# Multi-Resolution Docking Methods for Electron Microscopy

> **NIH NIH R01** · OLD DOMINION UNIVERSITY · 2022 · $311,844

## Abstract

Summary
In the past decade, we have witnessed a revolutionary progress in camera technology and the attainable
resolution of macromolecular assemblies via cryogenic electron microscopy (cryo-EM) and in the development
of computational algorithms that relate the resulting 3D maps to atomic resolution structures. Whereas single-
particle cryo-EM today is capable of directly solving atomic structures of biomolecular assemblies in isolation,
electron tomography (ET) in unstained frozen-hydrated samples is widely used to capture the 3D organization
of supramolecular complexes in their native (organelle, cell, or tissue) environments. We have identified three
inter-related research areas where our computational modeling experience (historically rooted in pre-revolution
multi-scale approaches) offers the biggest value to today's post-revolution EM community: (1) medium
resolution cryo-EM modeling, (2) the segmentation and denoising of cryo-ET data, and (3) the validation of
atomic models and their corresponding maps. The first aim is an extension of promising new ideas in flexible
fitting as well as secondary structure prediction for medium resolution maps, which have been our key
research areas in the past. medium resolution (5-10Å) maps are still widely used in EM and can be of
significant biological importance. This is particularly true in the case of cryo-ET maps, which are harder to read
than single particle cryo-EM maps because they often exhibit considerable noise, anisotropic resolution, and
anisotropic density variations due to the low dose requirements and the missing wedge in the Fourier space. In
the case of tightly packed or crowded macromolecular structures, the fusion of nearby biomolecular densities
prevents an automated segmentation of geometric shapes, requiring a labor-intensive manual tracing by
human experts. We are currently developing novel computational approaches to provide a more objective
strategy for missing wedge correction in homogeneous specimen areas of tomograms. Our hybrid approach
combines deconvolution and denoising with template matching in a unified mathematical framework that allows
modeling constraints to be imposed in a least-squares optimization process. Our approach can also be
extended to the flexible refinement of atomic structures using our damped dynamics flexible fitting approach by
tuning the internal point-spread functions to the missing wedge of the ET data. To support these aims, we will
quantitatively measure the fitness of an atomic model in local density regions and characterize the fitness of
maps with reliable reference structures. The collaborative efforts supported by this grant will include the
refinement of cytoskeletal filaments, molecular motors, bacterial chemoreceptor arrays, and hair cell
stereocilia. The algorithmic and methodological developments will be distributed freely through the established
Internet-based mechanisms used by the Situs and Sculptor packages and as plugins for the po...

## Key facts

- **NIH application ID:** 10473759
- **Project number:** 5R01GM062968-16
- **Recipient organization:** OLD DOMINION UNIVERSITY
- **Principal Investigator:** WILLY R WRIGGERS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $311,844
- **Award type:** 5
- **Project period:** 2001-04-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10473759

## Citation

> US National Institutes of Health, RePORTER application 10473759, Multi-Resolution Docking Methods for Electron Microscopy (5R01GM062968-16). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10473759. Licensed CC0.

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