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Journal of Translational Medicine

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Open Access 01-12-2018 | Research

Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants

Authors: Gandharva Nagpal, Kumardeep Chaudhary, Piyush Agrawal, Gajendra P. S. Raghava

Published in: Journal of Translational Medicine | Issue 1/2018

Abstract

Background

Evidences 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.

Methods

We 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.

Results

A 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.

Conclusion

The 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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Title: Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants
Authors: Gandharva Nagpal
Kumardeep Chaudhary
Piyush Agrawal
Gajendra P. S. Raghava
Publication date: 01-12-2018
Publisher: BioMed Central
Published in: Journal of Translational Medicine / Issue 1/2018
Electronic ISSN: 1479-5876
DOI: https://doi.org/10.1186/s12967-018-1560-1

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Conclusion

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