The purification of recombinant proteins for therapeutic or analytical applications requires the use of several chromatographic steps in order to achieve a high level of purity. A range of techniques is available such as anion and cation exchange chromatography, which can be carried out at different pHs, and hence used at different steps, hydrophobic interaction chromatography, gel filtration and affinity chromatography. Evidently when confronted with a complex mixture of partially unknown proteins or a clarified cell extract there are many different routes one can take in order to choose the minimum and most efficient number of purification steps to achieve a desired level of purity (e.g. 98, 99.5 or 99.9%). In this review we will show how an initial 'proteomic' characterization of the complex initial mixture of target protein and protein contaminants can be used to select the most efficient chromatographic separation steps in order to achieve a maximum level of purity with a minimum number of steps. The chosen methodology was implemented in a computer based expert system. The first algorithm developed was used to select the most efficient purification method to separate a protein from its contaminants based on the physicochemical properties of the protein product and the protein contaminants. The second algorithm developed was used to predict the number and concentration of contaminants after each separation as well as protein product purity. The successful application of the expert system approach, based on an initial proteomic characterization, to the practical cases of protein mixtures and clarified fermentation supernatant is presented and discussed. The purification strategy proposed was experimentally tested and validated with a mixture of four proteins and the experimental validation was also carried out with an 'unknown' supernatant of Bacillus subtilis producing a recombinant beta-1,3-glucanase. The system was robust to errors <10% which is the range that can be found in the experimental determination of the properties in the database of product and contaminants. On the other hand, the system was sensitive both to larger variations (>20%) in the properties of the contaminant database and the protein product and to variations in one protein property (e.g. hydrophobicity). Copyright (C) 2004 John Wiley Sons, Ltd.
