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The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per‐pattern capabilities of Resonet are demonstrated: (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. Resonet was tested across a compilation of diffraction images from synchrotron experiments and X‐ray free‐electron laser experiments. Crucially, these models readily execute on graphics processing units and can thus significantly outperform conventional algorithms. While Resonet is currently utilized to provide real‐time feedback for macromolecular crystallography users at the Stanford Synchrotron Radiation Lightsource, its simple Python‐based interface makes it easy to embed in other processing frameworks. This work highlights the utility of physics‐based simulation for training deep neural networks and lays the groundwork for the development of additional models to enhance diffraction collection and analysis. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic measures forced students to stay home and confined them to remote learning. This had a large impact on laboratory experiments, which are often impossible to complete from home. This article is a resource for instructors/educators to introduce the topic of structural biology and crystallographic methods. The main focus is to describe a hands-on crystallization laboratory exercise that can be carried out remotely at home with safe household products. X-ray crystallography is a vital technique for determining protein structure and function. This information can be used to understand fundamental biological processes and to help in the design of life-saving medications. Here, a method was developed to teach crystallography using reagents and equipment that can be found in grocery stores. The steps involved in a crystallography experiment are detailed with links and references to additional resources. [ABSTRACT FROM AUTHOR]

The article proposes a method for growing protein crystals (PC) on cryoloops and micromounts. The crystallization, crystal visualization and handling of proteins are described. In addition, a comparison of the diffraction patterns and the structures of PC grown by the proposed method and the conventional hanging-drop method is presented.

The article discusses how remote access to crystallography beamlines has set a new paradigm shift to develop new research tools at the Stanford Synchrotron Radiation Lightsource (SSRL). Remote access to control synchrotron beamline has established new collaborative research whereby remote users from different institutions can be connected at the same time to the SSRL beamlines. The access also triggered a shift in the way students of crystallography are introduced to synchrotron data collection and trained in the best methods for collecting data.