Synthesis and characterization of amino acid deletion analogs of κ-hefutoxin 1, a scorpion toxin on potassium channels.

Nine analogs of scorpion toxin peptide κ-hefutoxin 1 were synthesized by stepwise deletion of its amino acid residues. Disulfide bond pairings of the synthetic analogs were confirmed by enzymatic digestion followed by MALDI-TOF-MS measurements. Functional characterization shows that analogs in which N-terminal residues were deleted retained biological activity, whereas deletion of middle part residues resulted in loss of activity. Furthermore, κ-hefutoxin 1 and analogs were subjected to a screening on voltage-gated potassium channels in order to determine their subtype selectivity. It is shown that κ-hefutoxin 1 is suitable as template for peptidomimetics in order to design small peptide-based therapeutic compounds.

Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes.

BACKGROUND: T-cell epitopes that promiscuously bind to multiple alleles of a human leukocyte antigen (HLA) supertype are prime targets for development of vaccines and immunotherapies because they are relevant to a large proportion of the human population. The presence of clusters of promiscuous T-cell epitopes, immunological hotspots, has been observed in several antigens. These clusters may be exploited to facilitate the development of epitope-based vaccines by selecting a small number of hotspots that can elicit all of the required T-cell activation functions. Given the large size of pathogen proteomes, including of variant strains, computational tools are necessary for automated screening and selection of immunological hotspots. RESULTS: Hotspot Hunter is a web-based computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes through analysis of antigenic diversity. It allows screening and selection of hotspots specific to four common HLA supertypes, namely HLA class I A2, A3, B7 and class II DR. The system uses Artificial Neural Network and Support Vector Machine methods as predictive engines. Soft computing principles were employed to integrate the prediction results produced by both methods for robust prediction performance. Experimental validation of the predictions showed that Hotspot Hunter can successfully identify majority of the real hotspots. Users can predict hotspots from a single protein sequence, or from a set of aligned protein sequences representing pathogen proteome. The latter feature provides a global view of the localizations of the hotspots in the proteome set, enabling analysis of antigenic diversity and shift of hotspots across protein variants. The system also allows the integration of prediction results of the four supertypes for identification of hotspots common across multiple supertypes. The target selection feature of the system shortlists candidate peptide hotspots for the formulation of an epitope-based vaccine that could be effective against multiple variants of the pathogen and applicable to a large proportion of the human population. CONCLUSION: Hotspot Hunter is publicly accessible at http://antigen.i2r.a-star.edu.sg/hh/. It is a new generation computational tool aiding in epitope-based vaccine design.

Evolutionarily conserved protein sequences of influenza a viruses, avian and human, as vaccine targets.

BACKGROUND: Influenza A viruses generate an extreme genetic diversity through point mutation and gene segment exchange, resulting in many new strains that emerge from the animal reservoirs, among which was the recent highly pathogenic H5N1 virus. This genetic diversity also endows these viruses with a dynamic adaptability to their habitats, one result being the rapid selection of genomic variants that resist the immune responses of infected hosts. With the possibility of an influenza A pandemic, a critical need is a vaccine that will recognize and protect against any influenza A pathogen. One feasible approach is a vaccine containing conserved immunogenic protein sequences that represent the genotypic diversity of all current and future avian and human influenza viruses as an alternative to current vaccines that address only the known circulating virus strains. METHODOLOGY/PRINCIPAL FINDINGS: Methodologies for large-scale analysis of the evolutionary variability of the influenza A virus proteins recorded in public databases were developed and used to elucidate the amino acid sequence diversity and conservation of 36,343 sequences of the 11 viral proteins of the recorded virus isolates of the past 30 years. Technologies were also applied to identify the conserved amino acid sequences from isolates of the past decade, and to evaluate the predicted human lymphocyte antigen (HLA) supertype-restricted class I and II T-cell epitopes of the conserved sequences. Fifty-five (55) sequences of 9 or more amino acids of the polymerases (PB2, PB1, and PA), nucleoprotein (NP), and matrix 1 (M1) proteins were completely conserved in at least 80%, many in 95 to 100%, of the avian and human influenza A virus isolates despite the marked evolutionary variability of the viruses. Almost all (50) of these conserved sequences contained putative supertype HLA class I or class II epitopes as predicted by 4 peptide-HLA binding algorithms. Additionally, data of the Immune Epitope Database (IEDB) include 29 experimentally identified HLA class I and II T-cell epitopes present in 14 of the conserved sequences. CONCLUSIONS/SIGNIFICANCE: This study of all reported influenza A virus protein sequences, avian and human, has identified 55 highly conserved sequences, most of which are predicted to have immune relevance as T-cell epitopes. This is a necessary first step in the design and analysis of a polyepitope, pan-influenza A vaccine. In addition to the application described herein, these technologies can be applied to other pathogens and to other therapeutic modalities designed to attack DNA, RNA, or protein sequences critical to pathogen function.

PREDNOD, a prediction server for peptide binding to the H-2g7 haplotype of the non-obese diabetic mouse.

