Quality Control of Purified Proteins to Improve Research Data Reproducibility.

: As the research community strives to make published research ever more transparent and reliable, the quality of reagents used comes into focus. One category of such reagents that requires much stricter quality controls are recombinant proteins. Examples of typical quality issues with recombinant proteins will be presented, along with some results as to how this affects the reliability of the intended downstream application. One very problematic issue to be presented is aggregation and its effect on protein-protein affinity measurements. In order to improve the reliability and reproducibility of data using purified proteins in life science research, a group of professionals involved in protein purification and protein characterization/molecular biophysics from both the ARBRE-MOBIEU (Association of Resources for Biophysical research in Europe MOlecular BIophysics in EUrope) and P4EU (Protein Production and Purification Partnership in Europe) networks have drafted guidelines for improved quality control (QC). These guidelines, consisting of (i) minimal (but obligatory) information to be provided about the protein production process and methods used (ii) a minimal set of quality tests, i.e. purity, identity, homogeneity and lack of aggregation and (iii) some further recommendations (DNA binding, LPS contamination, competent fraction, batch-to-batch reproducibility, storage conditions, etc.) for tests based on the intended application of the proteins will be presented. Furthermore, over a one-year period, the networks have attempted to evaluate the impact of these guidelines by correlating the levels of quality control applied to given samples with the success and reproducibility of downstream experiments. The results indicate that QC guideline implementation can facilitate both experimental reliability and protein quality optimization. It seems, therefore, that investing in protein QC is advantageous to all the stakeholders in life sciences (researchers, editors and funding agencies alike) by improving data veracity and minimizing loss of valuable time and resources.