Connecting Peptide Physicochemical and Antimicrobial Properties by a Rational Prediction Model

Abstract

The increasing rate in antibiotic-resistant bacterial strains has become an imperative health issue. Thus, pharmaceutical industries have focussed their efforts to find new potent, non-toxic compounds to treat bacterial infections. Antimicrobial peptides (AMPs) are promising candidates in the fight against antibiotic-resistant pathogens due to their low toxicity, broad range of activity and unspecific mechanism of action. In this context, bioinformatics' strategies can inspire the design of new peptide leads with enhanced activity. Here, we describe an artificial neural network approach, based on the AMP's physicochemical characteristics, that is able not only to identify active peptides but also to assess its antimicrobial potency. The physicochemical properties considered are directly derived from the peptide sequence and comprise a complete set of parameters that accurately describe AMPs. Most interesting, the results obtained dovetail with a model for the AMP's mechanism of action that takes into account new concepts such as peptide aggregation. Moreover, this classification system displays high accuracy and is well correlated with the experimentally reported data. All together, these results suggest that the physicochemical properties of AMPs determine its action. In addition, we conclude that sequence derived parameters are enough to characterize antimicrobial peptides.

Introduction of a Lysine Residue Promotes Aggregation of Temporin L in Lipopolysaccharides and Augmentation of Its Antiendotoxin Property

“…In vitro aggregation and secondary-structure prediction were performed by use of TANGO software (20). The peptide's net hydrophobic mean character was calculated by the GRAVY (grand average of hydropathy) scale (21).…”

Prediction and Rational Design of Antimicrobial Peptides

“…Also using physicochemical properties, Torrent et al (2011) developed an ANN with eight properties: isoelectric point (pI), peptide length, -helix, -sheet and turn structure propensity, in vivo and in vitro aggregation propensity and hydrophobicity. The main data set was composed of 1157 AMPs from CAMP and 991 non-AMPs from SwissProt.…”

“…In fact, these studies played a critical role in identifying the AMP properties involved in antimicrobial activity. These properties served as the basis for developing approaches for antimicrobial activity prediction, through several methods, such as support vector machine (SVM, Lata et al, 2007;Porto et al, 2010;Thomas et al, 2010), artificial neural network (ANN, Fjell et al, 2009;Torrent et al, 2011) and quantitative structure-activity relationship (QSAR, Jenssen et al, 2007) as will be further detailed. By using machine learning methods, this field became more scientific than descriptive.…”

Use of Peptide Libraries for Identification and Optimization of Novel Antimicrobial Peptides

“…Such descriptors are amino acid composition, aggregated descriptors of amino acids (e.g., hydrophobicity and isoelectric point) or descriptors for physico-chemical properties of complete sequences (e.g., alpha-helix propensity [98], [99]. These descriptors are aggregated by Bayes classifiers [100], Neuro-Fuzzy models [99], Artificial Neural Networks [87], [98], [101] or Support Vector Machines [98], [101] to predict activity or toxicity for unknown peptide sequences. More powerful models can be designed if activity measurements against various microbes and toxicity values are available.…”