Conotoxin Prediction: New Features to Increase Prediction Accuracy.

Conotoxins are toxic, disulfide-bond-rich peptides from cone snail venom that target a wide range of receptors and ion channels with multiple pathophysiological effects. Conotoxins have extraordinary potential for medical therapeutics that include cancer, microbial infections, epilepsy, autoimmune diseases, neurological conditions, and cardiovascular disorders. Despite the potential for these compounds in novel therapeutic treatment development, the process of identifying and characterizing the toxicities of conotoxins is difficult, costly, and time-consuming. This challenge requires a series of diverse, complex, and labor-intensive biological, toxicological, and analytical techniques for effective characterization. While recent attempts, using machine learning based solely on primary amino acid sequences to predict biological toxins (e.g., conotoxins and animal venoms), have improved toxin identification, these methods are limited due to peptide conformational flexibility and the high frequency of cysteines present in toxin sequences. This results in an enumerable set of disulfide-bridged foldamers with different conformations of the same primary amino acid sequence that affect function and toxicity levels. Consequently, a given peptide may be toxic when its cysteine residues form a particular disulfide-bond pattern, while alternative bonding patterns (isoforms) or its reduced form (free cysteines with no disulfide bridges) may have little or no toxicological effects. Similarly, the same disulfide-bond pattern may be possible for other peptide sequences and result in different conformations that all exhibit varying toxicities to the same receptor or to different receptors. We present here new features, when combined with primary sequence features to train machine learning algorithms to predict conotoxins, that significantly increase prediction accuracy.

Reduced Parasite Burden in Children with Falciparum Malaria and Bacteremia Coinfections: Role of Mediators of Inflammation.

Bacteremia and malaria coinfection is a common and life-threatening condition in children residing in sub-Saharan Africa. We previously showed that coinfection with Gram negative (G[-]) enteric Bacilli and Plasmodium falciparum (Pf[+]) was associated with reduced high-density parasitemia (HDP, >10,000 parasites/µL), enhanced respiratory distress, and severe anemia. Since inflammatory mediators are largely unexplored in such coinfections, circulating cytokines were determined in four groups of children (n = 206, aged <3 yrs): healthy; Pf[+] alone; G[-] coinfected; and G[+] coinfected. Staphylococcus aureus and non-Typhi Salmonella were the most frequently isolated G[+] and G[-] organisms, respectively. Coinfected children, particularly those with G[-] pathogens, had lower parasite burden (peripheral and geometric mean parasitemia and HDP). In addition, both coinfected groups had increased IL-4, IL-5, IL-7, IL-12, IL-15, IL-17, IFN-γ, and IFN-α and decreased TNF-α relative to malaria alone. Children with G[-] coinfection had higher IL-1ß and IL-1Ra and lower IL-10 than the Pf[+] group and higher IFN-γ than the G[+] group. To determine how the immune response to malaria regulates parasitemia, cytokine production was investigated with a multiple mediation model. Cytokines with the greatest mediational impact on parasitemia were IL-4, IL-10, IL-12, and IFN-γ. Results here suggest that enhanced immune activation, especially in G[-] coinfected children, acts to reduce malaria parasite burden.

Genotype 1 and global hepatitis C T-cell vaccines designed to optimize coverage of genetic diversity.

Immunological control of hepatitis C virus (HCV) is possible and is probably mediated by host T-cell responses, but the genetic diversity of the virus poses a major challenge to vaccine development. We considered monovalent and polyvalent candidates for an HCV vaccine, including natural, consensus and synthetic 'mosaic' sequence cocktails. Mosaic vaccine reagents were designed using a computational approach first applied to and demonstrated experimentally for human immunodeficiency virus type 1 (HIV-Delta). Mosaic proteins resemble natural proteins, but are assembled from fragments of natural sequences via a genetic algorithm and optimized to maximize the coverage of potential T-cell epitopes (all 9-mers) found in natural sequences and to minimize the inclusion of rare 9-mers to avoid vaccine-specific responses. Genotype 1-specific and global vaccine cocktails were evaluated. Among vaccine candidates considered, polyvalent mosaic sequences provided the best coverage of both known and potential epitopes and had the fewest rare epitopes. A global vaccine based on conserved proteins across genotypes may be feasible, as a five-antigen mosaic cocktail provided 90, 77 and 70% coverage of the Core, NS3 and NS4 proteins, respectively; protein coverage diminished with increased protein variability, dropping to 38% for NS2. For the genotype 1-specific vaccine, the H77 prototype vaccine sequence matched only 50% of the potential epitopes in the population, whilst a polyprotein three-antigen mosaic cocktail increased potential epitope coverage to 83%. More than 75% coverage of all HCV proteins was achieved with a three-antigen mosaic cocktail, suggesting that genotype-specific vaccines could also include the more variable proteins.