ChAlPred: A web server for prediction of allergenicity of chemical compounds

Sharma, Neelam; Patiyal, Sumeet; Dhall, Anjali; Naorem, Leimarembi Devi; Raghava, Gajendra P. S.

doi:10.1016/j.compbiomed.2021.104746

Cited by 30 publications

(11 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The parameters considered to evaluate model performance are threshold dependent and threshold-independent. Threshold dependent parameters include: Sensitivity, Specificity, Accuracy, and Matthew’s correlation coefficient (MCC), whereas Area Under the Receiver Operating Characteristic (AUROC) is considered a threshold-independent parameter These performance evaluation criteria have been widely applied to evaluate the model’s effectiveness and they are well-defined in the literature [31, 38, 41, 46]. The measurements are defined as follows: …”

Section: Methodsmentioning

confidence: 99%

A method for predicting linear and conformational B-cell epitopes in an antigen from its primary sequence

Kumar

Tripathi

Sharma

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

B-cell is an essential component of the immune system that plays a vital role in providing the immune response against any pathogenic infection by producing antibodies. Existing methods either predict linear or conformational B-cell epitopes in an antigen. In this study, a single method was developed for predicting both types (linear/conformational) of B-cell epitopes. The dataset used in this study contains 3875 B-cell epitopes and 3996 non-B-cell epitopes, where B-cell epitopes consist of both linear and conformational B-cell epitopes. Our primary analysis indicates that certain residues (like Asp, Glu, Lys, Asn) are more prominent in B-cell epitopes. We developed machine-learning based methods using different types of sequence composition and achieved the highest AUC of 0.80 using dipeptide composition. In addition, models were developed on selected features, but no further improvement was observed. Our similarity-based method implemented using BLAST shows a high probability of correct prediction with poor sensitivity. Finally, we came up with a hybrid model that combine alignment free (dipeptide based random forest model) and alignment-based (BLAST based similarity) model. Our hybrid model attained maximum AUC 0.83 with MCC 0.49 on the independent dataset. Our hybrid model performs better than existing methods on an independent dataset used in this study. All models trained and tested on 80% data using cross-validation technique and final model was evaluated on 20% data called independent or validation dataset. A webserver and standalone package named "CLBTope" has been developed for predicting, designing, and scanning B-cell epitopes in an antigen sequence (https://webs.iiitd.edu.in/raghava/clbtope/).

show abstract

Section: Methodsmentioning

confidence: 99%

A method for predicting linear and conformational B-cell epitopes in an antigen from its primary sequence

Kumar

Tripathi

Sharma

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Acc (equation 5) denotes the percentage of the correctly predicted IL-5 inducers and non-inducers, and MCC (equation 6) is the relation between the predicted and actual values. These performance metrics are commonly used and well-annotated in the previous studies [55,56] and can be calculated as: …”

Section: Methodsmentioning

confidence: 99%

“…These performance metrics are commonly used and well-annotated in the previous studies [55,56] and can be calculated as:…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Prediction and scanning of IL-5 inducing peptides using alignment-free and alignment-based method

Naorem

Sharma

Raghava

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Interleukin-5 (IL-5) is the key cytokine produced by T-helper, eosinophils, mast and basophils cells. It can act as an enticing therapeutic target due to its pivotal role in several eosinophil-mediated diseases. Though numerous methods have been developed to predict HLA binders and cytokines-inducing peptides, no method was developed for predicting IL-5 inducing peptides. All models in this study have been trained, tested and validated on experimentally validated 1907 IL-5 inducing and 7759 non-IL-5 inducing peptides obtained from IEDB. First, alignment-based methods have been developed using similarity and motif search. These alignment-based methods provide high precision but poor coverage. In order to overcome this limitation, we developed machine learning-based models for predicting IL-5 inducing peptides using a wide range of peptide features. Our random-forest model developed using selected 250 dipeptides achieved the highest performance among alignment-free methods with AUC 0.75 and MCC 0.29 on validation dataset. In order to improve the performance, we developed an ensemble or hybrid method that combined alignment-based and alignment-free methods. Our hybrid method achieved AUC 0.94 with MCC 0.60 on validation/ independent dataset. The best model developed in this study has been incorporated in the web server IL5pred (https://webs.iiitd.edu.in/raghava/il5pred/).Key PointsIL-5 is a regulatory cytokine that plays a vital role in eosinophil-mediated diseasesBLAST-based similarity search against IL-5 inducing peptides was employedA hybrid approach combines alignment-based and alignment-free methodsAlignment-free models are based on machine learning techniquesA web server ‘IL5pred’ and its standalone software have been developedAuthors’ BiographyDr. Naorem Leimarembi Devi is currently working as a DBT-Research Associate in Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Neelam Sharma is pursuing her Ph.D. in Computational Biology from the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Prof. G.P.S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.

show abstract

“…Naïve Bayes is one of the most popular statistical approaches for solving classification problems and has been successfully used to classify biological sequences. − Naïve Bayes assumes that the probability distributions of variables are independent of each other. Hence, the calculation of conditional probabilities can be simplified significantly.…”

Section: Computational Frameworkmentioning

confidence: 99%

Sequence-Based Prediction of Plant Allergenic Proteins: Machine Learning Classification Approach

et al. 2023

View full text Add to dashboard Cite

This Article proposes a novel chemometric approach to understanding and exploring the allergenic nature of food proteins. Using machine learning methods (supervised and unsupervised), this work aims to predict the allergenicity of plant proteins. The strategy is based on scoring descriptors and testing their classification performance. Partitioning was based on support vector machines (SVM), and a k-nearest neighbor (KNN) classifier was applied. A fivefold cross-validation approach was used to validate the KNN classifier in the variable selection step as well as the final classifier. To overcome the problem of food allergies, a robust and efficient method for protein classification is needed.

show abstract

ChAlPred: A web server for prediction of allergenicity of chemical compounds

Cited by 30 publications

References 62 publications

A method for predicting linear and conformational B-cell epitopes in an antigen from its primary sequence

A method for predicting linear and conformational B-cell epitopes in an antigen from its primary sequence

Prediction and scanning of IL-5 inducing peptides using alignment-free and alignment-based method

Sequence-Based Prediction of Plant Allergenic Proteins: Machine Learning Classification Approach

Contact Info

Product

Resources

About