  X-ray crystallography is the predominant method for obtaining atomic-scale
information about biological macromolecules. Despite the success of the
technique, obtaining well diffracting crystals still critically limits going
from protein to structure. In practice, the crystallization process proceeds
through knowledge-informed empiricism. Better physico-chemical understanding
remains elusive because of the large number of variables involved, hence little
guidance is available to systematically identify solution conditions that
promote crystallization. To help determine relationships between macromolecular
properties and their crystallization propensity, we have trained statistical
models on samples for 182 proteins supplied by the Northeast Structural
Genomics consortium. Gaussian processes, which capture trends beyond the reach
of linear statistical models, distinguish between two main physico-chemical
mechanisms driving crystallization. One is characterized by low levels of side
chain entropy and has been extensively reported in the literature. The other
identifies specific electrostatic interactions not previously described in the
crystallization context. Because evidence for two distinct mechanisms can be
gleaned both from crystal contacts and from solution conditions leading to
successful crystallization, the model offers future avenues for optimizing
crystallization screens based on partial structural information. The
availability of crystallization data coupled with structural outcomes analyzed
through state-of-the-art statistical models may thus guide macromolecular
crystallization toward a more rational basis.
