Monosaccharides modulate HCV E2 protein-derived peptide biological properties

Synthetic Peptides Derived from Bovine Lactoferricin Exhibit Antimicrobial Activity against E. coli ATCC 11775, S. maltophilia ATCC 13636 and S. enteritidis ATCC 13076

Linear, dimeric, tetrameric, and cyclic peptides derived from lactoferricin B–containing non-natural amino acids and the RWQWR motif were synthesized, purified, and characterized using RP-HPLC, MALDI-TOF mass spectrometry, and circular dichroism. The antibacterial activity of peptides against Escherichia coli ATCC 11775, Stenotrophomonas maltophilia ATCC 13636, and Salmonella enteritidis ATCC 13076 was evaluated. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were determined. The synthetic bovine lactoferricin exhibited antibacterial activity against E. coli ATCC 11775 and S. enteritidis ATCC 13076. The dimeric peptide (RRWQWR)₂K-Ahx exhibited the highest antibacterial activity against the tested bacterial strain. The monomeric, cyclic, tetrameric, and palindromic peptides containing the RWQWR motif exhibited high and specific activity against E. coli ATCC 11775. The results suggest that short peptides derived from lactoferricin B could be considered as potential candidates for the development of antibacterial agents against infections caused by E. coli.

On evaluating MHC-II binding peptide prediction methods

Choice of one method over another for MHC-II binding peptide prediction is typically based on published reports of their estimated performance on standard benchmark datasets. We show that several standard benchmark datasets of unique peptides used in such studies contain a substantial number of peptides that share a high degree of sequence identity with one or more other peptide sequences in the same dataset. Thus, in a standard cross-validation setup, the test set and the training set are likely to contain sequences that share a high degree of sequence identity with each other, leading to overly optimistic estimates of performance. Hence, to more rigorously assess the relative performance of different prediction methods, we explore the use of similarity-reduced datasets. We introduce three similarity-reduced MHC-II benchmark datasets derived from MHCPEP, MHCBN, and IEDB databases. The results of our comparison of the performance of three MHC-II binding peptide prediction methods estimated using datasets of unique peptides with that obtained using their similarity-reduced counterparts shows that the former can be rather optimistic relative to the performance of the same methods on similarity-reduced counterparts of the same datasets. Furthermore, our results demonstrate that conclusions regarding the superiority of one method over another drawn on the basis of performance estimates obtained using commonly used datasets of unique peptides are often contradicted by the observed performance of the methods on the similarity-reduced versions of the same datasets. These results underscore the importance of using similarity-reduced datasets in rigorously comparing the performance of alternative MHC-II peptide prediction methods.