Engineering stable peptide toxins by means of backbone cyclization: Stabilization of the alpha-conotoxin MII

Conotoxins (CTXs), with their exquisite specificity and potency, have recently created much excitement as drug leads. However, like most peptides, their beneficial activities may potentially be undermined by susceptibility to proteolysis in vivo. By cyclizing the alpha-CTX MII by using a range of linkers, we have engineered peptides that preserve their full activity but have greatly improved resistance to proteolytic degradation. The cyclic MII analogue containing a seven-residue linker joining the N and C termini was as active and selective as the native peptide for native and recombinant neuronal nicotinic acetylcholine receptor subtypes present in bovine chromaffin cells and expressed in Xerl oocytes, respectively. Furthermore, its resistance to proteolysis against a specific protease and in human plasma was significantly improved. More generally, to our knowledge, this report is the first on the cyclization of disulfide-rich toxins. Cyclization strategies represent an approach for stabilizing bioactive peptides while keeping their full potencies and should boost applications of peptide-based drugs in human medicine.

Deriving matrix of peptide-MHC interactions in diabetic mouse by genetic algorithm

Finding motifs that can elucidate rules that govern peptide binding to medically important receptors is important for screening targets for drugs and vaccines. This paper focuses on elucidation of peptide binding to I-A(g7) molecule of the non-obese diabetic (NOD) mouse - an animal model for insulin-dependent diabetes mellitus (IDDM). A number of proposed motifs that describe peptide binding to I-A(g7) have been proposed. These motifs results from independent experimental studies carried out on small data sets. Testing with multiple data sets showed that each of the motifs at best describes only a subset of the solution space, and these motifs therefore lack generalization ability. This study focuses on seeking a motif with higher generalization ability so that it can predict binders in all A(g7) data sets with high accuracy. A binding score matrix representing peptide binding motif to A(g7) was derived using genetic algorithm (GA). The evolved score matrix significantly outperformed previously reported