Statistical Analysis of Crystallization Database Links Protein Physico-Chemical Features with Crystallization Mechanisms

Abstract

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.

The “Sticky Patch” Model of Crystallization and Modification of Proteins for Enhanced Crystallizability

“…A third study looked in detail at the behavior of 182 proteins (also from the NESGC) which were each subject to extensive crystallization screen using 1,536 conditions and developed by the High-Throughput Crystallization Screening Laboratory of the Hauptman Woodward Medical Research Institute [82]. Statistical models were trained on this sample capturing trends driving crystallization.…”

“…In addition, a new correlation was found between crystallization and complementary electrostatic interactions. The study concluded that crystal contacts have ‘specific physico-chemical signature even if they are not biologically functional’ [82]. …”

“…The basic premise of the SER strategy is strongly corroborated by the aforementioned data-mining studies showing that preponderance of high-entropy amino acids in protein sequence is negatively correlated with crystallization success [81,82]. In an effort to better understand what mutational strategy is optimal, a more comprehensive study was carried out using the same model system of RhoGDI, expanding the target amino acids to Lys, Glu and Gln (which has the same conformational entropy as Glu, but has no charge) and replacing them with Ser, Thr and His [133].…”

Computational crystallization

“…However, this tool, as well as MCSG Z-score [54], is limited by its use of data from only one structural genomics center for training, and may thus perform poorly in predicting crystallizability of proteins that differ substantially from those in this relatively small training set. Logistic regression also cannot capture non-monotonic trends [55]. …”

Competition between Monomeric and Dimeric Crystals in Schematic Models for Globular Proteins

“…Fortunately, increasing the number of new structures deposited in public databases [1] enriches our understanding of effective crystallization screens and strategies that can be used with the vast majority of biomolecules that still resist assembly [4]. Yet even elementary structural analyses reveal blind spots in our materials comprehension.…”