A start to something bigger :)
Over the last few weeks, I’ve spent a lot of time talking about MELD – the replica exchange molecular dynamics simulation that offers a physics-based perspective on protein folding, which current AI platforms cannot fully capture. I also mentioned how resource-intensive it is due to the need for GPU usage and a vast knowledge base. However, I was truly amazed by the ability of simulations to capture such an intricate process, and I wanted to figure out a way to run a simulation, understand its mechanics, and see what basic simulations can highlight.
After reaching out again to Mr. Bogetti, I learned about OpenMM, a Python toolkit for high-performance molecular simulations! OpenMM is more beginner-friendly and easier to understand. In the code I ran, the following components simulated my protein fragment’s movement in a heat bath at a set temperature:
- Loads a pre-prepared molecular system (Amber files).
-> These input and output files help run the simulation and contain information on topology (e.g., amino acids, bond angles, bond lengths), force field parameters, and the molecule’s coordinates over time.
- Sets up an implicit solvent model.
- Uses a Langevin thermostat at a specific temperature.
-> This thermostat maintains the desired temperature and controls how tightly the system is coupled to the heat bath.
- Minimizes the energy.
- Simulates molecular dynamics for 1 ns.
-> The simulation includes 500,000 steps, with each step being 2 femtoseconds. This results in a 1-nanosecond simulation.
- Outputs trajectory and system data.
-> The simulation captures the molecule’s coordinates every 5000 steps, producing 100 frames showing how the molecule changes over time.
Using this code, I ran a simulation on my 1–36 residue protein fragment of 1VXA at two temperatures: 300K and 450K.
300K is a commonly used temperature – it’s warm enough to allow natural movement, and it’s often used to observe molecular behavior in relatively stable environments. At 300K, the molecule moved quite a bit. While the alpha helices didn’t change drastically, the originally L-shaped molecule began to squiggle into curves not native to its topology.
Next, I ran the simulation at 450K. This is where I saw dramatic changes: the coils in the helices began to unravel, the molecule straightened out (though still squiggling), and the uncoiled regions even started to resemble beta sheets.
This experiment was a fascinating way to observe how the molecule behaves at different temperatures and under the influence of physical principles. While I didn’t have the capability to perform replica exchange or later cool down the molecule to see what it might have refolded into, this was still a fun, accessible spin-off (I could run it on my own CPU!) that showcases MELD’s potential and what we might integrate into AI models.
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