Development of a fully automated macromolecular crystallization/observation robotic system, HTS-80

Abstract

A robotic system has been developed to be used for macromolecular crystallization and observation in typical university laboratories with a research focus on protein crystallography. The system consists of three major parts: a dispenser unit, a storage unit and an observation unit. This system is designed to automatically perform all of the processes involved in crystallization and observation without requiring any manual operations. The dispenser and observation units can carry out both sitting-drop vapor-diffusion procedures and microbatch procedures. With this system, the procedures are controlled by a personal computer running GUI-based software. After the dispensing of protein solution into the crystallization plates, they are automatically transferred to the storage units, followed by automatic observation according to a required schedule with arbitrary intervals. At each stage of crystallization, droplets in the crystallization plates are examined by original image-processing software in order to evaluate the appearance of the crystals.

Nucleant-mediated protein crystallization with the application of microporous synthetic zeolites

“…The most widely used method for improving crystal quality is the large-scale screening of crystallization conditions. In order to facilitate the process of crystal screening, a number of automated crystallization facilities have been developed by structural genomics initiatives (Stevens, 2000;Sulzenbacher et al, 2002;Sugahara & Miyano, 2002;Watanabe et al, 2002;Hui & Edwards, 2003;Adachi et al, 2004;Shah et al, 2005;Miyatake et al, 2005;Hiraki et al, 2006). In spite of these efforts, the crystallization of proteins with poor crystallizability still remains a major problem.…”

Evaluation of crystalline objects in crystallizing protein droplets based on line-segment information in greyscale images

“…Rupp (2003) used phase congruency to detect a large number of small crystals and Gester et al (2003) automated the counting of the number of crystals to generate three-dimensional surface plots of the crystals and to determine the crystal size based on the length of the perimeter of the crystals. Miyatake et al (2005) developed an automated crystallization/observation robotic system, HTS-80, which was reported to be able to categorize the crystallization droplet status into four stages based on the extracted contour information. Most of these previous studies focused on the existence or absence of a crystal.…”

Establishing a training set through the visual analysis of crystallization trials. Part I: ∼150 000 images

“…There have been a number of efforts to automate the image analysis of crystallization outcomes. Many of these efforts emphasize the identification of several specific categories of outcomes related to crystallization leads (Zuk & Ward, 1991;Cumbaa et al, 2003;Miyatake et al, 2005;Bern et al, 2004;Mayo et al, 2005;Berry et al, 2006;Walker et al, 2007;Cumbaa & Jurisica, 2005;Wilson & Main, 2000;Wilson, 2002;Kawabata et al, 2006).…”