An official website of the United States government
The translocation of specific polypeptide chains across membranes is an essential activity for all life forms. The main components of the general secretory (Sec) system of E. coli include integral membrane translocon SecYEG, peripheral ATPase SecA, and SecDF, an ancillary complex that enhances polypeptide secretion by coupling translocation to proton motive force. Atomic force microscopy (AFM), a single-molecule imaging technique, is well suited to unmask complex, asynchronous molecular activities of membrane-associated proteins including those comprising the Sec apparatus. Using AFM, the dynamic structure of membrane-external protein topography of Sec system components can be directly visualized with high spatial-temporal precision. This mini-review is focused on AFM imaging of the Sec system in near-native fluid conditions where activity can be maintained and biochemically verified. Angstrom-scale conformational changes of SecYEG are reported on 100 ms timescales in fluid lipid bilayers. The association of SecA with SecYEG, forming membrane-bound SecYEG/SecA translocases, is directly visualized. Recent work showing topographical aspects of the translocation process that vary with precursor species is also discussed. The data suggests that the Sec system does not employ a single translocation mechanism. We posit that differences in the spatial frequency distribution of hydrophobic content within precursor sequences may be a determining factor in mechanism selection. Precise AFM investigations of active translocases are poised to advance our currently vague understanding of the complicated macromolecular movements underlying protein export across membranes.
more »
« less
This content will become publicly available on July 4, 2026
Automated Registration and Clustering for Enhanced Localization Atomic Force Microscopy of Flexible Membrane Proteins
Atomic Force Microscopy (AFM) can create images of biomolecules under near-native conditions but suffers from limited lateral resolution due to the finite AFM tip size and recording frequency. The recently developed Localization Atomic Force Microscopy or LAFM (Heath et al., Nature 594, 385 (2021)) enhances lateral resolution by reconstructing peak positions in AFM image stacks, but it is less effective for flexible proteins with multiple conformations. Here we introduce an unsupervised deep learning algorithm that simultaneously registers and clusters images by protein conformation, thus making LAFM applicable to more flexible proteins. Using simulated AFM images from molecular dynamics simulations of the SecYEG translocon as a model membrane protein system, we demonstrate improved resolution for individual protein conformations. This work represents a step towards a more general LAFM algorithm that can handle biological macromolecules with multiple distinct conformational states such as SecYEG. Author summaryAtomic Force Microscopy (AFM) enables high-resolution imaging of biomolecules under near-native conditions but faces lateral resolution limits due to the finite AFM tip size and recording frequency. The recently developed Localization Atomic Force Microscopy (LAFM) method addresses this by reconstructing peak positions from AFM image stacks, achieving almost atomic resolution for rigid proteins like bacteriorhodopsin (Heath et al., Nature 594, 385 (2021)). However, flexible membrane proteins with dynamic conformations, such as the SecYEG translocon, which exhibits large and highly mobile cytoplasmic loops, lead to non-physical smearing in standard LAFM reconstructions. Here, we present a computational framework combining unsupervised deep clustering and LAFM to enhance the lateral resolution of AFM images of flexible membrane proteins. Our neural network algorithm (i) groups AFM images into conformationally homogeneous clusters and (ii) registers images within each cluster. Applying LAFM separately to these clusters minimizes smearing artifacts, yielding high-resolution reconstructions for distinct conformations. We validate this approach using synthetic AFM images generated from all-atom molecular dynamics simulations of SecYEG in a solvated POPE lipid bilayer. This advancement extends LAFM’s utility to encompass conformationally diverse membrane proteins.
