IL13Pred: A method for predicting immunoregulatory cytokine IL-13 inducing peptides

Jain, Shreyansh; Dhall, Anjali; Patiyal, Sumeet; Raghava, Gajendra P. S.

doi:10.1016/j.compbiomed.2022.105297

Cited by 24 publications

(41 citation statements)

References 33 publications

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“…In this study, we have used standard parameters for the evaluation of prediction models. Here, we have calculated both threshold dependent as well as independent parameters as previously used in various studies [25]. In the case of threshold-dependent parameters we have computed, sensitivity (Sens), specificity (Spec), accuracy (Acc) and Matthews correlation coefficient (MCC) using the following equations (1-4).…”

Section: Methodsmentioning

confidence: 99%

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Prediction of celiac disease associated epitopes and motifs in a protein

Tomer

Patiyal

Dhall

et al. 2022

Preprint

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Celiac disease (CD) is an autoimmune gastrointestinal disorder which causes immune-mediated enteropathy against gluten. The gluten immunogenic peptides have the potential to trigger immune responses which leads to damage the small intestine. HLA-DQ2 and HLA-DQ8 are major alleles that bind to epitope/antigenic region of gluten and induce celiac disease. There is a need to identify CD associated epitopes in protein-based foods and therapeutics. In addition, prediction of CD associated epitope/peptide is also required for developing antigen-based immunotherapy against celiac disease. In this study, computational tools have been developed to predict CD associated epitopes and motifs. Dataset used in this study for training, testing and evaluation contain experimentally validated CD associated and non-CD associate peptides. Our analysis support existing hypothesis that proline (P) and glutamine (Q) are highly abundant in CD associated peptides. A model based on density of P&Q in peptides has been developed for predicting CD associated which achieve maximum AUROC 0.98. We discovered CD associated motifs (e.g., QPF, QPQ, PYP) which occurs specifically in CD associated peptides. We also developed machine learning based models using peptide composition and achieved maximum AUROC 0.99. Finally, we developed ensemble method that combines motif-based approach and machine learning based models. The ensemble model-predict CD associated motifs with 100% accuracy on an independent dataset, not used for training. Finally, the best models and motifs has been integrated in a web server and standalone software package CDpred. We hope this server anticipate the scientific community for the prediction, designing and scanning of CD associated peptides as well as CD associated motifs in a protein/peptide sequence (https://webs.iiitd.edu.in/raghava/cdpred/ ).

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Section: Methodsmentioning

confidence: 99%

“…In order to avoid overfitting we have train, test and validate the machine learning models by employing five-fold cross validation technique as implemented in previous studied [4,[24][25][26][27].…”

Section: Five-fold Cross Validationmentioning

confidence: 99%

Prediction of celiac disease associated epitopes and motifs in a protein

Tomer

Patiyal

Dhall

et al. 2022

Preprint

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“…It is commonly used to identify the most relevant features. Pfeature generates a large pool of features and [31] mostly redundant and irrelevant features exist in the original feature set, which can create over-fitting [29]. The selection of the best features can minimize the risk of over-fitting and increase efficiency by reducing the model’s complexity [29, 32].…”

Section: Methodsmentioning

confidence: 99%

“…Receiver Operating Characteristic (AUROC) curve are threshold-independent parameter [26,27,31]. These measurements are calculated using the following equations (2)(3)(4)(5)(6).…”

Section: Evaluation Parametersmentioning

confidence: 99%

“…threshold dependent parameters and threshold-independent parameters. Sensitivity, Specificity, Accuracy, and Matthew's correlation coefficient (MCC) are threshold dependent parameters while Area Under the Receiver Operating Characteristic (AUROC) curve are threshold-independent parameter [26,27,31]. These measurements are calculated using the following equations (2)(3)(4)(5)(6).…”

Section: Evaluation Parametersmentioning

confidence: 99%

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Identification of Antigenic Regions Responsible for inducing Type 1 diabetes mellitus

Kumar

Patiyal

Choudhury

et al. 2022

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There are a number of antigens that induce autoimmune response against beta-cells, leading to Type 1 diabetes mellitus (T1DM). Recently several antigen-specific immunotherapies have been developed to treat T1DM. Thus identification of T1DM associated peptides with antigenic regions or epitopes is important for peptide based-therapeutics (e.g., immunotherapeutic). In this study, for the first time an attempt has been made to develop a method for predicting, designing and scanning of T1DM associated peptides with high precision. We analyzed 815 T1DM associated peptides and observed that these peptides are not associated with a specific class of HLA alleles. Thus, HLA binder prediction methods are not suitable for predicting T1DM associated peptides. Firstly, we developed a similarity/alignment based method using BLAST and achieved a high probability of correct hits with poor coverage. Secondly, we developed an alignment free method using machine learning techniques and got maximum AUROC 0.89 using dipeptide composition. Finally, we developed a hybrid method that combines the strength of both alignment free and alignment based methods and achieve maximum AUROC 0.95 with MCC 0.81 on independent dataset. We developed a webserver DMPPred and standalone server, for predicting, designing and scanning of T1DM associated peptides (https://webs.iiitd.edu.in/raghava/dmppred/).

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A random forest model for predicting exosomal proteins using evolutionary information and motifs

et al. 2023

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Non‐invasive diagnostics and therapies are crucial to prevent patients from undergoing painful procedures. Exosomal proteins can serve as important biomarkers for such advancements. In this study, we attempted to build a model to predict exosomal proteins. All models are trained, tested, and evaluated on a non‐redundant dataset comprising 2831 exosomal and 2831 non‐exosomal proteins, where no two proteins have more than 40% similarity. Initially, the standard similarity‐based method Basic Local Alignment Search Tool (BLAST) was used to predict exosomal proteins, which failed due to low‐level similarity in the dataset. To overcome this challenge, machine learning (ML) based models were developed using compositional and evolutionary features of proteins achieving an area under the receiver operating characteristics (AUROC) of 0.73. Our analysis also indicated that exosomal proteins have a variety of sequence‐based motifs which can be used to predict exosomal proteins. Hence, we developed a hybrid method combining motif‐based and ML‐based approaches for predicting exosomal proteins, achieving a maximum AUROC of 0.85 and MCC of 0.56 on an independent dataset. This hybrid model performs better than presently available methods when assessed on an independent dataset. A web server and a standalone software ExoProPred (https://webs.iiitd.edu.in/raghava/exopropred/) have been created to help scientists predict and discover exosomal proteins and find functional motifs present in them.

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IL13Pred: A method for predicting immunoregulatory cytokine IL-13 inducing peptides

Cited by 24 publications

References 33 publications

Prediction of celiac disease associated epitopes and motifs in a protein

Prediction of celiac disease associated epitopes and motifs in a protein

Identification of Antigenic Regions Responsible for inducing Type 1 diabetes mellitus

A random forest model for predicting exosomal proteins using evolutionary information and motifs

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