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AntAngioCOOL: computational detection of anti-angiogenic peptides.

ABSTRACT: BACKGROUND:Angiogenesis inhibition research is a cutting edge area in angiogenesis-dependent disease therapy, especially in cancer therapy. Recently, studies on anti-angiogenic peptides have provided promising results in the field of cancer treatment. METHODS:A non-redundant dataset of 135 anti-angiogenic peptides (positive instances) and 135 non anti-angiogenic peptides (negative instances) was used in this study. Also, 20% of each class were selected to construct an independent test dataset (see Additional files 1, 2). We proposed an effective machine learning based R package (AntAngioCOOL) to predict anti-angiogenic peptides. We have examined more than 200 different classifiers to build an efficient predictor. Also, more than 17,000 features were extracted to encode the peptides. RESULTS:Finally, more than 2000 informative features were selected to train the classifiers for detecting anti-angiogenic peptides. AntAngioCOOL includes three different models that can be selected by the user for different purposes; it is the most sensitive, most specific and most accurate. According to the obtained results AntAngioCOOL can effectively suggest anti-angiogenic peptides; this tool achieved sensitivity of 88%, specificity of 77% and accuracy of 75% on the independent test set. AntAngioCOOL can be accessed at https://cran.r-project.org/ . CONCLUSIONS:Only 2% of the extracted descriptors were used to build the predictor models. The results revealed that physico-chemical profile is the most important feature type in predicting anti-angiogenic peptides. Also, atomic profile and PseAAC are the other important features.

SUBMITTER: Zahiri J

PROVIDER: S-EPMC6399940 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Abnormal migration and proliferation of endothelial cells (EC) drive neovascular retinopathies. While anti-VEGF treatment slows progression, pathology is often supported by decrease in intraocular pigment epithelium-derived factor (PEDF), an endogenous inhibitor of angiogenesis. A surface helical 34-mer peptide of PEDF, comprising this activity, is efficacious in animal models of neovascular retina disease but remains impractically large for therapeutic use. We sought smaller fragments within this sequence that mitigate choroidal neovascularization (CNV). Expecting rapid intravitreal (IVT) clearance, we also developed a method to reversibly attach peptides to nano-carriers for extended delivery. Synthetic fragments of 34-mer yielded smaller anti-angiogenic peptides, and N-terminal capping with dicarboxylic acids did not diminish activity. Charge restoration via substitution of an internal aspartate by asparagine improved potency, achieving low nM apoptotic response in VEGF-activated EC. Two optimized peptides (PEDF 335, 8-mer and PEDF 336, 9-mer) were tested in a mouse model of laser-induced CNV. IVT injection of either peptide, 2-5 days before laser treatment, gave significant CNV decrease at day +14 post laser treatment. The 8-mer also decreased CNV, when administered as eye drops. Also examined was a nanoparticle-conjugate (NPC) prodrug of the 9-mer, having positive zeta potential, expected to display longer intraocular residence. This NPC showed extended efficacy, even when injected 14 days before laser treatment. Neither inflammatory cells nor other histopathologic abnormalities were seen in rabbit eyes harvested 14 days following IVT injection of PEDF 336 (>200 ?g). No rabbit or mouse eye irritation was observed over 12-17 days of PEDF 335 eye drops (10 mM). Viability was unaffected in 3 retinal and 2 choroidal cell types by PEDF 335 up to 100 ?M, PEDF 336 (100 ?M) gave slight growth inhibition only in choroidal EC. A small anti-angiogenic PEDF epitope (G-Y-D-L-Y-R-V) was identified, variants (adipic-Sar-Y-N-L-Y-R-V) mitigate CNV, with clinical potential in treating neovascular retinopathy. Their shared active motif, Y - - - R, is found in laminin (Ln) peptide YIGSR, which binds Ln receptor 67LR, a known high-affinity ligand of PEDF 34-mer.

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AntAngioCOOL: computational detection of anti-angiogenic peptides.

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