Molecular dynamics (MD) simulations have emerged as a powerful method to study a wide range of biophysical phenomena at atomic resolution. However, MD simulations are limited in their ability to effectively explore phase space in phenomena such as nucleation and protein conformational changes typically classified as “rare events.” These events occur at long time scales due to the presence of a high free energy barrier between two different stable states of a system. Conformational changes in proteins follow a minimum free energy path (MFEP), whereby the energy required to transition between states is minimized. The string method, an energy minimization technique, has been widely used in MFEP calculations where the perpendicular component of the force along with the co-ordinate transformation tensor is iteratively minimized to locate the MFEP. Here, we propose a new and robust method to determine the MFEP, which can easily be implemented in software such as GROMACS and PLUMED. Our approach does not rely on the force and expensive tensor calculation traditionally used in the string method. We instead follow the mean values in collective variable space, obtained from the harmonically biased sampling at each point of the string to converge on the MFEP. We validate our approach and algorithms using the Müller-Brown potential, followed by a study of the conformational landscape of alanine-dipeptide in a vacuum where results are compared with well-tempered metadynamics. Additionally, we implemented the proposed method to capture a complex secondary structure transition of the G-protein from an α-helix to a β-sheet in an explicit solvent with the help of path collective variables (PCVs). In all the different cases, our approach is able to locate the MFEP with great accuracy obviating the need to iteratively evaluate the tensor during the evolution of the string.

New finite temperature string method approach to obtain a minimum free energy path in collective variables space

ACS Spring 2022

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