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Methods Mol Biol. 2017;1647:245-254. doi: 10.1007/978-1-4939-7201-2_17.

In Silico Design of Anticancer Peptides.

Author information

1
Department of Pathology, School of Medicine, University of Virginia, 345 Crispell Dr., MR6-B524, Charlottesville, VA, 22908, USA.
2
Department of Pathology, School of Medicine, University of Virginia, 345 Crispell Dr., MR6-B524, Charlottesville, VA, 22908, USA. hl9r@virginia.edu.
3
Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA, 22908, USA. hl9r@virginia.edu.

Abstract

In the past few years, small peptides having anticancer properties have emerged as a potential avenue for cancer therapy. Compared to current anti-cancer chemotherapeutic drugs (or small molecules), anticancer peptides (ACPs) have numerous advantageous properties, such as high specificity, low production cost, high tumor penetration, ease of synthesis and modification. However, in wet lab setups, identification and characterization of novel ACPs is a time-consuming and labor-intensive process. Therefore, in silico designing of anticancer peptides is beneficial, prior to their synthesis and characterization. This approach is less time consuming and more cost-effective. In this chapter, we discuss a web-based tool, AntiCP (http://crdd.osdd.net/raghava/anticp/), for designing ACPs.

KEYWORDS:

Anti-cancer peptides; Machine learning; Support vector machine

PMID:
28809008
DOI:
10.1007/978-1-4939-7201-2_17
[Indexed for MEDLINE]

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