Pairwise running of automated crystallographic model-building pipelines

Predicting the performance of automated crystallographic model-building pipelines

Proteins are macromolecules that perform essential biological functions which depend on their three-dimensional structure. Determining this structure involves complex laboratory and computational work. For the computational work, multiple software pipelines have been developed to build models of the protein structure from crystallographic data. Each of these pipelines performs differently depending on the characteristics of the electron-density map received as input. Identifying the best pipeline to use for a protein structure is difficult, as the pipeline performance differs significantly from one protein structure to another. As such, researchers often select pipelines that do not produce the best possible protein models from the available data. Here, a software tool is introduced which predicts key quality measures of the protein structures that a range of pipelines would generate if supplied with a given crystallographic data set. These measures are crystallographic quality-of-fit indicators based on included and withheld observations, and structure completeness. Extensive experiments carried out using over 2500 data sets show that the tool yields accurate predictions for both experimental phasing data sets (at resolutions between 1.2 and 4.0 Å) and molecular-replacement data sets (at resolutions between 1.0 and 3.5 Å). The tool can therefore provide a recommendation to the user concerning the pipelines that should be run in order to proceed most efficiently to a depositable model.

Structural barriers to scientific progress

Structural biases, which are intrinsic in the social structures in which we function, play a key role in maintaining boundaries between traditionally privileged and underprivileged groups; however, they are particularly difficult to identify from within those societies. Two instances are highlighted in which the social structures of science appear to have discouraged collaboration, to the disadvantage of software and data users. Possible links are suggested to the strongly hierarchical structure of science and other factors which may in turn also serve to maintain sex and/or gender disparities in participation in the scientific endeavour.