Automated 96-well purification of hexahistidine-tagged recombinant proteins on MagneHis Ni(2)+-particles.

Functional genomics and the application of high-throughput (HT) approaches to solve biological and medical questions are the main drivers behind the increasing need for HT parallel expression and purification of recombinant proteins. Automation is necessary to facilitate this complex multistep process. We describe, in detail, an HT-automated purification of hexahistidine-tagged recombinant proteins using MagneHis Ni-Particles and the Biomek FX robot. This procedure is universally applicable to hexa-histidine-tagged recombinant proteins with the tag positioned at either the N- or C-terminus. With minor modifications, the automated protein purification protocol presented in this chapter could be adapted to purify recombinant proteins bearing other tags than hexahistidine and/or other expression systems than E. coli.

High-throughput protein concentration and buffer exchange: comparison of ultrafiltration and ammonium sulfate precipitation.

High-throughput protein purification is a complex, multi-step process. There are several technical challenges in the course of this process that are not experienced when purifying a single protein. Among the most challenging are the high-throughput protein concentration and buffer exchange, which are not only labor-intensive but can also result in significant losses of purified proteins. We describe two methods of high-throughput protein concentration and buffer exchange: one using ammonium sulfate precipitation and one using micro-concentrating devices based on membrane ultrafiltration. We evaluated the efficiency of both methods on a set of 18 randomly selected purified proteins from Shewanella oneidensis. While both methods provide similar yield and efficiency, the ammonium sulfate precipitation is much less labor intensive and time consuming than the ultrafiltration.

Automated purification of recombinant proteins: combining high-throughput with high yield.

Protein crystallography, mapping protein interactions, and other functional genomic approaches require purifying many different proteins, each of sufficient yield and homogeneity, for subsequent high-throughput applications. To fill this requirement efficiently, there is a need to develop robust, automated, high-throughput protein expression, and purification processes. We developed and compared two alternative workflows for automated purification of recombinant proteins based on expression of bacterial genes in Escherichia coli (E. coli). The first is a filtration separation protocol in which proteins of interest are expressed in a large volume, 800 ml of E. coli cultures, then isolated by filtration purification using Ni-NTA-Agarose (Qiagen). The second is a smaller scale magnetic separation method in which proteins of interest are expressed in a small volume, 25 ml, of E. coli cultures then isolated using a 96-well purification system with MagneHis Ni2+ Agarose (Promega). Both workflows provided comparable average yields of proteins, about 8 microg of purified protein per optical density unit of bacterial culture measured at 600 nm. We discuss advantages and limitations of these automated workflows, which can provide proteins with more than 90% purity and yields in the range of 100 microg to 45 mg per purification run, as well as strategies for optimizing these protocols.

Simple protein complex purification and identification method for high-throughput mapping of protein interaction networks.

Most current methods for purification and identification of protein complexes use endogenous expression of affinity-tagged bait, tandem affinity tag purification of protein complexes followed by specific elution of complexes from beads, and gel separation and in-gel digestion prior to mass spectrometric analysis of protein interactors. We propose a single affinity tag in vitro pull-down assay with denaturing elution, trypsin digestion in organic solvent, and LC-ESI MS/MS protein identification using SEQUEST analysis. Our method is simple and easy to scale-up and automate, making it suitable for high-throughput mapping of protein interaction networks and functional proteomics.