Protein crystallizability

Arabinogalactan Proteins Are the Possible Extracellular Molecules for Binding Exogenous Cerium(III) in the Acidic Environment Outside Plant Cells

Rare earth elements [REE(III)] increasingly accumulate in the atmosphere and can be absorbed by plant leaves. Our previous study showed that after treatment of REE(III) on plant, REE(III) is first bound by some extracellular molecules of plant cells, and then the endocytosis of leaf cells will be initiated, which terminates the endocytic inertia of leaf cells. Identifying the extracellular molecules for binding REE(III) is the crucial first step to elucidate the mechanism of REE(III) initiating the endocytosis in leaf cells. Unfortunately, the molecules are unknown. Here, cerium(III) [Ce(III)] and Arabidopsis served as a representative of REE(III) and plants, respectively. By using interdisciplinary methods such as confocal laser scanning microscopy, immune-Au and fluorescent labeling, transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS), ultraviolet-visible spectroscopy, circular dichroism spectroscopy, fluorescent spectrometry and molecular dynamics simulation, we obtained two important discoveries: first, the arabinogalactan proteins (AGP) inside leaf cells were sensitively increased in protein expression and recruited onto the plasma membrane; second, to verify whether AGP can bind to Ce(III) in the acidic environment outside leaf cells, by choosing fasciclin-like AGP11 (AtFLA11) as a representative of AGP, we found that Ce(III) can form stable [Ce(H2O)7](III)-AtFLA11 complexes with an apparent binding constant of 1.44 × 10-6 in simulated acidic environment outside leaf cells, in which the secondary and tertiary structure of AtFLA11 was changed. The structural change in AtFLA11 and the interaction between AtFLA11 and Ce(III) were enhanced with increasing the concentration of Ce(III). Therefore, AtFLA11 can serve as Lewis bases to coordinately bind to Ce(III), which broke traditional chemical principle. The results confirmed that AGP can be the possible extracellular molecules for binding to exogenous Ce(III) outside leaf cells, and provided references for elucidating the mechanism of REE(III) initiating the endocytosis in leaf cells.

Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity

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.

fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization

Background

Development of predictors of propensity of protein sequences for successful crystallization has been actively pursued for over a decade. A few novel methods that expanded the scope of these predictions to address additional steps of protein production and structure determination pipelines were released in recent years. The predictive performance of the current methods is modest. This is because the only input that they use is the protein sequence and since the experimental annotations of these data might be inconsistent given that they were collected across many laboratories and centers. However, even these modest levels of predictive quality are still practical compared to the reported low success rates of crystallization, which are below 10%. We focus on another important aspect related to a high computational cost of running the predictors that offer the expanded scope.

Results

We introduce a novel fDETECT webserver that provides very fast and modestly accurate predictions of the success of protein production, purification, crystallization, and structure determination. Empirical tests on two datasets demonstrate that fDETECT is more accurate than the only other similarly fast method, and similarly accurate and three orders of magnitude faster than the currently most accurate predictors. Our method predicts a single protein in about 120 milliseconds and needs less than an hour to generate the four predictions for an entire human proteome. Moreover, we empirically show that fDETECT secures similar levels of predictive performance when compared with four representative methods that only predict success of crystallization, while it also provides the other three predictions. A webserver that implements fDETECT is available at http://biomine.cs.vcu.edu/servers/fDETECT/.

Conclusions

fDETECT is a computational tool that supports target selection for protein production and X-ray crystallography-based structure determination. It offers predictive quality that matches or exceeds other state-of-the-art tools and is especially suitable for the analysis of large protein sets.

Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures

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.

Protein stability: a crystallographer's perspective

Protein stability is a topic of major interest for the biotechnology, pharmaceutical and food industries, in addition to being a daily consideration for academic researchers studying proteins. An understanding of protein stability is essential for optimizing the expression, purification, formulation, storage and structural studies of proteins. In this review, discussion will focus on factors affecting protein stability, on a somewhat practical level, particularly from the view of a protein crystallographer. The differences between protein conformational stability and protein compositional stability will be discussed, along with a brief introduction to key methods useful for analyzing protein stability. Finally, tactics for addressing protein-stability issues during protein expression, purification and crystallization will be discussed.

Developments and recent advancements in the field of endogenous amino acid selective bond forming reactions for bioconjugation

Bioconjugation methodologies have proven to play a central enabling role in the recent development of biotherapeutics and chemical biology approaches. Recent endeavours in these fields shed light on unprecedented chemical challenges to attain bioselectivity, biocompatibility, and biostability required by modern applications. In this review the current developments in various techniques of selective bond forming reactions of proteins and peptides were highlighted. The utility of each endogenous amino acid-selective conjugation methodology in the fields of biology and protein science has been surveyed with emphasis on the most relevant among reported transformations; selectivity and practical use have been discussed.