Protein structure prediction has undergone a revolution with the advent of AI- based algorithms, such as AlphaFold and RoseTTAFold. As a result, over 200 million predicted protein structures have been...

Heterologous protein expression is an indispensable strategy for generating recombinant proteins. Escherichia coli (E. coli) is the most widely used microbial host for recombinant protein production due to its rapid growth, well-characterized genetics, and ability to produce recombinant proteins in high yields using modern recombinant DNA technology. However, while there is a plethora of robust protein expression protocols for E. coli, these methods are often unsuitable for high-throughput screening due to their significant resource and time consumption; these protocols are also susceptible to operator error and inconsistency. To address these challenges, we developed Automated Protein EXpression (APEX), a robust and automated pipeline for recombinant protein production in E. coli. APEX leverages the accessible, open-source Opentrons OT-2 platform to automate microbial handling and protein expression with high precision and reproducibility. APEX can be configured to perform heat shock transformation, selective plating, colony sampling, microculturing, and protein expression induction using a low-cost, minimal OT-2 hardware setup. We further demonstrate the efficacy of our automated transformation workflows using a variety of plasmids (2.7–17.7 kb) and exemplify the automated heterologous expression of a diverse array of proteins (27–222 kDa). Designed with customization, modularity, and user-friendliness in mind, APEX can be easily adapted to the operator’s needs without requiring any coding expertise. APEX is available at https://github.com/stracquadaniolab/apex-nf under the AGPL3 license.

Su et al. elucidated the cryo-EM structures of the CASTOR1-GATOR2, Sestrin2-GATOR2, and CASTOR1-Sestrin2-GATOR2 complexes. By integrating the structural findings with in vitro pull-down assays and HDX...

In this work the authors develop a machine learning tool to classify spherical densities as ions and waters in provided x-ray crystallography and cryo-EM macromolecular structures. The method is valid...