NCSI: Benchmarking of an Implicit Model with Electrostatic Interactions in the Membrane Environment

Status	Completed
Mentor Name	Rituparna Samanta
Mentor's XSEDE Affiliation	Research allocation
Mentor Has Been in XSEDE Community	4-5 years
Project Title	Benchmarking of an Implicit Model with Electrostatic Interactions in the Membrane Environment
Summary	Membrane proteins are proteins present in the cell membrane and are essential due to several factors: they are responsible for protecting the cells and keeping them healthy, drug targets and ion channels in membranes are critical to the nervous system. The presence of lipid layers makes it difficult to analyze their structures experimentally, in-silico analysis has emerged as a promising tool. However, unlike soluble proteins, membrane protein design remains challenging due to the presence of a complex and diverse lipid layer. The goal of this work is to build an implicit model which can include the electrostatic interaction due to the membrane environment for different lipid layers.
Job Description	The goal of the project for the student trainee would be to test the ability of the implicit model to capture membrane protein orientation, stability, sequence, and structure through various benchmark tests. The student has the following specific tasks: (i) Run existing benchmark tests and search for new tests from experimental literature. (ii)Analyze the results using their biophysics and physical chemistry skill sets. (iii)Compare the results with the previous models and cite areas of improvement for the model.
Computational Resources	The iterative nature of our project to continuously improve our implicit model makes it intractable without the use of high-performance computing facilities such as XSEDE. I am a postdoctoral student. As a group, we focus on developing methods to solve practical problems in areas spanning the structure prediction and functioning of antibodies, glycans, membrane-proteins, antibody-antigen complex, and protein-protein complexes. We do our calculations on an in-house computing cluster, XSEDE resources such as Johns Hopkins University's MARCC cluster along with stampede2.
Contribution to Community	With this model validated by various benchmark tests, we will have an optimized software suite to better capture orientation, stability, sequence, and structure instead of specialized functional models for membrane proteins. Through this opportunity, I will gain the experience of working with an undergraduate. On the other hand, the mentee would be introduced to concepts of high-performance computing, parallel computing, and scalability while applying to intriguing biological problems of protein design, bio-molecular simulation. The Empower fellowship would encourage the undergraduate student to not only attain their objective to participate in a computational biology research program but give them a chance to get to know the broader researches happening in the XSEDE community. I certainly believe this experience will help them in making an informed decision after graduation.
Position Type	Apprentice
Training Plan	The student will closely work with the mentor and have an individual meeting once a week. Additionally, they would have the chance to attend weekly group meetings where they can meet other graduate students, postdocs, and PI. They will have the opportunity to present their work to the entire research group once a semester and get their feedback on the science.
Student Prerequisites/Conditions/Qualifications	The student should have a working knowledge of Python and familiarity with C++ is a plus. The student will be provided with study material and video tutorials for learning Rosetta and PyRosetta.
Duration	Semester
Start Date	01/10/2022
End Date	05/15/2022