NCSI: Computational Insight into Plant Hormone Signaling Mechanisms

Status	Completed
Mentor Name	Diwakar Shukla
Mentor's XSEDE Affiliation	None
Mentor Has Been in XSEDE Community	4-5 years
Project Title	Computational Insight into Plant Hormone Signaling Mechanisms
Summary	Strigolactones are a class of plant hormones that regulate shoot branching in flowering plants.They also stimulate germination in theStrigagenus of parasitic weeds, which destroy∼10 billionworth of crops annually. The current model of strigolactone signaling in plants entails binding ofthe strigolactone hormone to a receptor protein, D14, enzymatic hydrolysis of the hormone by D14,a conformational change of the D14-hormone complex, and association of D14 with a signalingpartner, D3/MAX2. Recent biochemical analyses have shown that an interaction between D14and D3/MAX2 inhibits the enzymatic activity of D14 toward the hormone. This project aims tounderstand the molecular mechanism by which D14 enzymatic activity inhibited by D3. Ultimately,an enhanced understanding of strigolactone signaling will aid the development of effective controlsfor witchweed and help improve food security in witchweed-vulnerable regions
Job Description	Student will perform molecular dynamics simulations to simulate the bicarbonate transport process. The student will perform analysis of high dimensional molecular simulation data through Markov state models and write a paper/report on research findings for publication in a scientific manuscript and/or oral/poster presentations.
Computational Resources	The student would work with the PI and a graduate student on a key problem in understanding perception of plant hormones and mechanisms of regulation of parasitic weeds. The simulations will be performed on the Blue Waters supercomputer hosted by NCSA at University of Illinois.
Contribution to Community
Position Type	Learner
Training Plan	Training Plan: Stage 1: Learn basics of molecular dynamics simulation, high performance computation, parallel programming. Stage 2: Conduct simulations. Stage 3: Process simulation data and construct Markov state models to describe the detailed dynamics.Stage 4: Identify potential mutations from simulations, bioinformatics, machine learning to enhance transport function.Stage 5: Organize results into a scientific manuscript and presentation. PI will meet with the students on a weekly basis to ensure successful execution of the training plan. Graduate student, Jiming Chen (Chemical & Biomolecular Engineering, University of Illinois) would work with the student on a daily basis to ensure effective training.
Student Prerequisites/Conditions/Qualifications
Duration	Summer
Start Date	05/16/2020
End Date	08/15/2020