Note: AntiCP 2.0 an updated model for predicting anticancer (ACP) peptides_2020

Note: AntiCP 2.0 an updated model for predicting anticancer (ACP) peptides_2020

doi: 10.1093/bib/bbaa153

In silico model development;

• predicting and designing anticancer peptides

• Preference

• Amino acids

• A, F, K, L and W

• Position

• A, F and K favour at N-terminal

• L and K favour at C-terminal

• Motif

• LAKLA

• AKLAK

• FAKL

• LAKL

• Machine model (tree-based model)

Web server: https://webs.iiitd.edu.in/raghava/anticp2/

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Content

What they claim why peptides are good for treating cancer ..

• High target specificity

• Good efficacy

• Easily to synthesized

• Low toxicity

• Easily to be chemically modified

• Less immunogenic compared to recombinant antibodies

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• >60 drugs have been approved by FDA

• >500 under clinical trials

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ACP is part of the antimicrobial peptide

• 5-50 AA

• Cationic

• Mostly, alpha-helix

• Some, beta-sheet

• Some, linear

Cancer cell mb

• Larger surface area (microvilli by nature)

• Negatively charged

• Higher fluidity 

Dataset to generate model

• ACP-DL, ACPP, ACPred-FL, AntiCP, iACP peptide, CancerPPD

Workflow of ACP

Various mechanism of ACP

Limitation

• No structural properties have been determined

• Secondary structure features

• Surface accessibility value

• Disulfide bond formation

• Lack of post-translational modification (ex, terminal-modification, glycosylation, phosphorylation)