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. Practically , the crystallization process mainly proceeds through knowledge-informed empiricism. Better physico-chemical assistance remains elusive because of the large number of variables involved ; little microscopic guidance is available to  identify solution conditions that promote crystallization. To help determine relationships between macromolecular properties and their crystallization propensity, we train 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, help us distinguish between two main physico-chemical mechanisms driving crystallization. One is characterized by low levels of side chain entropy and is 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 precursor macromolecular solution conditions , the model offers  avenues for optimizing crystallization screens based on partial structural information or even from primary sequence analyses . The availability of crystallization data coupled with structural outcomes analyzed through complex statistical models will guide macromolecular crystallization toward a more rational basis.
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.
R
Practically
In practice
D
mainly
R
assistance
understanding
R
; little microscopic
, hence little
A
systematically
R
train
have trained
R
Consortium. Gaussian Processes
consortium. Gaussian processes
D
help us
R
is
has been
D
,
R
precursor macromolecular solution conditions
solution conditions leading to successful crystallization
A
future
D
or even from primary sequence analyses
R
complex statistical models will
state-of-the-art statistical models may thus