Accurate computational design of three-dimensional protein crystals.

Protein crystallization plays a central role in structural biology. Despite this, the process of crystallization remains poorly understood and highly empirical, with crystal contacts, lattice packing arrangements and space group preferences being largely unpredictable. Programming protein crystallization through precisely engineered side-chain-side-chain interactions across protein-protein interfaces is an outstanding challenge. Here we develop a general computational approach for designing three-dimensional protein crystals with prespecified lattice architectures at atomic accuracy that hierarchically constrains the overall number of degrees of freedom of the system. We design three pairs of oligomers that can be individually purified, and upon mixing, spontaneously self-assemble into >100 µm three-dimensional crystals. The structures of these crystals are nearly identical to the computational design models, closely corresponding in both overall architecture and the specific protein-protein interactions. The dimensions of the crystal unit cell can be systematically redesigned while retaining the space group symmetry and overall architecture, and the crystals are extremely porous and highly stable. Our approach enables the computational design of protein crystals with high accuracy, and the designed protein crystals, which have both structural and assembly information encoded in their primary sequences, provide a powerful platform for biological materials engineering.

Characterizing Protein Kinase Substrate Specificity Using the Proteomic Peptide Library (ProPeL) Approach.

Characterizing protein kinase substrate specificity motifs represents a powerful step in elucidating kinase-signaling cascades. The protocol described here uses a bacterial system to evaluate kinase specificity motifs in vivo, without the need for radioactive ATP. The human kinase of interest is cloned into a heterologous bacterial expression vector and allowed to phosphorylate E. coli proteins in vivo, consistent with its endogenous substrate preferences. The cells are lysed, and the bacterial proteins are digested into peptides and phosphoenriched using bulk TiO2 . The pooled phosphopeptides are identified by tandem mass spectrometry, and bioinformatically analyzed using the pLogo visualization tool. The ProPeL approach allows for detailed characterization of wildtype kinase specificity motifs, identification of specificity drift due to kinase mutations, and evaluation of kinase residue structure-function relationships. © 2018 by John Wiley & Sons, Inc.

An in silico analysis of primary and secondary structure specificity determinants for human peptidylarginine deiminase types 2 and 4.

Human peptidylarginine deiminases (hPADs) are a family of five calcium-dependent enzymes that facilitate citrullination, which is the post-translational modification of peptidyl arginine to peptidyl citrulline. The isozymes hPAD2 and hPAD4 have been implicated in the development and progression of several autoimmune diseases, including rheumatoid arthritis and multiple sclerosis. To better characterize the primary and secondary structure determinants of citrullination specificity, we mined the literature for protein sequences susceptible to citrullination by hPAD2 or hPAD4. First, protein secondary structure classification (α-helix, ß-sheet, or coil) was predicted using the PSIPRED software. Next, we used motif-x and pLogo to extract and visualize statistically significant motifs within each data set. Within the data sets of peptides predicted to lie in coil regions, both hPAD2 and hPAD4 appear to favor citrullination of glycine-containing motifs, while distinct hydrophobic motifs were identified for hPAD2 citrullination sites predicted to reside within α-helical and ß-sheet regions. Additionally, we identified potential substrate overlap between coil region citrullination and arginine methylation. Together, these results confirm the importance and offer some insight into the role of secondary structure elements for citrullination specificity, and provide biological context for the existing hPAD specificity and arginine post-translational modification literature.

Cushing's syndrome mutant PKAL205R exhibits altered substrate specificity.

The PKAL205R hotspot mutation has been implicated in Cushing's syndrome through hyperactive gain-of-function PKA signaling; however, its influence on substrate specificity has not been investigated. Here, we employ the Proteomic Peptide Library (ProPeL) approach to create high-resolution models for PKAWT and PKAL205R substrate specificity. We reveal that the L205R mutation reduces canonical hydrophobic preference at the substrate P + 1 position, and increases acidic preference in downstream positions. Using these models, we designed peptide substrates that exhibit altered selectivity for specific PKA variants, and demonstrate the feasibility of selective PKAL205R loss-of-function signaling. Through these results, we suggest that substrate rewiring may contribute to Cushing's syndrome disease etiology, and introduce a powerful new paradigm for investigating mutation-induced kinase substrate rewiring in human disease.

Using bacteria to determine protein kinase specificity and predict target substrates.

The identification of protein kinase targets remains a significant bottleneck for our understanding of signal transduction in normal and diseased cellular states. Kinases recognize their substrates in part through sequence motifs on substrate proteins, which, to date, have most effectively been elucidated using combinatorial peptide library approaches. Here, we present and demonstrate the ProPeL method for easy and accurate discovery of kinase specificity motifs through the use of native bacterial proteomes that serve as in vivo libraries for thousands of simultaneous phosphorylation reactions. Using recombinant kinases expressed in E. coli followed by mass spectrometry, the approach accurately recapitulated the well-established motif preferences of human basophilic (Protein Kinase A) and acidophilic (Casein Kinase II) kinases. These motifs, derived for PKA and CK II using only bacterial sequence data, were then further validated by utilizing them in conjunction with the scan-x software program to computationally predict known human phosphorylation sites with high confidence.