Making plants into cost-effective bioreactors for highly active antimicrobial peptides.

As antibiotic-resistant bacterial pathogens become an ever-increasing concern, antimicrobial peptides (AMPs) have grown increasingly attractive as alternatives. Potentially, plants could be used as cost-effective AMP bioreactors; however, reported heterologous AMP expression is much lower in plants than in E. coli expression systems and often results in plant cytotoxicity, even for AMPs fused to carrier proteins.

Repurposing a drug targeting peptide for targeting antimicrobial peptides against Staphylococcus.

Objectives: Targeted therapies seek to selectively eliminate a pathogen without disrupting the resident microbial community. However, with selectivity comes the potential for developing bacterial resistance. Thus, a diverse range of targeting peptides must be made available.

Classes, Databases, and Prediction Methods of Pharmaceutically and Commercially Important Cystine-Stabilized Peptides.

Cystine-stabilized peptides represent a large family of peptides characterized by high structural stability and bactericidal, fungicidal, or insecticidal properties. Found throughout a wide range of taxa, this broad and functionally important family can be subclassified into distinct groups dependent upon their number and type of cystine bonding patters, tertiary structures, and/or their species of origin. Furthermore, the annotation of proteins related to the cystine-stabilized family are under-represented in the literature due to their difficulty of isolation and identification.

Assigning biological function using hidden signatures in cystine-stabilized peptide sequences.

Cystine-stabilized peptides have great utility as they naturally block ion channels, inhibit acetylcholine receptors, or inactivate microbes. However, only a tiny fraction of these peptides has been characterized. Exploration for novel peptides most efficiently starts with the identification of candidates from genome sequence data.

Protein classification using modified n-grams and skip-grams.

Motivation: Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model.

PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides.

Background: Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability.