Drexel Researchers Develop Simpler Way to Predict Protein Shape-Shifting

A new study from Drexel University introduces a simplified method for simulating how proteins change shape, using only limited experimental data. This approach, demonstrated through a detailed case study of the bacterial enzyme T4 lysozyme, could help scientists investigate proteins that are difficult to fully visualize, a common challenge in structural biology and drug development.

The research was published in The Journal of Physical Chemistry B by Salsabil Abou-Hatab, a postdoctoral researcher in the Department of Chemical and Biological Engineering, working with Cameron Abrams, PhD, Bartlett-Barry Professor and department head.

Proteins often switch between different shapes to perform essential functions such as breaking down molecules or transmitting signals. Capturing these transitions through computer simulations usually requires detailed, high-resolution structural data, which is not always available.

To overcome this limitation, the Drexel researchers used two enhanced sampling techniques: steered molecular dynamics and temperature accelerated molecular dynamics. These methods allowed them to drive the T4 lysozyme through its conformational change using only a small number of targeted features. Rather than relying on a complete structural model, they identified a minimal set of just four key features, known as collective variables, that were sufficient to reproduce the full transition between the enzyme’s open and closed states.

These variables captured both large-scale movements of the protein and smaller, local shifts, such as the breaking of a stabilizing salt bridge and the rotation of a single side chain into a hydrophobic pocket. The study showed that these same variables could also drive the transition in reverse, and that they remained effective even when the simulation was not directed toward a known final structure.

The work stands out not just for its technical achievement, but for the way it establishes a practical framework through a well-characterized system. T4 lysozyme, with its well-documented structural states, served as an ideal model for proving that complex protein behavior can be predicted using minimal inputs in a steered MD simulation. TAMD was then used as a validation tool to support the choice of collective variables.

“This study shows that with the right choice of features and this simple enhanced sampling technique, we can get powerful insight into how proteins move, without needing a perfect picture of the whole process,” said Abou-Hatab.

By validating the method through the T4 lysozyme case study, the researchers have laid the groundwork for applying this strategy to other proteins where structural data is incomplete. The approach may offer a faster and more accessible tool for understanding protein behavior and accelerating therapeutic design.