BackgroundInfluenza 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 FindingsMethodologies 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/SignificanceThis 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.

BackgroundGenetic variation and rapid evolution are hallmarks of RNA viruses, the result of high mutation rates in RNA replication and selection of mutants that enhance viral adaptation, including the escape from host immune responses. Variability is uneven across the genome because mutations resulting in a deleterious effect on viral fitness are restricted. RNA viruses are thus marked by protein sites permissive to multiple mutations and sites critical to viral structure-function that are evolutionarily robust and highly conserved. Identification and characterization of the historical dynamics of the conserved sites have relevance to multiple applications, including potential targets for diagnosis, and prophylactic and therapeutic purposes.Methodology/Principal FindingsWe describe a large-scale identification and analysis of evolutionarily highly conserved amino acid sequences of the entire dengue virus (DENV) proteome, with a focus on sequences of 9 amino acids or more, and thus immune-relevant as potential T-cell determinants. DENV protein sequence data were collected from the NCBI Entrez protein database in 2005 (9,512 sequences) and again in 2007 (12,404 sequences). Forty-four (44) sequences (pan-DENV sequences), mainly those of nonstructural proteins and representing ∼15% of the DENV polyprotein length, were identical in 80% or more of all recorded DENV sequences. Of these 44 sequences, 34 (∼77%) were present in ≥95% of sequences of each DENV type, and 27 (∼61%) were conserved in other Flaviviruses. The frequencies of variants of the pan-DENV sequences were low (0 to ∼5%), as compared to variant frequencies of ∼60 to ∼85% in the non pan-DENV sequence regions. We further showed that the majority of the conserved sequences were immunologically relevant: 34 contained numerous predicted human leukocyte antigen (HLA) supertype-restricted peptide sequences, and 26 contained T-cell determinants identified by studies with HLA-transgenic mice and/or reported to be immunogenic in humans.Conclusions/SignificanceForty-four (44) pan-DENV sequences of at least 9 amino acids were highly conserved and identical in 80% or more of all recorded DENV sequences, and the majority were found to be immune-relevant by their correspondence to known or putative HLA-restricted T-cell determinants. The conservation of these sequences through the entire recorded DENV genetic history supports their possible value for diagnosis, prophylactic and/or therapeutic applications. The combination of bioinformatics and experimental approaches applied herein provides a framework for large-scale and systematic analysis of conserved and variable sequences of other pathogens, in particular, for rapidly mutating viruses, such as influenza A virus and HIV.

Background: The identification of mutations that confer unique properties to a pathogen, such as host range, is of fundamental importance in the fight against disease. This paper describes a novel method for identifying amino acid sites that distinguish specific sets of protein sequences, by comparative analysis of matched alignments. The use of mutual information to identify distinctive residues responsible for functional variants makes this approach highly suitable for analyzing large sets of sequences. To support mutual information analysis, we developed the AVANA software, which utilizes sequence annotations to select sets for comparison, according to user-specified criteria. The method presented was applied to an analysis of influenza A PB2 protein sequences, with the objective of identifying the components of adaptation to human-to-human transmission, and reconstructing the mutation history of these components.

BackgroundThere is widespread concern that H5N1 avian influenza A viruses will emerge as a pandemic threat, if they become capable of human-to-human (H2H) transmission. Avian strains lack this capability, which suggests that it requires important adaptive mutations. We performed a large-scale comparative analysis of proteins from avian and human strains, to produce a catalogue of mutations associated with H2H transmissibility, and to detect their presence in avian isolates.Methodology/Principal FindingsWe constructed a dataset of influenza A protein sequences from 92,343 public database records. Human and avian sequence subsets were compared, using a method based on mutual information, to identify characteristic sites where human isolates present conserved mutations. The resulting catalogue comprises 68 characteristic sites in eight internal proteins. Subtype variability prevented the identification of adaptive mutations in the hemagglutinin and neuraminidase proteins. The high number of sites in the ribonucleoprotein complex suggests interdependence between mutations in multiple proteins. Characteristic sites are often clustered within known functional regions, suggesting their functional roles in cellular processes. By isolating and concatenating characteristic site residues, we defined adaptation signatures, which summarize the adaptive potential of specific isolates. Most adaptive mutations emerged within three decades after the 1918 pandemic, and have remained remarkably stable thereafter. Two lineages with stable internal protein constellations have circulated among humans without reassorting. On the contrary, H5N1 avian and swine viruses reassort frequently, causing both gains and losses of adaptive mutations.ConclusionsHuman host adaptation appears to be complex and systemic, involving nearly all influenza proteins. Adaptation signatures suggest that the ability of H5N1 strains to infect humans is related to the presence of an unusually high number of adaptive mutations. However, these mutations appear unstable, suggesting low pandemic potential of H5N1 in its current form. In addition, adaptation signatures indicate that pandemic H1N1/09 strain possesses multiple human-transmissibility mutations, though not an unusually high number with respect to swine strains that infected humans in the past. Adaptation signatures provide a novel tool for identifying zoonotic strains with the potential to infect humans.

