In Crystallo Selection to Establish New RNA Crystal Contacts.
ABSTRACT: Crystallography is a major technique for determining large RNA structures. Obtaining diffraction-quality crystals has been the bottleneck. Although several RNA crystallization methods have been developed, the field strongly needs additional approaches. Here we invented an in crystallo selection strategy for identifying mutations that enhance a target RNA's crystallizability. The strategy includes constructing an RNA pool containing random mutations, obtaining crystals, and amplifying the sequences enriched by crystallization. We demonstrated a proof-of-principle application to the P4-P6 domain from the Tetrahymena ribozyme. We further determined the structures of four selected mutants. All four establish new crystal lattice contacts while maintaining the native structure. Three mutants achieve this by relocating bulges and one by making a helix more flexible. In crystallo selection provides opportunities to improve crystals of RNAs or RNA-ligand complexes. Our results also suggest that mutants may be rationally designed for crystallization by "walking" a bulge along the RNA chain.
Project description:Tyrosinases are type 3 copper enzymes that belong to the polyphenol oxidase (PPO) family and are able to catalyze both the ortho-hydroxylation of monophenols and their subsequent oxidation to o-quinones, which are precursors for the biosynthesis of colouring substances such as melanin. The first plant pro-tyrosinase from Malus domestica (MdPPO1) was recombinantly expressed in its latent form (56.4?kDa) and mutated at four positions around the catalytic pocket which are believed to influence the activity of the enzyme. Mutating the amino acids, which are known as activity controllers, yielded the mutants MdPPO1-Ala239Thr and MdPPO1-Leu243Arg, whereas mutation of the so-called water-keeper and gatekeeper residues resulted in the mutants MdPPO1-Glu234Ala and MdPPO1-Phe259Ala, respectively. The wild-type enzyme and two of the mutants, MdPPO1-Ala239Thr and MdPPO1-Phe259Ala, were successfully crystallized, leading to single crystals that diffracted to 1.35, 1.55 and 1.70?Å resolution, respectively. All crystals belonged to space group P212121, exhibiting similar unit-cell parameters: a = 50.70, b = 80.15, c = 115.96?Å for the wild type, a = 50.58, b = 79.90, c = 115.76?Å for MdPPO1-Ala239Thr and a = 50.53, b = 79.76, c = 116.07?Å for MdPPO1-Phe259Ala. In crystallo activity tests with the crystals of the wild type and the two mutants were performed by adding the monophenolic substrate tyramine and the diphenolic substrate dopamine to crystal-containing drops. The effects of the mutation on the activity of the enzyme were observed by colour changes of the crystals owing to the conversion of the substrates to dark chromophore products.
Project description:Crystallization of macromolecules has long been perceived as a stochastic process, which cannot be predicted or controlled. This is consistent with another popular notion that the interactions of molecules within the crystal, i.e., crystal contacts, are essentially random and devoid of specific physicochemical features. In contrast, functionally relevant surfaces, such as oligomerization interfaces and specific protein-protein interaction sites, are under evolutionary pressures so their amino acid composition, structure, and topology are distinct. However, current theoretical and experimental studies are significantly changing our understanding of the nature of crystallization. The increasingly popular "sticky patch" model, derived from soft matter physics, describes crystallization as a process driven by interactions between select, specific surface patches, with properties thermodynamically favorable for cohesive interactions. Independent support for this model comes from various sources including structural studies and bioinformatics. Proteins that are recalcitrant to crystallization can be modified for enhanced crystallizability through chemical or mutational modification of their surface to effectively engineer "sticky patches" which would drive crystallization. Here, we discuss the current state of knowledge of the relationship between the microscopic properties of the target macromolecule and its crystallizability, focusing on the "sticky patch" model. We discuss state-of-the-art in silico methods that evaluate the propensity of a given target protein to form crystals based on these relationships, with the objective to design variants with modified molecular surface properties and enhanced crystallization propensity. We illustrate this discussion with specific cases where these approaches allowed to generate crystals suitable for structural analysis.
