AntiAngioPred: A Server for Prediction of Anti-Angiogenic Peptides

Ramaprasad, Azhagiya Singam Ettayapuram; Singh, Sandeep; Raghava, Gajendra P. S.; Venkatesan, S.

doi:10.1371/journal.pone.0136990

Cited by 62 publications

(67 citation statements)

References 33 publications

(27 reference statements)

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“…In case of ‘wget’, we downloaded information of peptides in ‘HTML’ format, which were processed using in-house PERL scripts to extract desired information. In addition, we also obtained peptides from two peptide datasets containing antiangiogenic and toxic peptides that were used in the development of prediction methods ‘AntiAngioPred’ ( 12 ) and ‘Toxinpred’ ( 10 ) respectively. We removed all the peptides having undefined amino acid in their sequence (for example sequence having amino acid X and meaning of X is not defined).…”

Section: Methodsmentioning

confidence: 99%

“…In addition, a significant progress has been made over the years in the field of computational peptidology ( 9 ). Several computational methods have been developed for predicting pharmacologically important properties of peptides like toxicity ( 10 ), half-life ( 11 ), antiangiogenic ( 12 ), antimicrobial ( 13 – 15 ) immunogenic peptides ( 16 ). These in silico prediction tools not only help in designing peptide analogs with improved physicochemical properties but also help in screening peptide libraries for the desired therapeutic property.…”

Section: Introductionmentioning

confidence: 99%

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SATPdb: a database of structurally annotated therapeutic peptides

et al. 2015

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SATPdb (http://crdd.osdd.net/raghava/satpdb/) is a database of structurally annotated therapeutic peptides, curated from 22 public domain peptide databases/datasets including 9 of our own. The current version holds 19192 unique experimentally validated therapeutic peptide sequences having length between 2 and 50 amino acids. It covers peptides having natural, non-natural and modified residues. These peptides were systematically grouped into 10 categories based on their major function or therapeutic property like 1099 anticancer, 10585 antimicrobial, 1642 drug delivery and 1698 antihypertensive peptides. We assigned or annotated structure of these therapeutic peptides using structural databases (Protein Data Bank) and state-of-the-art structure prediction methods like I-TASSER, HHsearch and PEPstrMOD. In addition, SATPdb facilitates users in performing various tasks that include: (i) structure and sequence similarity search, (ii) peptide browsing based on their function and properties, (iii) identification of moonlighting peptides and (iv) searching of peptides having desired structure and therapeutic activities. We hope this database will be useful for researchers working in the field of peptide-based therapeutics.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SATPdb: a database of structurally annotated therapeutic peptides

et al. 2015

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“…We have used the gold standard dataset that has been recently published 8 . After removing redundant peptides, this dataset contains 135 anti-angiogenic peptides (positive instances) and 135 non anti-angiogenic peptides (negative instances).…”

Section: Datasetmentioning

confidence: 99%

AntAngioCOOL: An R Package for Computational Detection of Anti-Angiogenic Peptides

Zahiri

Khorsand-Ghaffari

Zade

et al. 2017

Preprint

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Angiogenesis inhibition research is a cutting edge in angiogenesis-dependent disease therapy, and especially in cancer therapy. Recently, studies on anti-angiogenic peptides have provided promising results in the cancer treatment field. In the current study we propose 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 17000 features have been extracted to encode the peptides. However, finally, more than 2000 informative features have been selected to train the classifiers. According to the obtained results AntAngioCOOL can effectively predict anti-angiogenic peptides: this tool achieved sensitivity of 88%, specificity of 77% and accuracy of 75% on independent test set. AntAngioCOOL can be accessed at https://cran.r-project.org/.

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“…Ettayapuram Ramaprasad AS et al . 24 developed a support vector machine (SVM)-based predictor to identify anti-angiogenic peptides, using various features extracted from peptide sequences including Binary Profile Patterns (BPP), Amino Acid Composition (AAC), and Dipeptide Compositions (DPC). The accuracy and Matthew’s Correlation Coefficient (MCC) of the method are 0.748 and 0.500, respectively.…”

Section: Introductionmentioning

confidence: 99%

Using a Classifier Fusion Strategy to Identify Anti-angiogenic Peptides

Zhang

2018

Sci Rep

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Anti-angiogenic peptides perform distinct physiological functions and potential therapies for angiogenesis-related diseases. Accurate identification of anti-angiogenic peptides may provide significant clues to understand the essential angiogenic homeostasis within tissues and develop antineoplastic therapies. In this study, an ensemble predictor is proposed for anti-angiogenic peptide prediction by fusing an individual classifier with the best sensitivity and another individual one with the best specificity. We investigate predictive capabilities of various feature spaces with respect to the corresponding optimal individual classifiers and ensemble classifiers. The accuracy and Matthew’s Correlation Coefficient (MCC) of the ensemble classifier trained by Bi-profile Bayes (BpB) features are 0.822 and 0.649, respectively, which represents the highest prediction results among the investigated prediction models. Discriminative features are obtained from BpB using the Relief algorithm followed by the Incremental Feature Selection (IFS) method. The sensitivity, specificity, accuracy, and MCC of the ensemble classifier trained by the discriminative features reach up to 0.776, 0.888, 0.832, and 0.668, respectively. Experimental results indicate that the proposed method is far superior to the previous study for anti-angiogenic peptide prediction.

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AntiAngioPred: A Server for Prediction of Anti-Angiogenic Peptides

Cited by 62 publications

References 33 publications

SATPdb: a database of structurally annotated therapeutic peptides

SATPdb: a database of structurally annotated therapeutic peptides

AntAngioCOOL: An R Package for Computational Detection of Anti-Angiogenic Peptides

Using a Classifier Fusion Strategy to Identify Anti-angiogenic Peptides

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