Back

Identification of binding interaction between Curcumin derivative and PERK13 Proline rich receptor like protein kinase protein using In silico docking techniques - to help plants tackle salt water

Vaithyanathan, P. A.

2021-10-16 bioinformatics
10.1101/2021.10.15.464501 bioRxiv
Show abstract

IntroductionArabidopsis thaliana, mouse ear cress or thale cress are small flowering plants included in the cruciferae family. They comprise of various characteristics such as diploid genetics, small genome size, rapid growth cycle, and relatively low repetitive DNA content, making it a perfect model for plant genome projects. ObjectiveThe aim of this present Insilico research study is to carry out molecular drug docking studies between PERK13-Proline rich receptor like protein kinase and De O Acetylated Curcumin Di Galactose or di-galactosylated curcumin, a derivative of curcumin. PERK13 protein is considered to play a significant role in helping plants tackle salinity levels in water. MethodsIn this study, protein modelling tools and servers are used to model the 3D structure and the same is validated using Protein structure validation tools. Automated drug docking servers were used to dock the modelled protein with the chemical compound to analyse the electrostatic (H bond) interaction between PERK13 and De O Acetylated Curcumin Di Galactose, a better water-soluble compound than curcumin. The docked structure was visualized using an advanced molecular visualization tool. Results and DiscussionThe overall results obtained from this study on De O Acetylated Curcumin Di Galactose and PERK13 protein shows that De O Acetylated Curcumin Di Galactose, directly binds with the active site and other potentially binding regions of PERK13. Hence, it is concluded that De O Acetylated Curcumin Di Galactose could potentially play a vital role in future research related to the problem of helping the plants tackle increased saline levels in water.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.