided a visualizer for peptidic natural products (PNP), to explore and validate PNP-spectrum matches. The authors provided a figure for their visualizer and designed some integration with metabolic platforms. In general, the strengths of their visualizer compared with others can be provided. Besides, the matching method for PNP is missing in the manuscript, which is the core of NPvis. Below are the detailed suggestions and questions.

 

1.     The definition of PNP (represents a medically-important class of secondary metabolites) means that PNP belongs to metabolites. The authors compared it with proteomic tools in the introduction, while the comparison with metabolic tools was missing.

2.     How did the authors solve the error-prone problem compared with the conventional in silico identification method?

3.     What is the matching algorithm used in NPvis? Is this method specially designed for NPvis and targeted at PNP, which has cyclic, branched, or more sophisticated structures, contains non-proteinogenic amino acids and complex post-translational modifications, and includes non-amide linkages of amino acids?

4.     Adances on mass spectrometry (VIEW 2020, 1:20200063; Angew.Chem.Int.Ed. 2021, 60, 12504; Angew.Chem.Int.Ed. 2021, 60, ‍11310) and machine learning (Angew.Chem.Int.Ed. 2020, 59, 10831; Nature Communications 2020, 11, 3556; Advanced Science 2020, 7, 2002021) should be included.

5.     The novelty of this manuscript should be enhanced on the interactive visualizer. For example, what is the comparison with other metabolic visualizers? What are NPvis’ strengths compared with other metabolic visualizers? Is there any other novelties of NPvis?

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

please see attached file

Comments for author File: Comments.docx

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The paper can be accepted.

Back to TopTop