A Web Server and Mobile App for Computing Hemolytic Potency of Peptides

Chaudhary, Kumardeep; Kumar, Ritesh; Singh, Sandeep; Tuknait, Abhishek; Gautam, Ankur; Mathur, Deepika; Anand, Paras; Varshney, Grish C.; Raghava, Gajendra P. S.

doi:10.1038/srep22843

Cited by 192 publications

(169 citation statements)

References 35 publications

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“…Peptides containing various therapeutic properties have been discovered67891011121314151617181920 and their number is increasing with time21. Owing to their applicability, a number of bioinformatics platforms have been developed to assist peptide therapeutics22232425262728. According to a recent report, 128 peptides are in the clinical pipeline.…”

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confidence: 99%

PEPlife: A Repository of the Half-life of Peptides

Mathur

Prakash

Anand

et al. 2016

Sci Rep

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124

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Short half-life is one of the key challenges in the field of therapeutic peptides. Various studies have reported enhancement in the stability of peptides using methods like chemical modifications, D-amino acid substitution, cyclization, replacement of labile aminos acids, etc. In order to study this scattered data, there is a pressing need for a repository dedicated to the half-life of peptides. To fill this lacuna, we have developed PEPlife (http://crdd.osdd.net/raghava/peplife), a manually curated resource of experimentally determined half-life of peptides. PEPlife contains 2229 entries covering 1193 unique peptides. Each entry provides detailed information of the peptide, like its name, sequence, half-life, modifications, the experimental assay for determining half-life, biological nature and activity of the peptide. We also maintain SMILES and structures of peptides. We have incorporated web-based modules to offer user-friendly data searching and browsing in the database. PEPlife integrates numerous tools to perform various types of analysis such as BLAST, Smith-Waterman algorithm, GGSEARCH, Jalview and MUSTANG. PEPlife would augment the understanding of different factors that affect the half-life of peptides like modifications, sequence, length, route of delivery of the peptide, etc. We anticipate that PEPlife will be useful for the researchers working in the area of peptide-based therapeutics.

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mentioning

confidence: 99%

PEPlife: A Repository of the Half-life of Peptides

Mathur

Prakash

Anand

et al. 2016

Sci Rep

Self Cite

124

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“…We employed these five training and test sets for performing five-fold cross-validation to select the best machine learning models as well as for developing BLAST similarity search-based module, as explained in the next sections. Five-fold cross-validation is a standard process that has been successfully implemented in several machine learningbased studies in the past (39,(46)(47)(48)(49)(50).…”

Section: Five-fold Cross Validationmentioning

confidence: 99%

A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information

2020

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This study describes a method developed for predicting pattern recognition receptors (PRRs), which are an integral part of the immune system. The models developed here were trained and evaluated on the largest possible non-redundant PRRs, obtained from PRRDB 2.0, and non-pattern recognition receptors (Non-PRRs), obtained from Swiss-Prot. Firstly, a similarity-based approach using BLAST was used to predict PRRs and got limited success due to a large number of no-hits. Secondly, machine learningbased models were developed using sequence composition and achieved a maximum MCC of 0.63. In addition to this, models were developed using evolutionary information in the form of PSSM composition and achieved maximum MCC value of 0.66. Finally, we developed hybrid models that combined a similarity-based approach using BLAST and machine learning-based models. Our best model, which combined BLAST and PSSM based model, achieved a maximum MCC value of 0.82 with an AUROC value of 0.95, utilizing the potential of both similarity-based search and machine learning techniques. In order to facilitate the scientific community, we also developed a web server "PRRpred" based on the best model developed in this study (http://webs.iiitd.edu.in/ raghava/prrpred/).

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“…We employed these five training and test sets for performing five-fold cross-validation to select the best machine learning models as well as for developing BLAST similarity search based module, as explained in the next sections. Five-fold cross-validation is a standard process that has been successfully implemented in several machine learning-based studies in the past (29,(36)(37)(38)(39)(40).…”

Section: Five -Fold Cross Validationmentioning

confidence: 99%

“…Different classifiers such as Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Extra Trees (ET), K-Nearest Neighbour (KNN) and Multi-Layer Perceptron (MLP) were used to develop prediction models. All these machine learning methods have been successfully applied in many bioinformatics studies (29,36,40,48,49).…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

A Hybrid Model for Predicting Pattern Recognition Receptors using Evolutionary Information

Kaur

Arora

Raghava

2019

Preprint

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This study describes a method developed for predicting pattern recognition receptors (PRRs), which are an integral part of the immune system. The models developed here were trained and evaluated on the largest possible non-redundant PRRs, and non-pattern recognition receptors (Non-PRRs) obtained from PRRDB 2.0. Firstly, a similarity-based approach using BLAST was used to predict PRRs and got limited success due to a large number of no-hits. Secondly, machine learning-based models were developed using sequence composition and achieved a maximum MCC of 0.63. In addition to this, models were developed using evolutionary information in the form of PSSM composition and achieved maximum MCC value of 0.66. Finally, we developed hybrid models that combined a similarity-based approach using BLAST and machine learningbased models. Our best model, which combined BLAST and PSSM based model, achieved a maximum MCC value of 0.82 with an AUROC value of 0.95, utilizing the potential of both similarity-based search and machine learning techniques. In order to facilitate the scientific community, we also developed a web server "PRRpred" based on the best model developed in this study (http://webs.iiitd.edu.in/raghava/prrpred/).

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A Web Server and Mobile App for Computing Hemolytic Potency of Peptides

Cited by 192 publications

References 35 publications

PEPlife: A Repository of the Half-life of Peptides

PEPlife: A Repository of the Half-life of Peptides

A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information

A Hybrid Model for Predicting Pattern Recognition Receptors using Evolutionary Information

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