Biology subjects

Maurer, V. J.

Publications and source records attributed to Maurer, V. J..

4 recordsLinked to original sources

bioRxiv2025

Constrained template matching using rejection sampling

Identifying macromolecular complexes in situ using cryo-electron tomography remains challenging, with low signal-to-noise ratios, the missing wedge, and crowded backgrounds among the key limiting factors. By integrating prior knowledge on macromolecular localization, such as the preferred orientations of membrane-associated proteins, detection can be improved by constraining searches to biologically feasible orientations. Here we describe rejection sampling, an approach for integrating such constraints at voxel resolution that remains both accurate and computationally efficient. Using synthetic and experimental data, we show that these constraints improve detection, orientational assignment, and discrimination between macromolecules. The resulting picks match the performance of deep-learning methods informed by membrane structure without requiring annotated data. We further apply rejection sampling to ATP synthase on mitochondrial cristae, illustrating how it extends macromolecular detection to the large and highly curved membrane systems that pervade cells.

biophysics↗

bioRxiv2025

Helfrich Monte Carlo Flexible Fitting: physics-based, data-driven cell-scale simulations

Simulating entire cells represents the next frontier of computational biology. Achieving this goal requires methods that accurately describe cellular membranes across spatial and temporal scales. Although three-dimensional electron microscopy enables detailed membrane visualization, limitations on acquisition geometry, data quality, and field-of-view often result in fragmented membrane representations incompatible with simulations. To resolve this, here we introduce Helfrich Monte Carlo Flexible Fitting (HMFF), an approach that integrates experimental density data into physical simulations to determine membrane structure. Through the accompanying Mosaic software platform, we apply HMFF to influenza virus particles, Mycoplasma pneumoniae cells, and entire eukaryotic organelles. The resulting models enable multi-scale simulations spanning millions of lipids and proteins at experimentally determined positions, support quantitative morphological analysis, assess uncertainty in membrane localization, and reveal physical effects implicit in the data. Together, these capabilities establish a foundation for data-driven whole-cell simulations.

biophysics↗

bioRxiv2023

PyTME (Python Template Matching Engine): A fast, flexible, and multi-purpose template matching library for cryogenic electron microscopy data

Cryogenic electron microscopy (cryo-EM) is a key method in structural and cell biology. Analysis of cryo-EM images requires interpretation of noisy, low-resolution densities which relies on identifying the most probable orientation of macromolecules in a target using template matching. Many method-specific template matching software exist for single-particle cryo-EM, cryo-electron tomography (cryo-ET), or fitting atomic structures into averaged 3D maps of macromolecules. Here, we report the Python Template Matching Engine (pyTME), a software engine that consolidates method-specific template matching problems. The underlying library provides highly efficient template-matching implementation and abstract data structures for storing and manipulating input and output data. It scales favorable to large datasets, both with multiple CPUs and GPUs, compared to existing software enabling template matching of even unbinned cryo-ET data in hours, which was previously nearly impossible due to technical restraints. Any hardware-specific optimization needed for dealing with large data is automatically performed to increase ease of use and minimize user intervention. The efficiency and simplicity of pyTME will enable high throughput mining of a variety of cryo-EM and ET datasets in the future.

bioinformatics↗

bioRxiv2023

The Shape of Things in Cryo-ET: Why Emojis Aren't Just for Texts

Detecting specific biological macromolecules in cryogenic electron tomography (cryo-ET) data is frequently approached by applying cross-correlation-based 3D template matching. To reduce computational cost and noise, high binning is used to aggregate voxels before template matching. This remains a prevalent practice in both practical applications and method development. Here, we systematically evaluate the relation between template size, shape, and angular sampling to identify ribosomes in a ground truth annotated dataset. We show that at the commonly used binning, a detailed subtomogram average, a sphere, and the heart emoji [Formula] results in near-identical performance. Our findings indicate that with current template-matching practices, macromolecules can only be detected with high precision if their shape and size are sufficiently different from the background. Using theoretical considerations we rationalize our experimental results and discuss why at high binning, primarily low-frequency information remains and that template matching fails to be accurate because similarly shaped and sized macromolecules have similar low-frequency spectrums. We discuss these challenges and propose potential enhancements for future template-matching methodologies.

biophysics↗