Binding specificities and potential roles of isoforms of eukaryotic initiation factor 4E in Leishmania

The 5' cap structure of trypanosomatid mRNAs, denoted cap 4, is a complex structure that contains unusual modifications on the first four nucleotides. We examined the four eukaryotic initiation factor 4E (eIF4E) homologues found in the Leishmania genome database. These proteins, denoted LeishIF4E-1 to LeishIF4E-4, are located in the cytoplasm. They show only a limited degree of sequence homology with known eIF4E isoforms and among themselves. However, computerized structure prediction suggests that the cap-binding pocket is conserved in each of the homologues, as confirmed by binding assays to m(7)GTP, cap 4, and its intermediates. LeishIF4E-1 and LeishIF4E-4 each bind m(7)GTP and cap 4 comparably well, and only these two proteins could interact with the mammalian eIF4E binding protein 4EBP1, though with different efficiencies. 4EBP1 is a translation repressor that competes with eIF4G for the same residues on eIF4E; thus, LeishIF4E-1 and LeishIF4E-4 are reasonable candidates for serving as translation factors. LeishIF4E-1 is more abundant in amastigotes and also contains a typical 3' untranslated region element that is found in amastigote-specific genes. LeishIF4E-2 bound mainly to cap 4 and comigrated with polysomal fractions on sucrose gradients. Since the consensus eIF4E is usually found in 48S complexes, LeishIF4E-2 could possibly be associated with the stabilization of trypanosomatid polysomes. LeishIF4E-3 bound mainly m(7)GTP, excluding its involvement in the translation of cap 4-protected mRNAs. It comigrates with 80S complexes which are resistant to micrococcal nuclease, but its function is yet unknown. None of the isoforms can functionally complement the Saccharomyces cerevisiae eIF4E, indicating that despite their structural conservation, they are considerably diverged.

BETASCAN: probable beta-amyloids identified by pairwise probabilistic analysis

Amyloids and prion proteins are clinically and biologically important beta-structures, whose supersecondary structures are difficult to determine by standard experimental or computational means. In addition, significant conformational heterogeneity is known or suspected to exist in many amyloid fibrils. Recent work has indicated the utility of pairwise probabilistic statistics in beta-structure prediction. We develop here a new strategy for beta-structure prediction, emphasizing the determination of beta-strands and pairs of beta-strands as fundamental units of beta-structure. Our program, BETASCAN, calculates likelihood scores for potential beta-strands and strand-pairs based on correlations observed in parallel beta-sheets. The program then determines the strands and pairs with the greatest local likelihood for all of the sequence's potential beta-structures. BETASCAN suggests multiple alternate folding patterns and assigns relative a priori probabilities based solely on amino acid sequence, probability tables, and pre-chosen parameters. The algorithm compares favorably with the results of previous algorithms (BETAPRO, PASTA, SALSA, TANGO, and Zyggregator) in beta-structure prediction and amyloid propensity prediction. Accurate prediction is demonstrated for experimentally determined amyloid beta-structures, for a set of known beta-aggregates, and for the parallel beta-strands of beta-helices, amyloid-like globular proteins. BETASCAN is able both to detect beta-strands with higher sensitivity and to detect the edges of beta-strands in a richly beta-like sequence. For two proteins (Abeta and Het-s), there exist multiple sets of experimental data implying contradictory structures; BETASCAN is able to detect each competing structure as a potential structure variant. The ability to correlate multiple alternate beta-structures to experiment opens the possibility of computational investigation of prion strains and structural heterogeneity of amyloid. BETASCAN is publicly accessible on the Web at http://betascan.csail.mit.edu.

Allergen motifs and the prediction of allergenicity

We have recently shown that the majority of allergens can be represented by allergen motifs. This observation prompted us to experimentally investigate the synthesized peptides corresponding to the in silico motifs with regard to potential IgE binding and cross-reactions with allergens. Two motifs were selected as examples to conduct in vitro studies. From the first motif, derived from allergenic MnSOD sequences, the motif stretch of the allergen Asp f 6 was selected and synthesized as a peptide (MnSOD Mot). The corresponding full-length MnSOD was also expressed in Escherichia coli and both were compared for IgE reactivity with sera of patients reacting to the MnSOD of Aspergillus fumigatus or Malassezia sympodialis. For the second motif, the invertebrate tropomyosin sequences were aligned and a motif consensus sequence was expressed as a recombinant protein (Trop Mot). The IgE reactivity of Trop Mot was analyzed in ELISA and compared to that of recombinant tropomyosin from the shrimp Penaeus aztecus (rPen a 1) in ImmunoCAP. MnSOD Mot was weakly recognized by some of the tested sera, suggesting that the IgE binding epitopes of a multimeric globular protein such as MnSOD cannot be fully represented by a motif peptide. In contrast, the motif Trop Mot showed the same IgE reactivity as shrimp full-length tropomyosin, indicating that the major allergenic reactivity of a repetitive structure such as tropomyosin can be covered by a motif peptide. Our results suggest that the motif-generating algorithm may be used for identifying major IgE binding structures of coiled-coil proteins.

Mobility and Bandwidth Prediction as a Service in Virtualized LTE Systems

Recently telecommunication industry benefits from infrastructure sharing, one of the most fundamental enablers of cloud computing, leading to emergence of the Mobile Virtual Network Operator (MVNO) concept. The most momentous intents by this approach are the support of on-demand provisioning and elasticity of virtualized mobile network components, based on data traffic load. To realize it, during operation and management procedures, the virtualized services need be triggered in order to scale-up/down or scale-out/in an instance. In this paper we propose an architecture called MOBaaS (Mobility and Bandwidth Availability Prediction as a Service), comprising two algorithms in order to predict user(s) mobility and network link bandwidth availability, that can be implemented in cloud based mobile network structure and can be used as a support service by any other virtualized mobile network services. MOBaaS can provide prediction information in order to generate required triggers for on-demand deploying, provisioning, disposing of virtualized network components. This information can be used for self-adaptation procedures and optimal network function configuration during run-time operation, as well. Through the preliminary experiments with the prototype implementation on the OpenStack platform, we evaluated and confirmed the feasibility and the effectiveness of the prediction algorithms and the proposed architecture.