APRANK: Computational Prioritization of Antigenic Proteins and Peptides From Complete Pathogen Proteomes.
APRANK: Computational Prioritization of Antigenic Proteins and Peptides From Complete Pathogen Proteomes.
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DOI：
10.3389/fimmu.2021.702552
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发表时间：
2021
影响因子：
7.3
通讯作者：
Agüero F
中科院分区：
文献类型：
--
作者：
Ricci AD;Brunner M;Ramoa D;Carmona SJ;Nielsen M;Agüero F
高度平行化免疫测定的可用性重新燃起了发现传染病血清学生物标志物的兴趣。蛋白质和肽微阵列现在为潜在抗原和b细胞表位的免疫检测和验证提供了一个快速、高通量的平台。然而，在设计这些阵列时，仍然需要工具来优先考虑和选择相关探针。在这项工作中，我们描述了一种称为APRANK （Antigenic Protein and Peptide Ranker）的计算方法，该方法整合了多个分子特征，以优先考虑给定病原体蛋白质组中潜在的抗原蛋白和肽。这些特征包括亚细胞定位、重复基序的存在、天然紊乱区域、二级结构、跨膜跨越和预测与免疫系统的相互作用。我们用许多导致人类疾病的细菌和原生动物对这种方法进行了训练和测试：伯氏疏螺旋体（莱姆病）、布鲁氏菌（布鲁氏菌病）、伯氏杆菌（Q热）、大肠杆菌（胃肠炎）、土拉菌（土拉菌病）、巴西利什曼原虫（利什曼病）、钩端螺旋体（钩端螺旋体病）、麻风分枝杆菌（leprae）、结核分枝杆菌（结核病）、恶性疟原虫（疟疾）、牙龈卟啉单胞菌（牙周病）、金黄色葡萄球菌（菌血症）、化脓性链球菌（A群链球菌感染）、刚地弓形虫（弓形虫病）和克氏锥虫（恰加斯病）。我们使用非参数roc曲线对该综合方法进行了评价，并以盘尾丝虫作为独立数据集进行了无偏验证。我们发现APRANK能够成功预测所有被测病原体的抗原性，促进抗原富集蛋白亚群的产生。我们使APRANK可用，以促进在传染病新的诊断抗原的鉴定。
Availability of highly parallelized immunoassays has renewed interest in the discovery of serology biomarkers for infectious diseases. Protein and peptide microarrays now provide a rapid, high-throughput platform for immunological testing and validation of potential antigens and B-cell epitopes. However, there is still a need for tools to prioritize and select relevant probes when designing these arrays. In this work we describe a computational method called APRANK (Antigenic Protein and Peptide Ranker) which integrates multiple molecular features to prioritize potentially antigenic proteins and peptides in a given pathogen proteome. These features include subcellular localization, presence of repetitive motifs, natively disordered regions, secondary structure, transmembrane spans and predicted interaction with the immune system. We trained and tested this method with a number of bacteria and protozoa causing human diseases: Borrelia burgdorferi (Lyme disease), Brucella melitensis (Brucellosis), Coxiella burnetii (Q fever), Escherichia coli (Gastroenteritis), Francisella tularensis (Tularemia), Leishmania braziliensis (Leishmaniasis), Leptospira interrogans (Leptospirosis), Mycobacterium leprae (Leprae), Mycobacterium tuberculosis (Tuberculosis), Plasmodium falciparum (Malaria), Porphyromonas gingivalis (Periodontal disease), Staphylococcus aureus (Bacteremia), Streptococcus pyogenes (Group A Streptococcal infections), Toxoplasma gondii (Toxoplasmosis) and Trypanosoma cruzi (Chagas Disease). We have evaluated this integrative method using non-parametric ROC-curves and made an unbiased validation using Onchocerca volvulus as an independent data set. We found that APRANK is successful in predicting antigenicity for all pathogen species tested, facilitating the production of antigen-enriched protein subsets. We make APRANK available to facilitate the identification of novel diagnostic antigens in infectious diseases.
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DOI：
10.4269/ajtmh.17-0437
发表时间：
2018-01
期刊：
The American journal of tropical medicine and hygiene
影响因子：
--
作者：
Liu EW;Skinner J;Tran TM;Kumar K;Narum DL;Jain A;Ongoiba A;Traoré B;Felgner PL;Crompton PD
通讯作者：
Crompton PD
影响因子：
3.8
作者：
Lessa-Aquino C;Borges Rodrigues C;Pablo J;Sasaki R;Jasinskas A;Liang L;Wunder EA Jr;Ribeiro GS;Vigil A;Galler R;Molina D;Liang X;Reis MG;Ko AI;Medeiros MA;Felgner PL
通讯作者：
Felgner PL
影响因子：
3.8
作者：
Lagatie O;Van Dorst B;Stuyver LJ
通讯作者：
Stuyver LJ
影响因子：
3.8
作者：
Durante IM;La Spina PE;Carmona SJ;Agüero F;Buscaglia CA
通讯作者：
Buscaglia CA
影响因子：
--
作者：
Beare, Paul A.;Chen, Chen;Heinzen, Robert A.
通讯作者：
Heinzen, Robert A.