A Hybrid Model for Predicting Pattern Recognition Receptors using Evolutionary Information

Kaur, Dilraj; Arora, Chakit; Raghava, Gajendra P. S.

doi:10.1101/846469

Cited by 4 publications

(7 citation statements)

References 53 publications

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“…As BLAST is previously used for annotating and assigning functions to proteins based on similarity searches, we have developed a similarity search-based module to further improve the model performance [42, 47]. We implemented a similar concept (blastp) for annotating the given peptide as a B-cell epitope or non B-cell epitope.…”

Section: Resultsmentioning

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

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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/).

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Section: Resultsmentioning

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

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show abstract

“…To further improve our model accuracy, we have designed a similarity search based module based on the BLAST similarity search score, as BLAST is previously used for the annotation and assignment of the functions to a protein on the basis of similarity search [33, 37]. We also used the same approach (blastp) for assigning the given peptide as T1DM associated or non-T1DM associated peptide.…”

Section: Resultsmentioning

confidence: 99%

“…The query peptide is annotated based on its alignment score with known peptides. One of the commonly used method for similarity search is BLAST [33][34][35][36]. Currently, we have implemented BLAST based search for the identification of similarity of peptides/epitopes with T1DM causing and non-T1DM causing peptides.…”

Section: Similarity Searchmentioning

confidence: 99%

Identification of Antigenic Regions Responsible for inducing Type 1 diabetes mellitus

Kumar

Patiyal

Choudhury

et al. 2022

Preprint

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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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“…The process is repeated five times and each sample is processed once as a testing data point and four times as training data point. Five-fold cross-validation has been successfully implemented in previous studies [ 40 , 41 , 42 , 43 , 44 , 45 , 46 ].…”

Section: Methodsmentioning

confidence: 99%

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data

Arora

Kaur

Lathwal

et al. 2020

Heliyon

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Risk assessment in cutaneous melanoma (CM) patients is one of the major challenges in the effective treatment of CM patients. Traditionally, clinico-pathological features such as Breslow thickness, American Joint Committee on Cancer (AJCC) tumor staging, etc. are utilized for this purpose. However, due to advancements in technology, most of the upcoming risk prediction methods are gene-expression profile (GEP) based. In this study, we have tried to develop new GEP and clinico-pathological features-based biomarkers and assessed their prognostic strength in contrast to existing prognostic methods. We developed risk prediction models using the expression of the genes associated with different cancer-related pathways and got a maximum hazard ratio (HR) of 2.52 with p-value~10 À8 for the apoptotic pathway. Another model, based on combination of apoptotic and notch pathway genes boosted the HR to 2.57. Next, we developed models based on individual clinical features and got a maximum HR of 2.45 with p-value~10 À6 for Breslow thickness. We also developed models using the best features of clinical as well as gene-expression data and obtained a maximum HR of 3.19 with p-value~10 À9. Finally, we developed a new ensemble method using clinical variables only and got a maximum HR of 6.40 with p-valuẽ 10 À15. Based on this method, a web-based service and an android application named 'CMcrpred' is available at (https://webs.iiitd.edu.in/raghava/cmcrpred/) and Google Play Store respectively to facilitate scientific community. This study reveals that our new ensemble method based on only clinico-pathological features overperforms methods based on GEP based profiles as well as currently used AJCC staging. It also highlights the need to explore the full potential of clinical variables for prognostication of cancer patients.

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A Hybrid Model for Predicting Pattern Recognition Receptors using Evolutionary Information

Cited by 4 publications

References 53 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

Identification of Antigenic Regions Responsible for inducing Type 1 diabetes mellitus

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data

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