BMC Structural Biology最新文献

White Spot Syndrome Virus (WSSV) is an enveloped double-stranded DNA virus which causes mortality of several species of shrimp, being considered one of the main pathogens that affects global shrimp farming. This virus presents a complex genome of ~?300?kb and viral isolates that present genomes with great identity. Despite this conservation, some variable regions in the WSSV genome occur in coding regions, and these putative proteins may have some relationship with viral adaptation and virulence mechanisms. Until now, the functions of these proteins were little studied. In this work, sequences and putative proteins encoded by WSSV variable regions were characterized in silico.

The in silico approach enabled determining the variability of some sequences, as well as the identification of some domains resembling the Formin homology 2, RNA recognition motif, Xeroderma pigmentosum group D repair helicase, Hemagglutinin and Ankyrin motif. The information obtained from the sequences and the analysis of secondary and tertiary structure models allow to infer that some of these proteins possibly have functions related to protein modulation/degradation, intracellular transport, recombination and endosome fusion events.

The bioinformatics approaches were efficient in generating three-dimensional models and to identify domains, thereby enabling to propose possible functions for the putative polypeptides produced by the ORFs wsv129, wsv178, wsv249, wsv463a, wsv477, wsv479, wsv492, and wsv497.

Plasmodium vivax merozoite surface protein 3α (PvMSP3α) is a promising vaccine candidate which has shown strong association with immunogenicity and protectiveness. Its use is however complicated by evolutionary plasticity features which enhance immune evasion. Low complexity regions (LCRs) provide plasticity in surface proteins of Plasmodium species, but its implication in vaccine design remain unexplored. Here population genetic, comparative phylogenetic and structural biology analysis was performed on the gene encoding PvMSP3α.

Three LCRs were found in PvMSP3α block II. Both the predicted tertiary structure of the protein and the phylogenetic trees based on this region were influenced by the presence of the LCRs. The LCRs were mainly B cell epitopes within or adjacent. In addition a repeat motif mimicking one of the B cell epitopes was found within the PvMSP3a block II low complexity region. This particular B cell epitope also featured rampant alanine substitutions which might impair antibody binding.

The findings indicate that PvMSP3α block II possesses LCRs which might confer a strong phenotypic plasticity. The phenomenon of phenotypic plasticity and implication of LCRs in malaria immunology in general and vaccine candidate genes in particular merits further exploration.

Computational models of RNA 3D structure often present various inaccuracies caused by simplifications used in structure prediction methods, such as template-based modeling or coarse-grained simulations. To obtain a high-quality model, the preliminary RNA structural model needs to be refined, taking into account atomic interactions. The goal of the refinement is not only to improve the local quality of the model but to bring it globally closer to the true structure.

We present QRNAS, a software tool for fine-grained refinement of nucleic acid structures, which is an extension of the AMBER simulation method with additional restraints. QRNAS is capable of handling RNA, DNA, chimeras, and hybrids thereof, and enables modeling of nucleic acids containing modified residues.

We demonstrate the ability of QRNAS to improve the quality of models generated with different methods. QRNAS was able to improve MolProbity scores of NMR structures, as well as of computational models generated in the course of the RNA-Puzzles experiment. The overall geometry improvement may be associated with increased model accuracy, especially on the level of correctly modeled base-pairs, but the systematic improvement of root mean square deviation to the reference structure should not be expected. The method has been integrated into a computational modeling workflow, enabling improved RNA 3D structure prediction.

The THAP (Thanatos Associated Proteins) protein family in humans is implicated in various important cellular processes like epigenetic regulation, maintenance of pluripotency, transposition and disorders like cancers and hemophilia. The human THAP protein family which consists of twelve members of different lengths has a well characterized amino terminal, zinc-coordinating, DNA-binding domain called the THAP domain. However, the carboxy terminus of most THAP proteins is yet to be structurally characterized. A coiled coil region is known to help in protein oligomerization in THAP1 and THAP11. It is not known if other human THAP proteins oligomerize. We have used bioinformatic tools to explore the possibility of dimerization of THAP proteins via a coiled coil region.

Classification of human THAP protein into three size based groups led to the identification of an evolutionarily conserved alpha helical region, downstream of the amino terminal THAP domain. Secondary structure predictions, alpha helical wheel plots and protein models demonstrated the strong possibility of coiled coil formation in this conserved, leucine rich region of all THAP proteins except THAP10.

The identification of a predicted oligomerization region in the human THAP protein family opens new directions to investigate the members of this protein family.

