High-throughput protein production systems have become an important issue, because protein production is one of the bottleneck steps in large-scale structural and functional analyses of proteins. We have developed a dialysis reactor and a fully automated system for protein production using the dialysis cell-free synthesis method, which we previously established to produce protein samples on a milligram scale in a high-throughput manner. The dialysis reactor was designed to be suitable for an automated system and has six dialysis cups attached to a flat dialysis membrane. The automated system is based on a Tecan Freedom EVO 200 workstation in a three-arm configuration, and is equipped with shaking incubators, a vacuum module, a robotic centrifuge, a plate heat sealer, and a custom-made tilting carrier for collection of reaction solutions from the flat-bottom cups with dialysis membranes. The consecutive process, from the dialysis cell-free protein synthesis to the partial purification by immobilized metal affinity chromatography on a 96-well filtration plate, was performed within ca. 14 h, including 8 h of cell-free protein synthesis. The proteins were eluted stepwise in a high concentration using EDTA by centrifugation, while the resin in the filtration plate was washed on the vacuum manifold. The system was validated to be able to simultaneously and automatically produce up to 96 proteins in yields of several milligrams with high well-to-well reliability, sufficient for structural and functional analyses of proteins. The protein samples produced by the automated system have been utilized for NMR screening to judge the protein foldedness and for structure determinations using heteronuclear multi-dimensional NMR spectroscopy. The automated high-throughput protein production system represents an important breakthrough in the structural and functional studies of proteins and has already contributed a massive amount of results in the structural genomics project at the RIKEN Structural Genomics/Proteomics Initiative (RSGI). (C) 2009 Elsevier Inc. All rights reserved.