Identification of antimicrobial peptides by using eigenvectors

• Authors: Carlos Polanco
• Languages of publication: EN

Abstracts

EN

Antibacterial peptides are subject to broad research due to their potential application and the benefit they can provide for a wide range of diseases. In this work, a mathematical-computational method, called the Polarity Vector Method, is introduced that has a high discriminative level (>70%) to identify peptides associated with Gram (-) bacteria, Gram (+) bacteria, cancer cells, fungi, insects, mammalian cells, parasites, and viruses, taken from the Antimicrobial Peptides Database. This supervised method uses only eigenvectors from the incident polar matrix of the group studied. It was verified with a comparative study with another extensively verified method developed previously by our team, the Polarity Index Method. The number of positive hits of both methods was up to 98% in all the tests conducted.

Keywords

EN

antimicrobial peptides; supervised methods; structural bioinformatics

• Journal: Acta Biochimica Polonica
• Year: 2016
• Volume: 63
• Issue: 3
• Pages: 483-491

Dates

published

2016

received

2015-02-13

revised

2016-02-10

accepted

2016-06-13

(unknown)

2016-06-23

Contributors

author

Carlos Polanco

• Department of Mathematics, Faculty of Sciences, Universidad Nacional Autónoma de México, México

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Identifiers

DOI

10.18388/abp.2015_993