It had long been thought that a protein exhibits its specific function through its own specific 3D-structure under physiological conditions. However, subsequent research has shown that there are many proteins without specific 3D-structures under physiological conditions, so-called intrinsically disordered proteins (IDPs). This study presents a new technique for predicting intrinsically disordered regions in a protein, based on our average distance map (ADM) technique. The ADM technique was developed to predict compact regions or structural domains in a protein. In a protein containing partially disordered regions, a domain region is likely to be ordered, thus it is unlikely that a disordered region would be part of any domain. Therefore, the ADM technique is expected to also predict a disordered region between domains.

The results of our new technique are comparable to the top three performing techniques in the community-wide CASP10 experiment. We further discuss the case of p53, a tumor-suppressor protein, which is the most significant protein among cell cycle regulatory proteins. This protein exhibits a disordered character as a monomer but an ordered character when two p53s form a dimer.

Our technique can predict the location of an intrinsically disordered region in a protein with an accuracy comparable to the best techniques proposed so far. Furthermore, it can also predict a core region of IDPs forming definite 3D structures through interactions, such as dimerization. The technique in our study may also serve as a means of predicting a disordered region which would become an ordered structure when binding to another protein.

Ribose-phosphate pyrophosphokinase (EC 2.7.6.1) is an enzyme that catalyzes the ATP-dependent conversion of ribose-5-phosphate to phosphoribosyl pyrophosphate. The reaction product is a key precursor for the biosynthesis of purine and pyrimidine nucleotides.

We report the 2.2?? crystal structure of the E. coli ribose-phosphate pyrophosphobinase (EcKPRS). The protein has two type I phosphoribosyltransferase folds, related by 2-fold pseudosymmetry. The propeller-shaped homohexameric structure of KPRS is composed of a trimer of dimers, with the C-terminal domains forming the dimeric blades of the propeller and the N-terminal domains forming the hexameric core. The key, conserved active site residues are well-defined in the structure and positioned appropriately to bind substrates, adenosine monophosphate and ribose-5-phosphate. The allosteric site is also relatively well conserved but, in the EcKPRS structure, several residues from a flexible loop occupy the site where the allosteric modulator, adenosine diphosphate, is predicted to bind. The presence of the loop in the allosteric site may be an additional level of regulation, whereby low affinity molecules are precluded from binding.

Overall, this study details key structural features of an enzyme that catalyzes a critical step in nucleotide metabolism. This work provides a framework for future studies of this important protein and, as nucleotides are critical for viability, may serve as a foundation for the development of novel anti-bacterial drugs.

Three transketolase genes have been identified in the human genome to date: transketolase (TKT), transketolase-like 1 (TKTL1) and transketolase-like 2 (TKTL2). Altered TKT functionality is strongly implicated in the development of diabetes and various cancers, thus offering possible therapeutic utility. It will be of great value to know whether TKTL1 and TKTL2 are, similarly, potential therapeutic targets. However, it remains unclear whether TKTL1 and TKTL2 are functional transketolases.

Homology modelling of TKTL1 and TKTL2 using TKT as template, revealed that both TKTL1 and TKTL2 could assume a folded structure like TKT. TKTL1/2 presented a cleft of suitable dimensions between the homodimer surfaces that could accommodate the co-factor-substrate. An appropriate cavity and a hydrophobic nodule were also present in TKTL1/2, into which the diphosphate group fitted, and that was implicated in aminopyrimidine and thiazole ring binding in TKT, respectively. The presence of several identical residues at structurally equivalent positions in TKTL1/2 and TKT identified a network of interactions between the protein and co-factor-substrate, suggesting the functional fidelity of TKTL1/2 as transketolases.

Our data support the hypothesis that TKTL1 and TKTL2 are functional transketolases and represent novel therapeutic targets for diabetes and cancer.

To perform a three-dimensional (3-D) reconstruction of electron cryomicroscopy (cryo-EM) images of viruses, it is necessary to determine the similarity of image blocks of the two-dimensional (2-D) projections of the virus. The projections containing high resolution information are typically very noisy. Instead of the traditional Euler metric, this paper proposes a new method, based on the geodesic metric, to measure the similarity of blocks.

Our method is a 2-D image denoising approach. A data set of 2243 cytoplasmic polyhedrosis virus (CPV) capsid particle images in different orientations was used to test the proposed method. Relative to Block-matching and three-dimensional filtering (BM3D), Stein’s unbiased risk estimator (SURE), Bayes shrink and K-means singular value decomposition (K-SVD), the experimental results show that the proposed method can achieve a peak signal-to-noise ratio (PSNR) of 45.65. The method can remove the noise from the cryo-EM image and improve the accuracy of particle picking.

The main contribution of the proposed model is to apply the geodesic distance to measure the similarity of image blocks. We conclude that manifold learning methods can effectively eliminate the noise of the cryo-EM image and improve the accuracy of particle picking.