Project description:The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/.
Project description:An alternative rational approach to improve protein crystals by using single-site mutation of surface residues is proposed based on the results of a statistical analysis using a compiled data set of 918 independent crystal structures, thereby reflecting not only the entropic effect but also other effects upon protein crystallization. This analysis reveals a clear difference in the crystal-packing propensity of amino acids depending on the secondary-structural class. To verify this result, a systematic crystallization experiment was performed with the biotin carboxyl carrier protein from Pyrococcus horikoshii OT3 (PhBCCP). Six single-site mutations were examined: Ala138 on the surface of a ?-sheet was mutated to Ile, Tyr, Arg, Gln, Val and Lys. In agreement with prediction, it was observed that the two mutants (A138I and A138Y) harbouring the residues with the highest crystal-packing propensities for ?-sheet at position 138 provided better crystallization scores relative to those of other constructs, including the wild type, and that the crystal-packing propensity for ?-sheet provided the best correlation with the ratio of obtaining crystals. Two new crystal forms of these mutants were obtained that diffracted to high resolution, generating novel packing interfaces with the mutated residues (Ile/Tyr). The mutations introduced did not affect the overall structures, indicating that a ?-sheet can accommodate a successful mutation if it is carefully selected so as to avoid intramolecular steric hindrance. A significant negative correlation between the ratio of obtaining amorphous precipitate and the crystal-packing propensity was also found.
Project description:Until recently, protein crystallization has mostly been regarded as a stochastic event over which the investigator has little or no control. With the dramatic technological advances in synchrotron-radiation sources and detectors and the equally impressive progress in crystallographic software, including automated model building and validation, crystallization has increasingly become the rate-limiting step in X-ray diffraction studies of macromolecules. However, with the advent of recombinant methods it has also become possible to engineer target proteins and their complexes for higher propensity to form crystals with desirable X-ray diffraction qualities. As most proteins that are under investigation today are obtained by heterologous overexpression, these techniques hold the promise of becoming routine tools with the potential to transform classical crystallization screening into a more rational high-success-rate approach. This article presents an overview of protein-engineering methods designed to enhance crystallizability and discusses a number of examples of their successful application.
Project description:Existing methods for predicting protein crystallization obtain high accuracy using various types of complemented features and complex ensemble classifiers, such as support vector machine (SVM) and Random Forest classifiers. It is desirable to develop a simple and easily interpretable prediction method with informative sequence features to provide insights into protein crystallization. This study proposes an ensemble method, SCMCRYS, to predict protein crystallization, for which each classifier is built by using a scoring card method (SCM) with estimating propensity scores of p-collocated amino acid (AA) pairs (p=0 for a dipeptide). The SCM classifier determines the crystallization of a sequence according to a weighted-sum score. The weights are the composition of the p-collocated AA pairs, and the propensity scores of these AA pairs are estimated using a statistic with optimization approach. SCMCRYS predicts the crystallization using a simple voting method from a number of SCM classifiers. The experimental results show that the single SCM classifier utilizing dipeptide composition with accuracy of 73.90% is comparable to the best previously-developed SVM-based classifier, SVM_POLY (74.6%), and our proposed SVM-based classifier utilizing the same dipeptide composition (77.55%). The SCMCRYS method with accuracy of 76.1% is comparable to the state-of-the-art ensemble methods PPCpred (76.8%) and RFCRYS (80.0%), which used the SVM and Random Forest classifiers, respectively. This study also investigates mutagenesis analysis based on SCM and the result reveals the hypothesis that the mutagenesis of surface residues Ala and Cys has large and small probabilities of enhancing protein crystallizability considering the estimated scores of crystallizability and solubility, melting point, molecular weight and conformational entropy of amino acids in a generalized condition. The propensity scores of amino acids and dipeptides for estimating the protein crystallizability can aid biologists in designing mutation of surface residues to enhance protein crystallizability. The source code of SCMCRYS is available at http://iclab.life.nctu.edu.tw/SCMCRYS/.