The non-obese diabetic (NOD) mouse is a widely used animal model for study of autoimmune diseases, in particular human type 1 diabetes mellitus (T1DM). Identification of the subset of peptides that bind MHC molecules comprising the H-2g7 haplotype of NOD mouse and thereby representing potential NOD T-cell epitopes is important for research into the pathogenesis and immunotherapy of T1DM. The H-2g7 haplotype comprises the MHC class-I molecules Kd and Db and a single class-II molecule I-Ag7. We have developed a prediction system, PREDNOD, for accurate identification of peptides that bind the MHC molecules constituting the H-2g7 haplotype. PREDNOD is accessible at http://antigen.i2r.a-star.edu.sg/Ag7.

SCORPION2: a database for structure-function analysis of scorpion toxins.

Scorpion toxins are important experimental tools for characterization of vast array of ion channels and serve as scaffolds for drug design. General public database entries contain limited annotation whereby rich structure-function information from mutation studies is typically not available. SCORPION2 contains more than 800 records of native and mutant toxin sequences enriched with binding affinity and toxicity information, 624 three-dimensional structures and some 500 references. SCORPION2 has a set of search and prediction tools that allow users to extract and perform specific queries: text searches of scorpion toxin records, sequence similarity search, extraction of sequences, visualization of scorpion toxin structures, analysis of toxic activity, and functional annotation of previously uncharacterized scorpion toxins. The SCORPION2 database is available at http://sdmc.i2r.a-star.edu.sg/scorpion/.

Neural models for predicting viral vaccine targets.

We applied artificial neural networks (ANN) for the prediction of targets of immune responses that are useful for study of vaccine formulations against viral infections. Using a novel data representation, we developed a system termed MULTIPRED that can predict peptide binding to multiple related human leukocyte antigens (HLA). This implementation showed high accuracy in the prediction of the promiscuous peptides that bind to five HLA-A2 allelic variants. MULTIPRED is useful for the identification of peptides that bind multiple HLA-A2 variants as a group. By implementing ANN as a classification engine, we enabled both the prediction of peptides binding to multiple individual HLA-A2 molecules and the prediction of promiscuous binders using a single model. The ANN MULTIPRED predicts peptide binding to HLA-A*0205 with excellent accuracy (area under the receiver operating characteristic curve--AROC>0.90), and to HLA-A*0201, HLA-A*0204 and HLA-A*0206 with high accuracy (AROC>0.85). Antigenic regions with high density of binders ("antigenic hot-spots") represent best targets for vaccine design. MULTIPRED not only predicts individual 9-mer binders but also predicts antigenic hot spots. Two HLA-A2 hot-spots in Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) membrane protein were predicted by using MULTIPRED.

MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides.

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules (proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability (area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets--termed T-cell epitope hotspots. MULTIPRED is available at http://antigen.i2r.a-star.edu.sg/multipred/.

PREDBALB/c: a system for the prediction of peptide binding to H2d molecules, a haplotype of the BALB/c mouse.

PRED(BALB/c) is a computational system that predicts peptides binding to the major histocompatibility complex-2 (H2(d)) of the BALB/c mouse, an important laboratory model organism. The predictions include the complete set of H2(d) class I (H2-K(d), H2-L(d) and H2-D(d)) and class II (I-E(d) and I-A(d)) molecules. The prediction system utilizes quantitative matrices, which were rigorously validated using experimentally determined binders and non-binders and also by in vivo studies using viral proteins. The prediction performance of PRED(BALB/c) is of very high accuracy. To our knowledge, this is the first online server for the prediction of peptides binding to a complete set of major histocompatibility complex molecules in a model organism (H2(d) haplotype). PRED(BALB/c) is available at http://antigen.i2r.a-star.edu.sg/predBalbc/.

kappa-Hefutoxin1, a novel toxin from the scorpion Heterometrus fulvipes with unique structure and function. Importance of the functional diad in potassium channel selectivity.

An important and exciting challenge in the postgenomic era is to understand the functions of newly discovered proteins based on their structures. The main thrust is to find the common structural motifs that contribute to specific functions. Using this premise, here we report the purification, solution NMR, and functional characterization of a novel class of weak potassium channel toxins from the venom of the scorpion Heterometrus fulvipes. These toxins, kappa-hefutoxin1 and kappa-hefutoxin2, exhibit no homology to any known toxins. NMR studies indicate that kappa-hefutoxin1 adopts a unique three-dimensional fold of two parallel helices linked by two disulfide bridges without any beta-sheets. Based on the presence of the functional diad (Tyr(5)/Lys(19)) at a distance (6.0 +/- 1.0 A) comparable with other potassium channel toxins, we hypothesized its function as a potassium channel toxin. kappa-Hefutoxin 1 not only blocks the voltage-gated K(+)-channels, Kv1.3 and Kv1.2, but also slows the activation kinetics of Kv1.3 currents, a novel feature of kappa-hefutoxin 1, unlike other scorpion toxins, which are considered solely pore blockers. Alanine mutants (Y5A, K19A, and Y5A/K19A) failed to block the channels, indicating the importance of the functional diad.