ArticleJournal of translational medicine2018
Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants.
Article in Journal of translational medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed, 86 citations in OpenAlex.
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- A large language model for predicting neurotoxic peptides and neurotoxins.Protein science : a publication of the Protein Society · 2025Article
- Advancing therapeutic vaccines for chronic hepatitis B: Integrating reverse vaccinology and immunoinformatics.World journal of hepatology · 2025Review
- Development of a Broad-Spectrum Pan-Mpox Vaccine via Immunoinformatic Approaches.International journal of molecular sciences · 2025Article
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- Machine learning insights into vaccine adjuvants and immune outcomes.Frontiers in immunology · 2025Article
- Design of a Multiepitope Pan-Proteomic mRNA Vaccine Construct Against African Swine Fever Virus: A Reverse Vaccinology Approach.Veterinary medicine international · 2025Article
- Interpretable molecular encodings and representations for machine learning tasks.Computational and structural biotechnology journal · 2024Article
- Advances of computational methods enhance the development of multi-epitope vaccines.Briefings in bioinformatics · 2024Review
- Tumor Neoepitope-Based Vaccines: A Scoping Review on Current Predictive Computational Strategies.Vaccines · 2024Article
- Immunomodulatory Peptides as Vaccine Adjuvants and Antimicrobial Agents.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Comparative analysis of adhesion virulence protein FadA from gut-associated bacteria of colorectal cancer patients (Journal of Cancer · 2024Article
- Personalized cancer vaccine design using AI-powered technologies.Frontiers in immunology · 2024Review
- In silico designing of an epitope-based peptide vaccine cocktail against Nipah virus: an Indian population-based epidemiological study.Archives of microbiology · 2023Article
- Teleost Piscidins-In Silico Perspective of Natural Peptide Antibiotics from Marine Sources.Antibiotics (Basel, Switzerland) · 2023Review
- Review
- Polypharmacological Cell-Penetrating Peptides from Venomous Marine Animals Based on Immunomodulating, Antimicrobial, and Anticancer Properties.Marine drugs · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundEvidences in literature strongly advocate the potential of immunomodulatory peptides for use as vaccine adjuvants. All the mechanisms of vaccine adjuvants ensuing immunostimulatory effects directly or indirectly stimulate antigen presenting cells (APCs). While numerous methods have been developed in the past for predicting B cell and T-cell epitopes; no method is available for predicting the peptides that can modulate the APCs.
methodsWe named the peptides that can activate APCs as A-cell epitopes and developed methods for their prediction in this study. A dataset of experimentally validated A-cell epitopes was collected and compiled from various resources. To predict A-cell epitopes, we developed support vector machine-based machine learning models using different sequence-based features.
resultsA hybrid model developed on a combination of sequence-based features (dipeptide composition and motif occurrence), achieved the highest accuracy of 95.71% with Matthews correlation coefficient (MCC) value of 0.91 on the training dataset. We also evaluated the hybrid models on an independent dataset and achieved a comparable accuracy of 95.00% with MCC 0.90.
conclusionThe models developed in this study were implemented in a web-based platform VaxinPAD to predict and design immunomodulatory peptides or A-cell epitopes. This web server available at http://webs.iiitd.edu.in/raghava/vaxinpad/ will facilitate researchers in designing peptide-based vaccine adjuvants.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.