Deciphering conformational dynamics in AFM data using fast nonlinear NMA and FFT-based search with AFMFit.

Area of Science:

• Biophysics
• Structural Biology
• Computational Biology

Background:

• Atomic Force Microscopy (AFM) enables single-molecule studies of protein dynamics under near-physiological conditions.
• Interpreting 2D AFM data as 3D conformational dynamics of single molecules presents a significant challenge.
• Existing methods struggle with large datasets and high-speed AFM (HS-AFM) imaging.

Purpose of the Study:

• To develop a computational method for interpreting AFM data to reveal protein conformational dynamics.
• To create a flexible fitting procedure that deforms atomic models to match multiple AFM observations.
• To enable the analysis of larger AFM datasets, including those from HS-AFM.

Main Methods:

• AFMfit, a flexible fitting procedure, deforms input atomic models to match multiple AFM observations.
• Utilizes a fast fitting algorithm based on nonlinear Normal Mode Analysis (NMA) called NOLB.
• Processes hundreds of AFM images of a single molecule rapidly on a single workstation.

Main Results:

• The fitted models form a conformational ensemble that unambiguously describes the AFM experiment.
• AFMfit successfully associates each molecule with its conformational state.
• Demonstrated applications on synthetic and experimental AFM/HS-AFM data, including activated factor V and TRPV3.

Conclusions:

• AFMfit provides an efficient and robust method for analyzing single-molecule protein dynamics from AFM data.
• The open-source package facilitates the study of conformational ensembles and enables analysis of large-scale AFM datasets.
• This approach enhances the interpretation of AFM experiments for structural biology and biophysics research.