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Salsabil Abou-Hatab¹, Cameron F Abrams¹

¹Department of Chemical and Biological Engineering, Drexel University, Philadelphia, Pennsylvania 19104-2816, United States.

The Journal of Physical Chemistry. B

|May 16, 2025

Summary

Identifying key protein movements is hard without detailed structures. This study finds a minimal set of collective variables (CVs) to guide steered molecular dynamics (SMD) simulations for observing protein conformational changes.

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Area of Science:

Biophysics
Computational Biology
Structural Biology

Background:

Observing protein conformational transitions is challenging with standard equilibrium molecular dynamics.
Enhanced sampling methods like Targeted Molecular Dynamics (MD) struggle without high-resolution structural data.
Low-resolution data can guide steered MD (SMD) simulations using collective variables (CVs), but optimal CV selection is unclear.

Purpose of the Study:

To identify a minimal set of CVs for successful protein conformational transitions.
To validate CVs using temperature-accelerated MD (TAMD) in the absence of target bias.
To demonstrate the utility of SMD simulations for predicting protein dynamics without high-resolution structures.

Main Methods:

Utilized steered molecular dynamics (SMD) simulations biased by collective variables (CVs).
Employed temperature-accelerated MD (TAMD) for unbiased acceleration of conformational changes.
Analyzed T4 lysozyme transitions using both large-scale (hinge bending) and small-scale (side-chain reorientation) CVs.

Main Results:

Identified a minimal set of CVs enabling successful transitions between metastable states in T4 lysozyme.
Found that both large-scale and small-scale CVs are essential for driving conformational changes.
Observed the breaking of a salt bridge (Arg8-Glu64) and reorientation of Phe4 as critical events for state stabilization.

Conclusions:

Appropriate CV selection and optimized steering protocols are crucial to prevent protein deformation during SMD simulations.
SMD simulations, guided by well-chosen CVs, can effectively predict protein conformational changes even without high-resolution structural data.
This approach provides a valuable predictive tool for understanding protein dynamics in low-resolution scenarios.