TMCrys: predict propensity of success for transmembrane protein crystallization.
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ABSTRACT:
SUBMITTER: Varga JK
PROVIDER: S-EPMC6137969 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The high-throughput structure determination pipelines developed by structural genomics programs offer a unique opportunity for data mining. One important question is how protein properties derived from a primary sequence correlate with the protein's propensity to yield X-ray quality crystals (crystallizability) and 3D X-ray structures. A set of protein properties were computed for over 1,300 proteins that expressed well but were insoluble, and for approximately 720 unique proteins that resulted in X-ray structures. The correlation of the protein's iso-electric point and grand average hydropathy (GRAVY) with crystallizability was analyzed for full length and domain constructs of protein targets. In a second step, several additional properties that can be calculated from the protein sequence were added and evaluated. Using statistical analyses we have identified a set of the attributes correlating with a protein's propensity to crystallize and implemented a Support Vector Machine (SVM) classifier based on these. We have created applications to analyze and provide optimal boundary information for query sequences and to visualize the data. These tools are available via the web site http://bioinformatics.anl.gov/cgi-bin/tools/pdpredictor .
| S-EPMC3366497 | biostudies-literature
Project description:Selection of proper targets for the X-ray crystallography will benefit biological research community immensely. Several computational models were proposed to predict propensity of successful protein production and diffraction quality crystallization from protein sequences. We reviewed a comprehensive collection of 22 such predictors that were developed in the last decade. We found that almost all of these models are easily accessible as webservers and/or standalone software and we demonstrated that some of them are widely used by the research community. We empirically evaluated and compared the predictive performance of seven representative methods. The analysis suggests that these methods produce quite accurate propensities for the diffraction-quality crystallization. We also summarized results of the first study of the relation between these predictive propensities and the resolution of the crystallizable proteins. We found that the propensities predicted by several methods are significantly higher for proteins that have high resolution structures compared to those with the low resolution structures. Moreover, we tested a new meta-predictor, MetaXXC, which averages the propensities generated by the three most accurate predictors of the diffraction-quality crystallization. MetaXXC generates putative values of resolution that have modest levels of correlation with the experimental resolutions and it offers the lowest mean absolute error when compared to the seven considered methods. We conclude that protein sequences can be used to fairly accurately predict whether their corresponding protein structures can be solved using X-ray crystallography. Moreover, we also ascertain that sequences can be used to reasonably well predict the resolution of the resulting protein crystals.
| S-EPMC7001581 | biostudies-literature
Project description:MotivationX-ray crystallography-based protein structure determination, which accounts for majority of solved structures, is characterized by relatively low success rates. One solution is to build tools which support selection of targets that are more likely to crystallize. Several in silico methods that predict propensity of diffraction-quality crystallization from protein chains were developed. We show that the quality of their predictions drops when applied to more recent crystallization trails, which calls for new solutions. We propose a novel approach that alleviates drawbacks of the existing methods by using a recent dataset and improved protocol to annotate progress along the crystallization process, by predicting the success of the entire process and steps which result in the failed attempts, and by utilizing a compact and comprehensive set of sequence-derived inputs to generate accurate predictions.ResultsThe proposed PPCpred (predictor of protein Production, Purification and Crystallization) predict propensity for production of diffraction-quality crystals, production of crystals, purification and production of the protein material. PPCpred utilizes comprehensive set of inputs based on energy and hydrophobicity indices, composition of certain amino acid types, predicted disorder, secondary structure and solvent accessibility, and content of certain buried and exposed residues. Our method significantly outperforms alignment-based predictions and several modern crystallization propensity predictors. Receiver operating characteristic (ROC) curves show that PPCpred is particularly useful for users who desire high true positive (TP) rates, i.e. low rate of mispredictions for solvable chains. Our model reveals several intuitive factors that influence the success of individual steps and the entire crystallization process, including the content of Cys, buried His and Ser, hydrophobic/hydrophilic segments and the number of predicted disordered segments.Availabilityhttp://biomine.ece.ualberta.ca/PPCpred/.Contactlkurgan@ece.ualberta.ca.
