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Bioinformatics Tool to Identify Peptides Associated to Cancer Cells

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We present a computational-mathematical algorithm that can identify peptides that are experimentally associated with their action against cancer cells and classified in the APD2 database. The algorithm, named polarity index method, showed an accuracy of 95% in a double-blind test applied to peptides from eight different databases. The method only requires the primary peptide structure, i.e. the amino acid sequence, to determine the polarity profile. Formerly, we have used this method to identify selective antibacterial peptides with a high efficiency. Our present study suggests that this computational method can also be used as a first filter in the analysis and identification of peptides and proteins that are related to cancer cells.
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Keywords: Bioinformatics methods; cancer cells; peptides; polarity index method; proteins

Document Type: Research Article

Publication date: 2015-11-01

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  • Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

    The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.

    Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.
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