Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2020
|
| Online Access: | https://doi.org/10.5281/zenodo.20155359 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866901910530818048 |
|---|---|
| author | Kaur, Dilraj Arora, Chakit Raghava, Gajendra |
| author_facet | Kaur, Dilraj Arora, Chakit Raghava, Gajendra |
| contents | <div class="markdown-heading"> <h1 class="heading-element">PRRpred: A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information</h1> <a class="anchor" href="https://github.com/sudhanshus-hash/PRRpred#prrpred-a-hybrid-model-for-predicting-pattern-recognition-receptors-using-evolutionary-information"></a></div> <p>PRRpred is a computational web server developed for predicting Pattern Recognition Receptors, also known as PRRs, from protein sequences.</p> <p>Pattern Recognition Receptors are important components of the innate immune system. They recognize pathogen-associated molecular patterns and damage-associated molecular patterns, helping the immune system detect infection, tissue damage, and inflammation. PRRpred uses similarity-based search, machine learning, and evolutionary information to classify proteins as PRRs or non-PRRs.</p> <p>Web Server: <a href="http://webs.iiitd.edu.in/raghava/prrpred/" rel="nofollow">http://webs.iiitd.edu.in/raghava/prrpred/</a></p> <div class="markdown-heading"> <h2 class="heading-element">Citation</h2> <a class="anchor" href="https://github.com/sudhanshus-hash/PRRpred#citation"></a></div> <p>Kaur, D., Arora, C., and Raghava, G. P. S. A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information. Frontiers in Immunology, 11, 71, 2020.</p> <p><a href="https://doi.org/10.3389/fimmu.2020.00071" rel="nofollow">https://doi.org/10.3389/fimmu.2020.00071</a></p> <p> </p> <div class="markdown-heading"> <h2 class="heading-element">About the Research</h2> <a class="anchor" href="https://github.com/sudhanshus-hash/PRRpred#about-the-research"></a></div> <p>Pattern Recognition Receptors are germline-encoded immune receptors that recognize conserved molecular patterns present in pathogens or damaged host cells. These receptors play a major role in innate immunity and help initiate inflammatory and antimicrobial responses.</p> <p>Major classes of PRRs include:</p> <ul> <li>Toll-like receptors</li> <li>NOD-like receptors</li> <li>RIG-I-like receptors</li> <li>C-type lectin receptors</li> <li>Scavenger receptors</li> <li>Mannose receptors</li> <li>Peptidoglycan recognition proteins</li> <li>Secreted PRRs such as collectins, ficolins, and pentraxins</li> </ul> <p>PRRs are involved in pathogen recognition, inflammation, autoimmune disorders, cancer, immunodeficiency, and vaccine adjuvant research. PRRpred was developed to computationally identify PRRs from protein sequences and support innate immunity research.</p> <p>Data Compilation: PRR sequences were collected from PRRDB 2.0, while non-PRR sequences were obtained from Swiss-Prot. After removing identical sequences, the final dataset contained 179 unique PRRs and 274 non-PRRs.</p> <p>Methodology: PRRpred uses a hybrid prediction strategy combining BLAST-based similarity search with machine learning models trained on protein sequence composition and evolutionary information.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20155359 |
| institution | Zenodo |
| language | |
| publishDate | 2020 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | PRRpred: A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information Kaur, Dilraj Arora, Chakit Raghava, Gajendra <div class="markdown-heading"> <h1 class="heading-element">PRRpred: A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information</h1> <a class="anchor" href="https://github.com/sudhanshus-hash/PRRpred#prrpred-a-hybrid-model-for-predicting-pattern-recognition-receptors-using-evolutionary-information"></a></div> <p>PRRpred is a computational web server developed for predicting Pattern Recognition Receptors, also known as PRRs, from protein sequences.</p> <p>Pattern Recognition Receptors are important components of the innate immune system. They recognize pathogen-associated molecular patterns and damage-associated molecular patterns, helping the immune system detect infection, tissue damage, and inflammation. PRRpred uses similarity-based search, machine learning, and evolutionary information to classify proteins as PRRs or non-PRRs.</p> <p>Web Server: <a href="http://webs.iiitd.edu.in/raghava/prrpred/" rel="nofollow">http://webs.iiitd.edu.in/raghava/prrpred/</a></p> <div class="markdown-heading"> <h2 class="heading-element">Citation</h2> <a class="anchor" href="https://github.com/sudhanshus-hash/PRRpred#citation"></a></div> <p>Kaur, D., Arora, C., and Raghava, G. P. S. A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information. Frontiers in Immunology, 11, 71, 2020.</p> <p><a href="https://doi.org/10.3389/fimmu.2020.00071" rel="nofollow">https://doi.org/10.3389/fimmu.2020.00071</a></p> <p> </p> <div class="markdown-heading"> <h2 class="heading-element">About the Research</h2> <a class="anchor" href="https://github.com/sudhanshus-hash/PRRpred#about-the-research"></a></div> <p>Pattern Recognition Receptors are germline-encoded immune receptors that recognize conserved molecular patterns present in pathogens or damaged host cells. These receptors play a major role in innate immunity and help initiate inflammatory and antimicrobial responses.</p> <p>Major classes of PRRs include:</p> <ul> <li>Toll-like receptors</li> <li>NOD-like receptors</li> <li>RIG-I-like receptors</li> <li>C-type lectin receptors</li> <li>Scavenger receptors</li> <li>Mannose receptors</li> <li>Peptidoglycan recognition proteins</li> <li>Secreted PRRs such as collectins, ficolins, and pentraxins</li> </ul> <p>PRRs are involved in pathogen recognition, inflammation, autoimmune disorders, cancer, immunodeficiency, and vaccine adjuvant research. PRRpred was developed to computationally identify PRRs from protein sequences and support innate immunity research.</p> <p>Data Compilation: PRR sequences were collected from PRRDB 2.0, while non-PRR sequences were obtained from Swiss-Prot. After removing identical sequences, the final dataset contained 179 unique PRRs and 274 non-PRRs.</p> <p>Methodology: PRRpred uses a hybrid prediction strategy combining BLAST-based similarity search with machine learning models trained on protein sequence composition and evolutionary information.</p> |
| title | PRRpred: A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information |
| url | https://doi.org/10.5281/zenodo.20155359 |