| S-EPMC3117383 | biostudies-literature
Project description:This study describes an efficient multiparallel automated workflow of cloning, expression, purification, and crystallization of a large set of construct variants for isolated protein domains aimed at structure determination by X-ray crystallography. This methodology is applied to MAPKAP kinase 2, a key enzyme in the inflammation pathway and thus an attractive drug target. The study reveals a distinct subset of truncation variants with improved crystallization properties. These constructs distinguish themselves by increased solubility and stability during a parallel automated multistep purification process including removal of the recombinant tag. High-throughput protein melting point analysis characterizes this subset of constructs as particularly thermostable. Both parallel purification screening and melting point determination clearly identify residue 364 as the optimal C terminus for the kinase domain. Moreover, all three constructs that ultimately crystallized feature this C terminus. At the N terminus, only three amino acids differentiate a noncrystallizing from a crystallizing construct. This study addresses the very common issues associated with difficult to crystallize proteins, those of solubility and stability, and the crucial importance of particular residues in the formation of crystal contacts. A methodology is suggested that includes biophysical measurements to efficiently identify and produce construct variants of isolated protein domains which exhibit higher crystallization propensity.
| S-EPMC2242438 | biostudies-literature
Project description:X-ray crystallography is the main tool for structural determination of proteins. Yet, the underlying crystallization process is costly, has a high attrition rate and involves a series of trial-and-error attempts to obtain diffraction-quality crystals. The Structural Genomics Consortium aims to systematically solve representative structures of major protein-fold classes using primarily high-throughput X-ray crystallography. The attrition rate of these efforts can be improved by selection of proteins that are potentially easier to be crystallized. In this context, bioinformatics approaches have been developed to predict crystallization propensities based on protein sequences. These approaches are used to facilitate prioritization of the most promising target proteins, search for alternative structural orthologues of the target proteins and suggest designs of constructs capable of potentially enhancing the likelihood of successful crystallization. We reviewed and compared nine predictors of protein crystallization propensity. Moreover, we demonstrated that integrating selected outputs from multiple predictors as candidate input features to build the predictive model results in a significantly higher predictive performance when compared to using these predictors individually. Furthermore, we also introduced a new and accurate predictor of protein crystallization propensity, Crysf, which uses functional features extracted from UniProt as inputs. This comprehensive review will assist structural biologists in selecting the most appropriate predictor, and is also beneficial for bioinformaticians to develop a new generation of predictive algorithms.
| S-EPMC6171492 | biostudies-literature
Project description:BackgroundCrystallization is a major bottleneck in the process of macromolecular structure determination by X-ray crystallography. Successful crystallization requires the formation of nuclei and their subsequent growth to crystals of suitable size. Crystal growth generally occurs spontaneously in a supersaturated solution as a result of homogenous nucleation. However, in a typical sparse matrix screening experiment, precipitant and protein concentration are not sampled extensively, and supersaturation conditions suitable for nucleation are often missed.Methodology/principal findingsWe tested the effect of nine potential heterogenous nucleating agents on crystallization of ten test proteins in a sparse matrix screen. Several nucleating agents induced crystal formation under conditions where no crystallization occurred in the absence of the nucleating agent. Four nucleating agents: dried seaweed; horse hair; cellulose and hydroxyapatite, had a considerable overall positive effect on crystallization success. This effect was further enhanced when these nucleating agents were used in combination with each other.Conclusions/significanceOur results suggest that the addition of heterogeneous nucleating agents increases the chances of crystal formation when using sparse matrix screens.
| S-EPMC2034409 | biostudies-literature
Project description:Gramicidin is an apolar pentadecapeptide antibiotic consisting of alternating D-and L-amino acids. It functions, in part, by creating pores in membranes of susceptible cells rendering them leaky to monovalent cations. The peptide should be able to traverse the host membrane either as a double stranded, intertwined double helix (DSDH) or as a head-to-head single stranded helix (HHSH). Current structure models are based on macromolecular X-ray crystallography (MX) and nuclear magnetic resonance (NMR). However, the HHSH form has only been observed by NMR. The shape and size of the different gramicidin conformations differ. We speculated therefore that reconstituting it into a lipidic mesophase with bilayers of different microstructures would preferentially stabilize one form over the other. By using such mesophases for in meso crystallogenesis the expectation was that at least one would generate crystals of gramicidin in the HHSH form for structure determination by MX. This was tested using commercial and in-house synthesised lipids that support in meso crystallogenesis. Lipid acyl chain lengths were varied from 14 to 18 carbons to provide mesophases with a range of bilayer thicknesses. Unexpectedly, all lipids produced high quality, structure-grade crystals with gramicidin only in the DSDH conformation.