Project description:Ubiquitin has many attributes suitable for a crystallization chaperone, including high stability and ease of expression. However, ubiquitin contains a high surface density of lysine residues and the doctrine of surface-entropy reduction suggests that these lysines will resist participating in packing interactions and thereby impede crystallization. To assess the contributions of these residues to crystallization behavior, each of the seven lysines of ubiquitin was mutated to serine and the corresponding single-site mutant proteins were expressed and purified. The behavior of these seven mutants was then compared with that of the wild-type protein in a 384-condition crystallization screen. The likelihood of obtaining crystals varied by two orders of magnitude within this set of eight proteins. Some mutants crystallized much more readily than the wild type, while others crystallized less readily. X-ray crystal structures were determined for three readily crystallized variants: K11S, K33S and the K11S/K63S double mutant. These structures revealed that the mutant serine residues can directly promote crystallization by participating in favorable packing interactions; the mutations can also exert permissive effects, wherein crystallization appears to be driven by removal of the lysine rather than by addition of a serine. Presumably, such permissive effects reflect the elimination of steric and electrostatic barriers to crystallization.
Project description:Combining macromolecular crystallography with in crystallo micro-spectrophotometry yields valuable complementary information on the sample, including the redox states of metal cofactors, the identification of bound ligands and the onset and strength of undesired photochemistry, also known as radiation damage. However, the analysis and processing of the resulting data differs significantly from the approaches used for solution spectrophotometric data. The varying size and shape of the sample, together with the suboptimal sample environment, the lack of proper reference signals and the general influence of the X-ray beam on the sample have to be considered and carefully corrected for. In the present article, how to characterize and treat these sample-dependent artefacts in a reproducible manner is discussed and the SLS-APE in situ, in crystallo optical spectroscopy data-analysis toolbox is demonstrated.
Project description:Crystallizing RNA has been an imperative and challenging task in the world of RNA research. Assistive methods such as chaperone-assisted RNA crystallography (CARC), employing monoclonal antibody fragments (Fabs) as crystallization chaperones have enabled us to obtain RNA crystal structures by forming crystal contacts and providing initial phasing information. Despite the early successes, the crystallization of large RNA-Fab complex remains a challenge in practice. The possible reason for this difficulty is that the Fab scaffold has not been optimized for crystallization in complex with RNA. Here, we have used the surface entropy reduction (SER) technique for the optimization of ?C209 P4-P6/Fab2 model system. Protruding lysine and glutamate residues were mutated to a set of alanines or serines to construct Fab2SMA or Fab2SMS. Expression with the shake flask approach was optimized to allow large scale production for crystallization. Crystal screening shows that significantly higher crystal-forming ratio was observed for the mutant complexes. As the chosen SER residues are far away from the CDR regions of the Fab, the same set of mutations can now be directly applied to other Fabs binding to a variety of ribozymes and riboswitches to improve the crystallizability of Fab-RNA complex.
Project description:Over the last decade, protein purification has become more efficient and standardized through the introduction of affinity tags. The choice and position of the tag, however, can directly influence the process of protein crystallization. Octopine dehydrogenase (OcDH) without a His tag and tagged protein constructs such as OcDH-His(5) and OcDH-LEHis(6) have been investigated for their crystallizability. Only OcDH-His(5) yielded crystals; however, they were multiple. To improve crystal quality, the cofactor NADH was added, resulting in single crystals that were suitable for structure determination. As shown by the structure, the His(5) tag protrudes into the cleft between the NADH and L-arginine-binding domains and is mainly fixed in place by water molecules. The protein is thereby stabilized to such an extent that the formation of crystal contacts can proceed. Together with NADH, the His(5) tag obviously locks the enzyme into a specific conformation which induces crystal growth.