In this article, we present a new technique for the rapid and precise docking of peptides to MHC class I and class II receptors. Our docking procedure consists of three steps: (1) peptide residues near the ends of the binding groove are docked by using an efficient pseudo-Brownian rigid body docking procedure followed by (2) loop closure of the intervening backbone structure by satisfaction of spatial constraints, and subsequently, (3) the refinement of the entire backbone and ligand interacting side chains and receptor side chains experiencing atomic clash at the MHC receptor-peptide interface. The method was tested by remodeling of 40 nonredundant complexes of at least 3.00 Å resolution for which three-dimensional structural information is available and independently for docking peptides derived from 15 nonredundant complexes into a single template structure. In the first test, 33 out of 40 MHC class I and class II peptides and in the second test, 11 out of 15 MHC-peptide complexes were modeled with a C␣ RMSD < 1.00 Å.

Epitope-based vaccines provide a new strategy for prophylactic and therapeutic application of pathogen-specific immunity. A critical requirement of this strategy is the identification and selection of T-cell epitopes that act as vaccine targets. This study describes current methodologies for the selection process, with dengue virus as a model system. A combination of publicly available bioinformatics algorithms and computational tools are used to screen and select antigen sequences as potential T-cell epitopes of supertype HLA alleles. The selected sequences are tested for biological function by their activation of T-cells of HLA transgenic mice and of pathogen infected subjects. This approach provides an experimental basis for the design of pathogen specific, T-cell epitopebased vaccines that are targeted to majority of the genetic variants of the pathogen, and are effective for a broad range of differences in human leukocyte antigens among the global human population.

Antimicrobial peptides (AMPs) are important components of the innate immune system of many species. These peptides are found in eukaryotes, including mammals, amphibians, insects and plants, as well as in prokaryotes. Other than having pathogen-lytic properties, these peptides have other activities like antitumor activity, mitogen activity, or they may act as signaling molecules. Their short length, fast and efficient action against microbes and low toxicity to mammals have made them potential candidates as peptide drugs. In many cases they are effective against pathogens that are resistant to conventional antibiotics. They can serve as natural templates for the design of novel antimicrobial drugs. Although there are vast amounts of data on natural AMPs, they are not available through one central resource. We have developed a comprehensive database (ANTIMIC, http://research.i2r. a-star.edu.sg/Templar/DB/ANTIMIC/) of known and putative AMPs, which contains approximately 1700 of these peptides. The database is integrated with tools to facilitate efficient extraction of data and their analysis at molecular level, as well as search for new AMPs. These tools include BLAST, PDB structure viewer and the Antimic profile module.

BackgroundMajority of influenza A viruses reside and circulate among animal populations, seldom infecting humans due to host range restriction. Yet when some avian strains do acquire the ability to overcome species barrier, they might become adapted to humans, replicating efficiently and causing diseases, leading to potential pandemic. With the huge influenza A virus reservoir in wild birds, it is a cause for concern when a new influenza strain emerges with the ability to cross host species barrier, as shown in light of the recent H7N9 outbreak in China. Several influenza proteins have been shown to be major determinants in host tropism. Further understanding and determining host tropism would be important in identifying zoonotic influenza virus strains capable of crossing species barrier and infecting humans.ResultsIn this study, computational models for 11 influenza proteins have been constructed using the machine learning algorithm random forest for prediction of host tropism. The prediction models were trained on influenza protein sequences isolated from both avian and human samples, which were transformed into amino acid physicochemical properties feature vectors. The results were highly accurate prediction models (ACC>96.57; AUC>0.980; MCC>0.916) capable of determining host tropism of individual influenza proteins. In addition, features from all 11 proteins were used to construct a combined model to predict host tropism of influenza virus strains. This would help assess a novel influenza strain's host range capability.ConclusionsFrom the prediction models constructed, all achieved high prediction performance, indicating clear distinctions in both avian and human proteins. When used together as a host tropism prediction system, zoonotic strains could potentially be identified based on different protein prediction results. Understanding and predicting host tropism of influenza proteins lay an important foundation for future work in constructing computation models capable of directly predicting interspecies transmission of influenza viruses. The models are available for prediction at http://fluleap.bic.nus.edu.sg.