| S-EPMC3427940 | biostudies-literature
Project description:Protein crystallization is one of the major bottlenecks in protein structure elucidation with new strategies being constantly developed to improve the chances of crystallization. Generally, well-ordered epitopes possessing complementary surface and capable of producing stable inter-protein interactions generate a regular three-dimensional arrangement of protein molecules which eventually results in a crystal lattice. Metals, when used for crystallization, with their various coordination numbers and geometries, can generate such epitopes mediating protein oligomerization and/or establish crystal contacts. Some examples of metal-mediated oligomerization and crystallization together with our experience on metal-mediated crystallization of a putative rRNA methyltransferase from Sinorhizobium meliloti are presented. Analysis of crystal structures from protein data bank (PDB) using a non-redundant data set with a 90% identity cutoff, reveals that around 67% of proteins contain at least one metal ion, with ?14% containing combination of metal ions. Interestingly, metal containing conditions in most commercially available and popular crystallization kits generally contain only a single metal ion, with combinations of metals only in a very few conditions. Based on the results presented in this review, it appears that the crystallization screens need expansion with systematic screening of metal ions that could be crucial for stabilizing the protein structure or for establishing crystal contact and thereby aiding protein crystallization.
| S-EPMC5563152 | biostudies-literature
Project description:BackgroundMany studies have correlated characteristics of amino acids with crystallization propensity, as part of the effort to determine the factors that affect the propensity of protein crystallization. However, these characteristics are constant; that is, the encoded amino acid sequences have the same value for each type of amino acid. To overcome this inflexibility, three dynamic characteristics of amino acids and protein were introduced to analyze the crystallization propensity of proteins. Both logistic regression and neural network models were used to correlate each of two dynamic characteristics with the crystallization propensity of 301 proteins from Arabidopsis thaliana, and their results were compared with those obtained from each of 531 constant amino acid characteristics, which served as the benchmark.ResultsThe neural network model was more powerful for predicting the crystallization propensity of proteins than the logistic regression model. Compared with the benchmark, the dynamic characteristics of amino acids provided good prediction results for the crystallization propensity, and the distribution probability gave the highest sensitivity. Using 90 % accuracy as a cutoff point, the predictable portion of A. thaliana portions was ranked, and the statistical analysis showed that the larger the predictable portion, the better the prediction.ConclusionsThese results demonstrate that dynamic characteristics have a certain relationship with the crystallization propensity, and they could be helpful for the prediction of protein crystallization, which may provide a theoretical concept for certain proteins before conducting experimental crystallization.
| S-EPMC4657326 | biostudies-literature
Project description:BACKGROUND: Few high-resolution structures of integral membranes proteins are available, as crystallization of such proteins needs yet to overcome too many technical limitations. Nevertheless, prediction of their transmembrane (TM) structure by bioinformatics tools provides interesting insights on the topology of these proteins. METHODS: We describe here how to extract new information from the analysis of hydrophobicity variations or hydrophobic pulses (HPulses) in the sequence of integral membrane proteins using the Hydrophobic Pulse Predictor, a new tool we developed for this purpose. To analyze the primary sequence of 70 integral membrane proteins we defined two levels of analysis: G1-HPulses for sliding windows of n = 2 to 6 and G2-HPulses for sliding windows of n = 12 to 16. RESULTS: The G2-HPulse analysis of 541 transmembrane helices allowed the definition of the new concept of transmembrane unit (TMU) that groups together transmembrane helices and segments with potential adjacent structures. In addition, the G1-HPulse analysis identified helix irregularities that corresponded to kinks, partial helices or unannotated structural events. These irregularities could represent key dynamic elements that are alternatively activated depending on the channel status as illustrated by the crystal structures of the lactose permease in different conformations. CONCLUSIONS: Our results open a new way in the understanding of transmembrane secondary structures: hydrophobicity through hydrophobic pulses strongly impacts on such embedded structures and is not confined to define the transmembrane status of amino acids.
| S-EPMC3110554 | biostudies-literature