A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information

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

doi:10.3389/fimmu.2020.00071

Cited by 21 publications

(9 citation statements)

References 64 publications

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“…It has been shown in number of studies in the past that evolutionary information based models perform better than single sequence models [72–75]. Thus, this study also explores the potential of evolutionary information in predicting AFPs.…”

Section: Resultsmentioning

confidence: 97%

Prediction of anti-freezing proteins from their evolutionary profile

Kumar,

Choudhury,

Bajiya

et al. 2024

Preprint

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Prediction of antifreeze proteins (AFPs) holds significant importance due to their diverse applications in healthcare. An inherent limitation of current AFP prediction methods is their reliance on unreviewed proteins for evaluation. This study evaluates proposed and existing methods on an independent dataset containing 81 AFPs and 73 non-AFPs obtained from Uniport, which have been already reviewed by experts. Initially, we constructed machine learning models for AFP prediction using selected composition-based protein features and achieved a peak AUC of 0.90 with an MCC of 0.69 on the independent dataset. Subsequently, we observed a notable enhancement in model performance, with the AUC increasing from 0.90 to 0.93 upon incorporating evolutionary information instead of relying solely on the primary sequence of proteins. Furthermore, we explored hybrid models integrating our machine learning approaches with BLAST-based similarity and motif-based methods. However, the performance of these hybrid models either matched or was inferior to that of our best machine-learning model. Our best model based on evolutionary information outperforms all existing methods on independent/validation dataset. To facilitate users, a user-friendly web server with a standalone package named AFPropred was developed (https://webs.iiitd.edu.in/raghava/afpropred).

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Section: Resultsmentioning

confidence: 97%

Prediction of anti-freezing proteins from their evolutionary profile

Kumar,

Choudhury,

Bajiya

et al. 2024

Preprint

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“…In order to provide internal and external validation, we divide our datasets into training and validation sets in 80% and 20% ration, respectively. In case of internal validation, we used a five-fold cross-validation technique, where sequences in the training sets are first arbitrarily divided into five equivalent folds ( 58 , 59 ). Thereafter, four of these folds are used for training and the remaining fold is used for testing.…”

Section: Methodsmentioning

confidence: 99%

In-Silico Tool for Predicting, Scanning, and Designing Defensins

et al. 2021

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Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a need to develop a fast and accurate method for predicting defensins. In this study, a systematic attempt has been made to develop models for predicting defensins from available information on defensins. We created a dataset of defensins and non-defensins called the main dataset that contains 1,036 defensins and 1,035 AMPs (antimicrobial peptides, or non-defensins) to understand the difference between defensins and AMPs. Our analysis indicates that certain residues like Cys, Arg, and Tyr are more abundant in defensins in comparison to AMPs. We developed machine learning technique-based models on the main dataset using a wide range of peptide features. Our SVM (support vector machine)-based model discriminates defensins and AMPs with MCC of 0.88 and AUC of 0.98 on the validation set of the main dataset. In addition, we created an alternate dataset that consists of 1,036 defensins and 1,054 non-defensins obtained from Swiss-Prot. Models were also developed on the alternate dataset to predict defensins. Our SVM-based model achieved maximum MCC of 0.96 with AUC of 0.99 on the validation set of the alternate dataset. All models were trained, tested, and validated using standard protocols. Finally, we developed a web-based service “DefPred” to predict defensins, scan defensins in proteins, and design the best defensins from their analogs. The stand-alone software and web server of DefPred are available at https://webs.iiitd.edu.in/raghava/defpred.

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“…The query peptide is annotated based on its alignment score with known peptides. In our study, we implemented the BLAST-based technique blastp (BLAST+ 2.7.1), a peptide-peptide BLAST for the prediction of B-cell epitopes and non B-cell epitopes [42][43][44][45]. BLAST formatted database were constructed using the training dataset against which the query sequences (sequences in the test set) were hit at various e-values that ranges from 1e-6 to 1e+3.…”

Section: Similarity Searchmentioning

confidence: 99%

“…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: Similarity Search Approachmentioning

confidence: 99%

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

Kumar,

Tripathi,

Sharma

et al. 2024

Computers in Biology and Medicine

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A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information

Cited by 21 publications

References 64 publications

Prediction of anti-freezing proteins from their evolutionary profile

Prediction of anti-freezing proteins from their evolutionary profile

In-Silico Tool for Predicting, Scanning, and Designing Defensins

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

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