Getting better protein models from given diffraction data set: moving beyond conventional practices

The quality of macromolecular crystal structures depends to a large extent on the quality and quantity of the data used to produce them. I will describe my collaborative work with Kay Diederichs that shows that conventionally used data quality indicators are misleading and the conventional practices for choosing a high resolution cutoff leads crystallographers to throw away useful data and, as a consequence, end up with lower quality refined models than they could otherwise obtain. I will also discuss the potential to greatly increase data quality through the merging of multiple measurements from multiple passes of single crystals or from multiple crystals. A key factor supporting these shifts in practices is the introduction of a more robust correlation coefficient based indicator of the precision of merged data sets.