Le, N. T. P. | Search bioRxiv

bioRxiv2025

Atomic Conformational Dynamics and Actin-Crosslinking Function of Alpha-Actinin Revealed by SimHS-AFMfit

Many molecular systems, such as intrinsically disordered proteins and flexible multi-domain complexes, are highly dynamic and often inaccessible to conventional X-ray crystallography or cryo-EM due to their conformational heterogeneity and flexibility. As a result, resolving their atomic-level dynamics remains a significant challenge. In this study, we present SimHS-AFMfit-MD, an integrative framework that combines high-speed atomic force microscopy (HS-AFM), molecular dynamics (MD) simulations, and AFMfit-based structural modeling to reconstruct dynamic protein conformations at atomic resolution. Using alpha-actinin, an actin crosslinking protein, as a challenging test system, we show that AFMfit guided by nonlinear normal mode analysis (AFMfit-NMA) enables accurate structural fitting, while guiding AFMfit with MD trajectories (AFMfit-MD) further enhances the flexible fitting performance, achieving closer agreement with unbiased all-atom MD simulation results. This strategy allows us to convert thousands of three-dimensional HS-AFM images into atomic-scale conformational ensembles, revealing the twisting and bending transitions underlying Ca{superscript 2}-bound and Ca{superscript 2}-unbound alpha-actinin. Together, our results establish a hybrid computational-experimental approach that bridges the spatial and, to some extent, temporal resolution gaps between simulation and imaging, paving the way for real-time visualization of protein conformational dynamics at the atomic scale. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/647477v3_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@147c1aforg.highwire.dtl.DTLVardef@1fd0036org.highwire.dtl.DTLVardef@118da3forg.highwire.dtl.DTLVardef@a062c3_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2025

Developing the Computational Image Processing Methodfor Quantitative Analysis of Nanopore Structure Obtained from HS-AFM (AFMnanoQ)

High-Speed Atomic Force Microscopy (HS-AFM) enables imaging of biological structures and dynamics with nanometer spatial and millisecond temporal resolution. AFM images contain three-dimensional (3D) surface information, comprising two-dimensional (2D) lateral (x-y) and one-dimensional (1D) height (z) encoded in pixel intensity. This dynamic structure poses significant challenges for instance boundary detection and morphological analysis. To address this, we develop AFMnanoSALQ, a feature-driven computational framework for semi-automatic labeling and quantitative (SALQ) detection and morphological measurement of HS-AFM data. Unlike conventional methods that rely solely on either visual or geometric features for 2D boundary detection, AFM- nanoSALQ integrates both to extract 3D morphology. It requires neither annotated data nor intensive training, enabling fast deployment at minimal cost. With performance comparable to typical deep-learning models, AFMnanoSALQ facilitates semi-automatic labeling, making it a practical tool for preliminary data inspection and accelerating the creation of training datasets. As a case study, we focus on -hemolysin (HL), a {beta}-barrel pore-forming toxin secreted by Staphylococcus aureus, using both synthetic and experimental AFM data. AFMnanoSALQ provides a foundation for future deep learning studies, enabling both dataset generation and cross-validation between feature-driven and data-driven approaches.

bioinformatics↗