more »
« less
- Award ID(s):
- 2122027
- PAR ID:
- 10615938
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- bioRxiv
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
SecA is the critical adenosine triphosphatase that drives preprotein transport through the translocon, SecYEG, in Escherichia coli . This process is thought to be regulated by conformational changes of specific domains of SecA, but real-time, real-space measurement of these changes is lacking. We use single-molecule atomic force microscopy (AFM) to visualize nucleotide-dependent conformations and conformational dynamics of SecA. Distinct topographical populations were observed in the presence of specific nucleotides. AFM investigations during basal adenosine triphosphate (ATP) hydrolysis revealed rapid, reversible transitions between a compact and an extended state at the ~100-ms time scale. A SecA mutant lacking the precursor-binding domain (PBD) aided interpretation. Further, the biochemical activity of SecA prepared for AFM was confirmed by tracking inorganic phosphate release. We conclude that ATP-driven dynamics are largely due to PBD motion but that other segments of SecA contribute to this motion during the transition state of the ATP hydrolysis cycle.more » « less
-
Machine learning approaches for improving atomic force microscopy instrumentation and data analyticsAtomic force microscopy (AFM) is a part of the scanning probe microscopy family. It provides a platform for high-resolution topographical imaging, surface analysis as well as nanomechanical property mapping for stiff and soft samples (live cells, proteins, and other biomolecules). AFM is also crucial for measuring single-molecule interaction forces and important parameters of binding dynamics for receptor-ligand interactions or protein-protein interactions on live cells. However, performing AFM measurements and the associated data analytics are tedious, laborious experimental procedures requiring specific skill sets and continuous user supervision. Significant progress has been made recently in artificial intelligence (AI) and deep learning (DL), extending into microscopy. In this review, we summarize how researchers have implemented machine learning approaches so far to improve the performance of atomic force microscopy (AFM), make AFM data analytics faster, and make data measurement procedures high-throughput. We also shed some light on the different application areas of AFM that have significantly benefited from applications of machine learning frameworks and discuss the scope and future possibilities of these crucial approaches.more » « less
-
Kymograph analysis is employed across the biological atomic force microscopy (AFM) community to boost temporal resolution. The method is well suited for revealing protein dynamics at the single molecule level in near-native conditions. Yet, kymograph analysis comes with limitations that depend on several factors including protein geometry and instrumental drift. This work focuses on conformational dynamics of difficult-to-study sparse distributions of membrane proteins. We compare and contrast AFM kymograph analysis for two proteins, one of which (SecDF) exhibits conformational dynamics primarily in the vertical direction (normal to the membrane surface) and the other (Pgp) exhibits a combination of lateral dynamics and vertical motion. Common experimental issues are analyzed including translational and rotational drift. Conformational transition detection is evaluated via kymograph simulations followed by state detection algorithms. We find that kymograph analysis is largely robust to lateral drift. Displacement of the AFM line scan trajectory away from the protein center of mass by a few nanometers, roughly half of the molecule diameter, does not significantly affect transition detection nor generate undue dwell time errors. On the other hand, for proteins like Pgp that exhibit significant azimuthal maximum height dependence, rotational drift can potentially produce artifactual transitions. Measuring the height of a membrane protein protrusion is generally superior to measurement of width, confirming intuition based on vertical resolution superiority. In low signal-to-noise scenarios, common state detection algorithms struggle with transition detection as opposed to infinite hidden Markov models. AFM kymography represents a valuable addition to the membrane biophysics toolkit; continued hardware and software improvements are poised to expand the method’s impact in the field.more » « less
-
Atomic force microscopy (AFM) provides a platform for high-resolution topographical imaging and the mechanical characterization of a wide range of samples, including live cells, proteins, and other biomolecules. AFM is also instrumental for measuring interaction forces and binding kinetics for protein–protein or receptor–ligand interactions on live cells at a single-molecule level. However, performing force measurements and high-resolution imaging with AFM and data analytics are time-consuming and require special skill sets and continuous human supervision. Recently, researchers have explored the applications of artificial intelligence (AI) and deep learning (DL) in the bioimaging field. However, the applications of AI to AFM operations for live-cell characterization are little-known. In this work, we implemented a DL framework to perform automatic sample selection based on the cell shape for AFM probe navigation during AFM biomechanical mapping. We also established a closed-loop scanner trajectory control for measuring multiple cell samples at high speed for automated navigation. With this, we achieved a 60× speed-up in AFM navigation and reduced the time involved in searching for the particular cell shape in a large sample. Our innovation directly applies to many bio-AFM applications with AI-guided intelligent automation through image data analysis together with smart navigation.more » « less
