One of the biggest challenges for any scientist is to ensure that their experimental model of choice actually mimics natural biological circumstances. While it is one thing to conduct research in a test tube or cell culture dish it is quite another to translate those results into human biology. It is therefore imperative for scientists to choose a research model that most closely resembles its scaled up reality.
Over the past decade, the field of proteomics has experienced exponential growth. With the rise of technologies such as protein crystallography, protein arrays and surface plasmon resonance more information can be gathered on a proteomic-wide scale than ever before. Nonetheless, most proteomic experiments are conducted in-vitro often after protein extraction and clean-up techniques in an environment that is far removed from their cellular milieu. Under such circumstances, one must wonder how biologically relevant results techniques such as protein-protein interaction actually are. In fact, in a recent story published in Chemical and Engineering News (C&EN), staff writer Celia Arnaud describes the often overlooked effect of protein crowding on proteins function and stability.
According to the article, when proteins are packed into a cell (as is often the case under natural biological conditions), excluded-volume effects occur, which means that many things happen simply because molecules occupy space. This circumstance is often compensated on the bench with the addition of chemical agents such as Ficoll, however this is not an ideal way to replicate protein crowding and will not necessarily replicate biochemical conditions found in the cell.
According to Martin Gruebele, a chemist at the University of Illinois, Urbana-Champaign, “Eventually, people will expect that a complete protein data set includes in-cell or crowded studies of various kinds, in addition to the current aqueous buffer data. This is clearly where things will shift in the next five to 10 years.”
For more on the importance of protein crowding see Close Quarters
