Unraveling Biomolecular Dynamics: A New Computational Tool for Analyzing Protein Movements from AFM Images

Depiction of Protein Molecules and Molecular Structures in Dynamic Biological Interactions ©克杜 - stock.adobe.com

The ability to observe the intricate dynamics of biomolecules is critical for understanding their function in various biological processes. High-speed atomic force microscopy (HS-AFM) has long provided a unique window into these movements, offering valuable insights by capturing images of protein structures in near-native environments. AFM typically uses a visible (670 nm) or shortwave near-infrared laser (from 780 to 980 nm) to detect the deflection of the AFM cantilever as it interacts with the sample. However, the AFM technique has faced significant limitations, most notably its inability to resolve atomic-level details due to the size of the AFM cantilever tip. To address this challenge, a team of researchers from Nagoya University and the RIKEN Center for Computational Science has developed an innovative algorithm that enhances the ability to model molecular dynamics from HS-AFM images (1,2).

The study was conducted by Xuan Wu, Osamu Miyashita, and Florence Tama from the Department of Physics, Graduate School of Science, Nagoya University, Japan; the RIKEN Center for Computational Science, Japan; and the Institute of Transformative Bio-Molecules, Nagoya University, Japan (1).

New Computational Tool
The newly proposed algorithm, normal mode flexible fitting-atomic force microscopy (NMFF-AFM), utilizes normal-mode analysis (NMA) to predict and model the conformational transitions of biomolecules from AFM images. The method capitalizes on NMA’s ability to describe molecular motions using a limited number of coordinates, offering an elegant solution to the resolution problem inherent in HS-AFM imaging. By integrating this approach, the NMFF-AFM algorithm not only improves the accuracy of the derived models but also reduces the risk of overinterpretation, which has been a long-standing issue with AFM data (1).

"Normal-mode analysis enables us to capture the large-scale, low-frequency motions of biomolecules, which are often the most functionally significant," the researchers noted in their paper. This unique capability makes NMFF-AFM particularly well-suited for studying conformational dynamics at an atomic level, a feat that traditional AFM imaging techniques have struggled to achieve (1).

Applications and Demonstrations
In their study, the researchers applied NMFF-AFM to synthetic data sets from three proteins: adenylate kinase (AKE), elongation factor 2 (EF2), and the ABCB1 transporter. These proteins were chosen due to their significant conformational changes during function. The results demonstrated the algorithm's ability to accurately model the structural dynamics of these proteins, highlighting its potential as a valuable tool for biophysical research (1).

One of the critical strengths of the NMFF-AFM algorithm is its flexibility and ease of use. Unlike previous methods that required extensive manual input and intervention, NMFF-AFM automates much of the modeling process, streamlining the workflow for researchers. This makes the tool not only faster but also accessible to a wider audience of scientists, potentially broadening its adoption in the field (1).

Read More: Applications of AFM

Overcoming AFM Limitations
HS-AFM has revolutionized the way scientists observe molecular motion, offering unprecedented insights into protein dynamics. However, its limitations, such as the inability to capture detailed 3D structures, have been a significant obstacle. To address this, previous attempts have included rigid-body fitting and coarse-grained modeling approaches. These methods, while valuable, lacked the atomic-level detail necessary for a complete understanding of biomolecular function (1).

The NMFF-AFM algorithm represents a hybrid approach, combining molecular mechanics simulations with experimental AFM data. This allows researchers to derive atomistic models of protein motion even when the available data is limited. "By focusing on low-frequency, large-scale movements, we can minimize the number of adjustable parameters and avoid the pitfalls of overfitting," the team wrote (1).

Future Directions and Challenges
While the NMFF-AFM algorithm has shown great promise in synthetic data sets, the researchers acknowledge that further refinement is needed before it can be widely applied to experimental data. One of the primary challenges lies in the uncertainty surrounding the tip shape and size of the AFM cantilever, which directly affects image resolution. Future studies will need to address this issue by improving methods for estimating tip geometry (1).

Additionally, while the algorithm works well for capturing hinge-type motions, it is less effective for more complex movements such as domain sliding or local unfolding. To overcome these limitations, the team suggests combining NMFF-AFM with other modeling techniques, such as molecular dynamics simulations, for a more comprehensive analysis (1).

Conclusion
The NMFF-AFM algorithm is a significant advancement in the field of biophysics, providing researchers with a powerful new tool for studying protein dynamics from AFM images. By leveraging normal-mode analysis, the algorithm overcomes the limitations of traditional AFM imaging techniques, offering atomic-level insights into biomolecular conformational transitions. As the team continues to refine the method, NMFF-AFM holds the potential to transform our understanding of protein function and dynamics, making it an invaluable asset for future research (1,2).

References

(1) Wu, X.; Miyashita, O.; Tama, F. Modeling Conformational Transitions of Biomolecules from Atomic Force Microscopy Images using Normal Mode Analysis. J. Phys. Chem. B 2024, Published September 25, 2024, XXXX, XXX, XXX-XXX. DOI:10.1021/acs.jpcb.4c04189

(2) Carapeto, A. P.; Marcuello, C.; Faísca, P. F.; Rodrigues, M. S. Morphological and Biophysical Study of S100A9 Protein Fibrils by Atomic Force Microscopy Imaging and Nanomechanical Analysis. Biomolecules 2024, 14 (9), 1091. DOI: 10.3390/biom14091091