Biology subjects

Waterman, D. G.

Publications and source records attributed to Waterman, D. G..

3 recordsLinked to original sources

bioRxiv2026

Automating 3DED data processing at eBIC

Three-dimensional electron diffraction (3DED) is an emerging and useful technique for solving molecular structures of small and biological macro-molecules from nanometre-sized crystals. We present our automated data processing workflow for 3DED datasets collected at Diamond Light Sources electron Bio-Imaging Centre (eBIC). For this purpose, we developed a package called AutoED. The processing pipeline includes data collection, analysis of the beam position, metadata gathering, file conversion, and finally data processing using xia2 (which supports both DIALS and XDS). The processing results are captured in a summary report produced by AutoED. Our main goal is to reduce the workload of electron diffraction scientists, but also to enforce good standards already used in macromolecular crystallography (MX). All the collected 3DED datasets are automatically converted into NeXus data format which is considered a Gold Standard for MX. This standardized data format allows for all the relevant metadata about the experiment to be kept together with diffraction images. We also discuss the methods used in AutoED to determine the electron beam position on diffraction images.

biophysics↗

bioRxiv2025

Predicting Protein Crystal Solvent Content from Patterson Maps Using Machine Learning

Estimating the solvent content of protein crystals is fundamental to identifying the correct symmetry and phasing of the unit cell. Typically, the number of molecules in the asymmetric unit is not known and probabilistic methods are used based on statistics derived from the Protein Data Bank (PDB). These methods tend to predict the number of molecules incorrectly in around 20% of cases, which can significantly impede the structure solution pipeline. Here multiple machine learning approaches are investigated to predict solvent content using Patterson Maps. Several architectures are shown to give a significant improvement over current approaches, with prediction errors being reduced by over 50%. In addition, the potential of embedded representations of Patterson Maps for clustering is demonstrated, which could lead to new approaches for identifying similar structures when processing novel data.

molecular biology↗

bioRxiv2020

A Workflow for Protein Structure Determination from Thin Crystal Lamella by Micro-Electron Diffraction

Micro-Electron Diffraction (MicroED) has recently emerged as a powerful method for the analysis of biological structures at atomic resolution. This technique has been largely limited to protein nanocrystals which grow either as needles or plates measuring only a few hundred nanometres in thickness. Furthermore, traditional microED data processing uses established X-ray crystallography software that is not optimised for handling compound effects that are unique to electron diffraction data. Here, we present an integrated workflow for microED, from sample preparation by cryo-focused ion beam milling, through data collection with a standard Ceta-D detector, to data processing using the DIALS software suite, thus enabling routine atomic structure determination of protein crystals of any size and shape using microED. We demonstrate the effectiveness of the workflow by determining the structure of proteinase K to 2.0 [A] resolution and show the advantage of using protein crystal lamellae over nanocrystals.

biophysics↗