Homology Similarity Analysis of Sequences of Lactoferricin and Its Derivatives

Abstract

A new method, homology similarity analysis (HSA), was developed to investigate homology pattern similarities of selected segments within sequences of peptides. This new approach facilitated elucidation of the structure-function relationships of lactoferricin derivatives. Helix propensity of positions 4-9 in the lactoferricin sequence was the most important in determining the antimicrobial activity of lactoferricin against Escherichia coli, followed by cationic charge pattern at positions 4-9 and 1-3. The pattern similarity of segments within sequences could be a useful tool for representing the distribution attributes of amino acid residue properties to the structure-function relationships of proteins and peptides, especially when used in conjunction with principal component similarity analysis followed by the regression version of artificial neural networks.

Novel antibacterial lactoferrin peptides generated by rennet digestion and autofocusing technique

“…In Table 2, the sequence and MS analyses revealed that pepsin LFH contained the 25-residues-(peak B) and 11-residues Lf-cin B (peak C) fragments, which both had the same antibacterial potency. These results are in agreement with the previous studies carried on porcine pepsin LFH by Nakai et al (2003). Rennet LFH contained also the same 11-residues Lf-cin B (peak F).…”

“…Depending on digestion condition, antimicrobial peptides with different lengths varying from 11 to 47 and from 6 to 25 amino acid residues are obtained from human and bovine LF, respectively. These antimicrobial peptides are known as lactoferricin H (human; Lf-cin H) or lactoferricin B (bovine; Lf-cin B) (Chapple et al, 1998a(Chapple et al, ,1998bNakai, Chan, Li-Chan, Dou, & Ogawa, 2003). The antimicrobial effect of Lf-cin has been recognized to be through the destabilization of the plasma membrane of Gram-positive and Gram-negative bacteria, resulting in disrupting membrane permeability (Ulvatne, Haukland, Olsvik, & Vorland, 2001;Vorland, Ulvatne, Rekdal, & Svendsen, 1999).…”

Mass spectrometry for nutritional peptidomics: How to analyze food bioactives and their health effects

“…Peptide QSAR modeling was particular efficient in prediction of antimicrobial, ACEinhibitory and bitter tasting peptide activity [88][89][90]. Using physicochemical descriptors on a dataset of peptides derived from milk proteins, a QSAR model was developed indicating that side chain hydrophobicity in the C-terminal position and side chain size in the penultimate amino acid were critical for the ACE-inhibitory potential [89].…”

Predicting and Annotating Catalytic Residues: An Information Theoretic Approach

“…Furthermore, our method also predicts the correct annotation for all of these; Nakai et al's method does not predict annotations. The method of Nakai et al (2003Nakai et al ( , 2004Nakai et al ( , 2005 falls in a class of QSAR methods that have been successful at characterizing each residue of a protein by various features (e.g.,ˇ-turn propensity, hydrophobicity) and then identifying, in a training set of proteins, the most important features for a particular property (e.g., catalysis). In any query protein, residues are then evaluated on these features to identify the most likely catalytic residues.…”