Collection of Data Variation Using a High-Throughput Image-Based Assay Platform Facilitates Data-Driven Understanding of TRPA1 Agonist Diversity
, Kenjiro Tanaka 2,†, Minami Matsuyama 1, Masaya Fujitani 2, Masatoshi Shibuya 2, Yoshihiko Yamamoto 2, Ryuji Kato 2,3,‡ and Keisuke Ito 1,*Round 1
Reviewer 1 Report
The work presented by Terada et al., describes a library data collection based on “image-based TRPA1assay system combined with in silico chemical space clustering concept.” However, no significantly evidences of the relevance of TRPA1 were highlight in the manuscript. it should be also necessary include novel advantages of this new approache vs. database previous.
Although the topic described could be of great interest to highlight the use of database that could help to identify TRPA1-targeting drug, it is necessary describe the relevance of this target in the physiological function.
Author Response
Response to Reviewer 1 Comments
We wish to express our appreciation to the Reviewer for his or her insightful comments, which have helped us significantly improve the paper.
Point 1: The work presented by Terada et al., describes a library data collection based on “image-based TRPA1assay system combined with in silico chemical space clustering concept.” However, no significantly evidences of the relevance of TRPA1 were highlight in the manuscript. It should be also necessary include novel advantages of this new approache vs. database previous.
Response to point 1: In this study, TRPA1 was selected as a model receptor, because TRPA1 is an important target of drug discovery with multiple interaction mechanisms. Our approach “image-based TRPA1 assay combined with in silicochemical space clustering concept” enabled the collection of new TRPA1 agonist interaction data that can provide new insights for TRPA1 agonists from the perspective of physicochemical molecular properties. This approach can be applied to other receptors and enzymes to investigate interaction mechanisms with ligands and substrates. We have added these sentences to lines 74 to 76, and 347 to 349 in the revised manuscript.
Our approach presents a cost- and labor-efficient concept to collect wider variations of agonist candidates within the limited assay number compared to the straight forward exhaustive library screening, since it can expand the candidate variation in prior in silico. We have added this sentence to lines 76 to 79 in the revised manuscript.
Point 2: Although the topic described could be of great interest to highlight the use of database that could help to identify TRPA1-targeting drug, it is necessary describe the relevance of this target in the physiological function.
Response to point 2: Because TRPA1 is involved in various physiological functions, such as energy metabolism, insulin secretion, and vasodilation, TRPA1-targeting drugs to treat obesity, diabetes, atherosclerosis, and others, have been energetically developed in pharmaceutical companies. Physiological functions of TRPA1 and medicinal effects of TRPA1-targeting drugs have shown in lines 54 to 59, and 87 to 90 in the revised manuscript.
Reviewer 2 Report
In this manuscript, Terada et al. utilized the image-based protein channel protein TRPA1 activity assay and chemical space clustering concept to understand the structure-activity relationship of TRPA1 agonists. The authors successfully identified several groups of substances with alkyne, amide, and ester moieties as potential highly active agonists. This study covers an intersting topic and it fits well the scope of the journal of Applied Sciences. The referee supports its acceptance with minor revisions noted.
- Please include some comments about how the solubility of substances/chemicals affects the agonist activity. For the N1=32 identified in the first assay, discuss possible approaches for obtaining these compounds' activities.
- Please highlight the important chemical moieties illustrated in Figure 4 b-d.
Author Response
Response to Reviewer 2 Comments
We wish to express our appreciation to the Reviewer for his or her insightful comments, which have helped us significantly improve the paper.
In this manuscript, Terada et al. utilized the image-based protein channel protein TRPA1 activity assay and chemical space clustering concept to understand the structure-activity relationship of TRPA1 agonists. The authors successfully identified several groups of substances with alkyne, amide, and ester moieties as potential highly active agonists. This study covers an intersting topic and it fits well the scope of the journal of Applied Sciences. The referee supports its acceptance with minor revisions noted.
Point 1: Please include some comments about how the solubility of substances/chemicals affects the agonist activity. For the N1=32 identified in the first assay, discuss possible approaches for obtaining these compounds' activities.
Response to point 1: Because the TRPA1-expressing cells were seeded on the bottom of 96-well plate, test compounds need to be dissolved in the measuring buffer to reach to the cells. Derivatization is a possible approach to improve solubility of N1 compounds. We have added these sentences to lines 126 to 129.
Point 2: Please highlight the important chemical moieties illustrated in Figure 4 b-d.
Response to point 2: In Figure 4b and c, we have highlighted chemical moieties that can contribute to TRPA1 activity. We have added the sentence “Chemical moieties that can contribute to TRPA1 activity of the compounds are highlighted in pink (b and c).” to lines 456 to 457. In Figure 4d, it was difficult to determine moieties that are important for their TRPA1 activity.
