MISO

Thu, 08 Oct 2026 00:00:00 +0000

Description

MISO (Model building from Identity, Sequence, and Observed location) is a workflow for generating three-dimensional models of biomolecules from scanning probe microscopy (SPM) images. Interpreting SPM images of flexible biomolecules such as glycans and glycoconjugates is usually done by hand: building candidate adsorption geometries one by one and testing them. MISO automates this step.

The user provides a hypothesis about:

the identity of the subunits (e.g. which monosaccharides),
their sequence (how they are connected), and
the observed location of each subunit in the SPM image.

MISO then builds structures consistent with that hypothesis. It aligns each monomer with the quaternion estimator algorithm (QUEST) and refines the polymer with biased classical molecular dynamics on the surface. Applied to existing SPM data of glycans and glycoconjugates, MISO reproduces structures previously validated by DFT. It can also filter out inconsistent hypotheses, and it scales up to a 3D model of an unfolded glycoprotein on a surface.

This work is described in Automating Model Building for SPM Images of Biomolecules Using MISO, J. Chem. Inf. Model. 66, 10056–10069 (2026).

Get the code from the repository.

MISO in SXM Viewer

The repository extends SXM Viewer, a desktop application for SPM data analysis (Anfatec/Omicron and Nanonis), with MISO tools, so the whole pipeline runs from the viewer:

Open an .sxm scan in SXM Viewer.
Tools → Position coordinates: toggle Pick mode and click on each molecule. The XY position (Å) and local height are recorded.
Export CSV: saves the molecule positions together with the STM grid (NPZ).
Tools → Run MISO: select the YAML configuration and the positions CSV, then set the number of iterations, polymers, compression steps and surface gravity.
The optimized structures are written to a results/ folder as .sdf, .mol and .mol2.

References

C. L. Gómez-Flores and K. Anggara. Automating Model Building for SPM Images of Biomolecules Using MISO. J. Chem. Inf. Model. 66, 10056–10069 (2026). https://doi.org/10.1021/acs.jcim.6c01134
SXM Viewer by Ex-libris. https://github.com/Ex-libris/sxm_viewer

SXM Image Analyzer

Thu, 08 Oct 2026 00:00:00 +0000

Description

SXM Image Analyzer is a Streamlit app for quickly viewing and cleaning up scanning probe microscopy (SPM/STM) images saved in the Nanonis .sxm format, with no local installation needed. Upload a .sxm file (up to 200 MB) to see the image and its acquisition metadata right away.

Open the app directly via the site link.

Features

Original image

View the raw topography image as recorded.
Download the unfiltered image (PNG), the scan metadata (TXT) and the STM data grid (NPZ) for further analysis in Python.

Processing

Use the Process tab to enhance the image interactively:

FFT filtering with Gaussian, Blackman–Harris or exponential windows and an adjustable width (σ), to remove high-frequency noise.
Unsharp masking with adjustable radius and amount, to sharpen molecular and atomic features.
Image transforms: contrast inversion and cosine transform.

The processed image, processed raw image and metadata can be downloaded.

Analysis (coming soon)

Dedicated analysis modes for specific adsorbates are under development: chitosan, RNA, proteins, chlorophyll and glycolipids.

Tutorial

A step-by-step notebook showing how to load .sxm files and apply these filters in Python is available on GitHub.

Scanning Probe Microscopy | My CV

MISO

Description

MISO in SXM Viewer

References

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SXM Image Analyzer

Description

Features

Original image

Processing

Analysis (coming soon)

Tutorial

Did you find this page helpful? Consider sharing it 🙌