A Day in my Life!

Srikanya g -

Inspired by other ‘Day in the Life’ blogs, I wanted to show you a day in my life at research!

Starting around 9:30 AM, I carpool with a friend to the lab to save gas. Depending on traffic, we usually get to lab by 10:15. Kicking off the day with a friend is always refreshing!

I begin by meeting with either my PhD mentor, Nikhil, or my advisor, Dr. Banu Ozkan, to discuss questions, new perspectives, or technical challenges. These conversations, often lasting 30 minutes to an hour, sometimes expand into broader scientific discussions. Recently, we debated whether protein folding conformations differ when both ends of a sequence can fold versus just one, as in natural protein synthesis, where amino acids emerge from a single ribosomal site. After some research, we confirmed that in vitro studies still offer valuable insights into this process.

Next, I settle into my office for some reading – either new papers from my advisor, articles I’ve found, or key sections I want to revisit. I’ve learned that I absorb information best earlier in the day, so I prioritize reading in the morning. A recent paper on molecular dynamic simulations helped me appreciate how forces like hydrogen bonding, van der Waals interactions, and electrostatic forces shape protein folding – something AI-driven models often overlook. One key takeaway: even before secondary structures form, early-stage proteins still maintain native-like topology.

Around lunchtime, I meet friends at the Memorial Union and debate between Chick-fil-A, Burger King, or the new Indian restaurant. Stepping away from the screen is a great mental reset.

In the afternoon, I either test new modeling software or research upcoming tools. This week, I focused on S4PRED and how proline’s role as a helix breaker or maker may have influenced my previous structure predictions. I also prepped for MELD, a molecular dynamics tool that incorporates physical forces into folding simulations. Next week, I’ll use MELD to model protein 1VXA and compare its results with AI-based predictions that lack these physical constraints.

Before wrapping up, I check in with my mentors to review my progress and plan next steps. Then, I head home and take an amazing nap.

That’s about it! Thanks for reading.

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    zara_f
    Hi Srikanya! Your schedule sounds so fun. Did it help to study hydrogen bonding and van der Waals interaction in Chemistry before, or did this require more in-depth knowledge? I'm looking forward to learning more!
    kaitlyn_p
    Hi Srikanya! When comparing MELD and AI-based models, do AI models have any strengths over MELD, or is MELD just better because it can account for the forces?
    srikanya_g
    Hi Katie! Thanks for your question! The strength of AI Models is computation time, they are much quicker than simulation-based models, however MELD is extremely useful for it's ability to account for physical forces.
    srikanya_g
    Hi Zara! Thanks for the question! Knowing those forces from chemistry was quite useful as I'm able to understand how residues can act based off of their properties, but since each residue is widely different and there are other traits to account for, there was a lot of individual research to accomplish.

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