Therapeutic potential of venom peptides

Venomous animals have evolved a vast array of peptide toxins for prey capture and defence. These peptides are directed against a wide variety of pharmacological targets, making them an invaluable source of ligands for studying the properties of these targets in different experimental paradigms. A number of these peptides have been used in vivo for proof-of-concept studies, with several having undergone preclinical or clinical development for the treatment of pain, diabetes, multiple sclerosis and cardiovascular diseases. Here we survey the pharmacology of venom peptides and assess their therapeutic prospects.

Zipping up transcription factors: Rational design of anti-Jun and anti-Fos peptides

Various members of the bZip and bHLH-Zip families of eukaryotic transcription factors, including Jun, Fos, and Myc, have been identified as oncoproteins; mutation or deregulated expression of these proteins leads to certain types of cancer. These proteins can only bind to their cognate DNA enhancer sites following homodimerization, or heterodimerization with another family member, via their leucine zipper domain. Thus, a novel anticancer strategy would be to inhibit dimerization of these proteins, thereby blocking their DNA binding and transactivation functions. In this paper we show that it is possible to rationally design leucine zipper peptides that bind with high affinity to the leucine zipper dimerization domains of c-Jun and c-Fos, thus preventing the formation of functional c-Jun homodimers and c-Jun:c-Fos heterodimers; we refer to such peptides as superzippers (SZs). In vivo, c-Jun:SZ and c-Fos:SZ heterodimers should be nonfunctional as they lack one of the two basic domains that are essential for DNA binding. While the transport of a peptidic agent into cells often poses a severe obstacle to its therapeutic use, we show that a 46-residue leucine zipper peptide can be transported into HeLa cells by coupling it to a 17-residue carrier peptide from the Antennapedia homeodomain, thus paving the way for detailed studies of the therapeutic potential of superzipper peptides.

Wolbachia neither induces nor suppresses transcripts encoding antimicrobial peptides

Wolbachia are intracellular maternally inherited microorganisms that are associated with reproductive abnormalities such as cytoplasmic incompatibility (CI), feminization and parthenogenesis in the various arthropod species they infect. Surveys indicate that these bacteria infect more than 16% of all insect species as well as isopods, mites and nematodes, making Wolbachia one of the most ubiquitous parasites yet described. However, nothing is known about the interactions of this bacterium with the host's immune system. We studied the expression of inducible antimicrobial markers in the adults of two Wolbachia infected insect species, Drosophila simulans and Aedes albopictus. The lack of available immune markers in the mosquito species led us to clone part of the defensin gene from this species, which was found to be very similar to the other mosquito defensins cloned from Anopheles gambiae and Aedes aegypti. Comparisons of the expression pattern of the antibacterial markers between Wolbachia-infected and cured lines, and also between bacteria-challenged and unchallenged adults indicated that Wolbachia does not either constitutively induce or suppress the transcription of these antibacterial genes. In addition, no difference in the transcription of these genes was found between double and single Wolbachia-infected strains or between strains in which Wolbachia has different tissue tropisms.

Strategies for identifying and predicting islet autoantigen T-cell epitopes in insulin-dependent diabetes mellitus

T cells recognize peptide epitopes bound to major histocompatibility complex molecules. Human T-cell epitopes have diagnostic and therapeutic applications in autoimmune diseases. However, their accurate definition within an autoantigen by T-cell bioassay, usually proliferation, involves many costly peptides and a large amount of blood, We have therefore developed a strategy to predict T-cell epitopes and applied it to tyrosine phosphatase IA-2, an autoantigen in IDDM, and HLA-DR4(*0401). First, the binding of synthetic overlapping peptides encompassing IA-2 was measured directly to purified DR4. Secondly, a large amount of HLA-DR4 binding data were analysed by alignment using a genetic algorithm and were used to train an artificial neural network to predict the affinity of binding. This bioinformatic prediction method was then validated experimentally and used to predict DR4 binding peptides in IA-2. The binding set encompassed 85% of experimentally determined T-cell epitopes. Both the experimental and bioinformatic methods had high negative predictive values, 92% and 95%, indicating that this strategy of combining experimental results with computer modelling should lead to a significant reduction in the amount of blood and the number of peptides required to define T-cell epitopes in humans.

Papillomavirus virus-like particles can deliver defined CTL epitopes to the MHC class I pathway

To evaluate an antigen delivery system in which exogenous antigen can target the major histocompatibility complex (MHC) class I pathway, a single human papillomavirus (HPV) 16 E7 cytotoxic T lymphocyte (CTL) epitope and a single HIV gp160 CTL epitope were separately fused to the C-terminus or bovine papillomavirus 1 (BPV1) L1 sequence to form hybrid BPV1L1 VLPs. Mice immunized with these hybrid VLPs mounted strong CTL responses against the relevant target cells in the absence of any adjuvants. In addition, the CTL responses induced by immunization with BPV1L1/HPV16E7CTL VLPs protected mice against challenge with E7-transformed tumor cells. Furthermore, a high titer-specific antibody response against BPV1L1 VLPs was also induced, and this antiserum could inhibit papillomavirus-induced agglutination of mouse erythrocytes, suggesting that the antibody may recognize conformational determinates relevant to virus neutralization. These data demonstrate that hybrid BPV1L1 VLPs can be used as carriers to target antigenic epitopes to both the MHC class I and class II pathways, providing a promising strategy for the design of vaccines to prevent virus infection, with the potential to elicit therapeutic virus-specific CTL responses. (C) 1998 Academic Press.

MHCPEP, a database of MHC-binding peptides: update 1997

MHCPEP (http://wehih.wehi.edu.au/mhcpep/) is a curated database comprising over 13 000 peptide sequences known to bind MHC molecules, Entries are compiled from published reports as well as from direct submissions of experimental data, Each entry contains the peptide sequence, its MHC specificity and where available, experimental method, observed activity, binding affinity, source protein and anchor positions, as well as publication references, The present format of the database allows text string matching searches but can easily be converted for use in conjunction with sequence analysis packages. The database can be accessed via Internet using WWW or FTP.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.