用于设计基于肽的疫苗佐剂的抗原呈递细胞调节剂的计算机

StephenW

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

    Gandharva Nagpal†, Kumardeep Chaudhary†, Piyush Agrawal and Gajendra P. S. RaghavaEmail author

†Contributed equally
Journal of Translational Medicine201816:181

https://doi.org/10.1186/s12967-018-1560-1

©  The Author(s) 2018

Received: 15 January 2018

Accepted: 23 June 2018

Published: 3 July 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.
Keywords
Immunomodulatory peptideAntigen presenting cellsA-cell epitopesSupport vector machineAdjuvants

StephenW

用于设计基于肽的疫苗佐剂的抗原呈递细胞调节剂的计算机辅助预测

    Gandharva Nagpal†，Kumardeep Chaudhary†，Piyush Agrawal和Gajendra P. S. Raghava电子书作者

†平均提供
Journal of Translational Medicine201816：181

https://doi.org/10.1186/s12967-018-1560-1

©作者2018

收到：2018年1月15日

接受：2018年6月23日

发布时间：2018年7月3日
抽象
背景

文献中的证据强烈提倡免疫调节肽用作疫苗佐剂的潜力。疫苗佐剂的所有机制随后产生免疫刺激作用，直接或间接刺激抗原呈递细胞（APC）。虽然过去已经开发了许多方法来预测B细胞和T细胞表位;没有方法可用于预测可以调节APC的肽。
方法

我们将可以激活APC的肽命名为A细胞表位，并在本研究中开发了预测它们的方法。收集实验验证的A细胞表位的数据集并从各种资源汇编。为了预测A细胞表位，我们使用不同的基于序列的特征开发了基于支持向量机的机器学习模型。
结果

基于序列特征（二肽组成和基序发生）的组合开发的混合模型在训练数据集上达到最高准确度95.71％，马修斯相关系数（MCC）值为0.91。我们还在独立数据集上评估了混合模型，并且与MCC 0.90实现了95.00％的可比精度。
结论

在该研究中开发的模型在基于网络的平台VaxinPAD中实施，以预测和设计免疫调节肽或A细胞表位。 http://webs.iiitd.edu.in/raghava/vaxinpad/上提供的这个网络服务器将有助于研究人员设计基于肽的疫苗佐剂。
关键词
免疫调节肽抗原呈递细胞A细胞表位支持载体机Adjuvants

StephenW

https://translational-medicine.b ... 6/s12967-018-1560-1

MP4

想起重庆啤酒
印度终于发力了吗
http://webs.iiitd.edu.in/raghava/vaxinpad/