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Bioinformatics-Based Identification of Selective Cationic Amphipatic Antibacterial Peptides: Aurein 1.2 Variants Case

[ Vol. 18 , Issue. 4 ]


Carlos Polanco*, Alberto Huberman, Vladimir N. Uversky, Leire Andrés, Thomas Buhse, Jorge Alberto Castañón-González and Juan Luciano Dı́az-González   Pages 505 - 518 ( 14 )


Background: Selective Cationic Amphipathic Antibacterial Peptides (SCAAPs) occupy a prominent place in the production of new drugs on account of their high toxicity towards bacteria and low toxicity towards mammalian cells, low hemolytic activity, and contribution to the protection of the human immune system.

Introduction: their number in nature is very low, and experimental tests are very protracted and costly. Therefore, it would be useful to have bioinformatics tools that would identify them in the existing databases and also propose new synthetic SCAAPs.

Methods: In order to reduce the costs of identification and/or chemical synthesis and to know the physicochemical characteristics of SCAAPs at a residues level and to obtain a “bioiformatics fingerprint” suitable for their selection, we have modified the Polarity Index Method® (PIM®) and the α-helical configuration of each sequence is included in determining their individual “PIM® profile”. We have also used a set of the computer program to determine their “Intrinsic Disorder Predisposition”. This information was then compared with other protein groups, such as bacteria, fungi, virus and cell-penetrating peptides (CPP) from the UniProt database and a set of intrinsically disordered proteins. Once the “fingerprint” of SCAAPs was obtained, it was used for searching among the 559228 “reviewed” proteins from the UniProt database and a set of synthetic SCAAPs characterized by the predefined “PIM® profile” selected.

Results: Our results showed that the metric named “PIM® profile” can identify, with a high level of accuracy, a group of bacterial SCAAPs. This bioinformatics study was supported at residues level, using the in-house bioinformatics system Polarity Index Method, the commonly used algorithm for predicting intrinsic disorder predisposition, PONDR® FIT.

Conclusions: The Polarity Index Method seems highly efficient in identifying SCAAP candidates.


Selective cationic amphipathic antibacterial peptides, antimicrobial peptides, structural proteomics, bioinformatics, intrinsic disorder predisposition, PIM® profile.


Department of Mathematics, Faculty of Sciences, Universidad Nacional Autónoma de México, México City 04510, Department of Biochemistry, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, C.P. 14080 México City, Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33647, Department of Pathology, Hospital de Cruces, 48903, Barakaldo, Chemical Research Center, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Department of Critical Care Medicine, Hospital Juárez de México, México City 07760, Department of Computer Sciences, Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, México City 04510

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