NAGbinder: An approach for identifying N‐acetylglucosamine interacting residues of a protein from its primary sequence

Patiyal, Sumeet; Agrawal, Piyush; Kumar, Vinod; Dhall, Anjali; Kumar, Rajesh; Mishra, Gaurav; Raghava, Gajendra P. S.

doi:10.1002/pro.3761

Cited by 32 publications

(24 citation statements)

References 41 publications

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“…In order to avoid the over-optimization in the training of models, we used standard 5-fold cross-validation (Patiyal et al, 2019). In brief, all instances are randomly divided into five sets; where, four sets are used for the training and the remaining fifth set for testing.…”

Section: Five-fold Cross-validationmentioning

confidence: 99%

Computing Skin Cutaneous Melanoma Outcome From the HLA-Alleles and Clinical Characteristics

et al. 2020

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Human leukocyte antigen (HLA) are essential components of the immune system that stimulate immune cells to provide protection and defense against cancer. Thousands of HLA alleles have been reported in the literature, but only a specific set of HLA alleles are present in an individual. The capability of the immune system to recognize cancer-associated mutations depends on the presence of a particular set of alleles, which elicit an immune response to fight against cancer. Therefore, the occurrence of specific HLA alleles affects the survival outcome of cancer patients. In the current study, prediction models were developed, using 401 cutaneous melanoma patients, to predict the overall survival (OS) of patients using their clinical data and HLA alleles. We observed that the presence of certain favorable superalleles like HLA-B * 55 (HR = 0.15, 95% CI 0.034-0.67), HLA-A * 01 (HR = 0.5, 95% CI 0.3-0.8), is responsible for the improved OS. In contrast, the presence of certain unfavorable superalleles such as HLA-B * 50 (HR = 2.76, 95% CI 1.284-5.941), HLA-DRB1 * 12 (HR = 3.44, 95% CI 1.64-7.2) is responsible for the poor survival. We developed prediction models using key 14 HLA superalleles, demographic, and clinical characteristics for predicting high-risk cutaneous melanoma patients and achieved HR = 4.52 (95% CI 3.088-6.609, p-value = 8.01E-15). Eventually, we also provide a web-based service to the community for predicting the risk status in cutaneous melanoma patients (https://webs.iiitd.edu.in/raghava/skcmhrp/).

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Section: Five-fold Cross-validationmentioning

confidence: 99%

Computing Skin Cutaneous Melanoma Outcome From the HLA-Alleles and Clinical Characteristics

et al. 2020

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“…were referred as favourable superalleles like HLA-B*55 (HR=0.15, 95% CI 0.034 to 0.67), HLA-33 A*01 (HR=0.5, 95% CI 0.3 to 0.8). In contrast, presence of certain superalleles in the patients is 34 responsible for their poor survival, those superalleles were referred as unfavourable superalleles such 35…”

Section: Abstract 23mentioning

confidence: 99%

“…In order to avoid the over optimisation in the training of models, we used standard five-fold cross-170 validation (34). In brief, all instances are randomly divided into five sets; where, four sets are used 171…”

Section: Five-fold Cross-validation 169mentioning

confidence: 99%

Computing Skin Cutaneous Melanoma Outcome from the HLA-alleles and Clinical Characteristics

Dhall

Patiyal

Kaur

et al. 2019

Preprint

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AbstractHuman Leukocyte Antigen (HLA) is an essential component of the immune system which stimulates immune cells to provide protection and defense against cancer. More than thousands of HLA alleles have been reported in the literature; but, only a specific set of HLA alleles expressed in an individual. Recognition of cancer-associated mutations by the immune system depends on the presence of a particular set of alleles, that elicit an immune response to fight against cancer. It indicates that the occurrence of specific HLA alleles also affects the outcome of the cancer patients. In the current study, prediction models have been developed using 415 skin cutaneous melanoma (SKCM) patients for predicting the overall survival of patients from their HLA-alleles. It has been observed that, the presence of certain superalleles in the patients, is responsible for improved overall survival which were referred as favourable superalleles like HLA-B*55 (HR=0.15, 95% CI 0.034 to 0.67), HLA-A*01 (HR=0.5, 95% CI 0.3 to 0.8). In contrast, presence of certain superalleles in the patients is responsible for their poor survival, those superalleles were referred as unfavourable superalleles such as HLA-B*50 (HR=2.76, 95% CI 1.284 to 5.941), HLA-DRB1*12 (HR=3.44, 95% CI 1.64 to 7.2). We developed prediction models using 14 HLA-superalleles and five clinical characteristics for predicting high-risk SKCM patients and achieve HR=4.52 (95% CI 3.088-6.609) with p-value = 8.01E-15. Lastly, we provide a web-based service to community for predicting the risk in SKCM patients (https://webs.iiitd.edu.in/raghava/skcmhrp/)

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“…These performance evaluation parameters are well-defined in the literature and have been extensively used in assessing the performance of the model. 12, 32, 33 where FP, FN, TP, and TN are false positive, false negative, true positive, and true negative predictions, respectively.…”

Section: Methodsmentioning

confidence: 99%

ChAlPred: A Web Server for Prediction of Allergenicity of Chemical Compounds

Sharma

Patiyal

Dhall

et al. 2021

Preprint

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Allergy is the abrupt reaction of the immune system that may occur after the exposure with allergens like protein/peptide or chemical allergens. In past number of methods of have been developed for classifying the protein/peptide based allergen. To the best of our knowledge, there is no method to classify the allergenicity of chemical compound. Here, we have proposed a method named ChAlPred, which can be used to fill the gap for predicting the chemical compound that might cause allergy. In this study, we have obtained the dataset of 403 allergen and 1074 non-allergen chemical compounds and used 2D, 3D and FP descriptors to train, test and validate our prediction models. The fingerprint analysis of the dataset indicates that PubChemFP129 and GraphFP1014 are more frequent in the allergenic chemical compounds, whereas KRFP890 is highly present in non-allergenic chemical compounds. Our XGB based model achieved the AUC of 0.89 on validation dataset using 2D descriptors. RF based model has outperformed other classifiers using 3D descriptors (AUC = 0.85), FP descriptors (AUC = 0.92), combined descriptors (AUC = 0.93), and hybrid model (AUC = 0.92) on validation dataset. In addition, we have also reported some FDA-approved drugs like Cefuroxime, Spironolactone, and Tioconazole which can cause the allergic symptoms. A user user-friendly web server named ChAlPred has been developed to predict the chemical allergens. It can be easily accessed at https://webs.iiitd.edu.in/raghava/chalpred/.

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NAGbinder: An approach for identifying N‐acetylglucosamine interacting residues of a protein from its primary sequence

Cited by 32 publications

References 41 publications

Computing Skin Cutaneous Melanoma Outcome From the HLA-Alleles and Clinical Characteristics

Computing Skin Cutaneous Melanoma Outcome From the HLA-Alleles and Clinical Characteristics

Computing Skin Cutaneous Melanoma Outcome from the HLA-alleles and Clinical Characteristics

ChAlPred: A Web Server for Prediction of Allergenicity of Chemical Compounds

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