TAP 1.0: A robust immunoinformatic tool for the prediction of tumor T-cell antigens based on AAindex properties. (April 2021)

• Record Type: Journal Article
• Authors: Herrera-Bravo, Jesús; Herrera Belén, Lisandra; Farias, Jorge G.; Beltrán, Jorge F.
• Is Part Of: Computational biology and chemistry. Volume 91(2021)
• Publication Date: 2021-04
• Subjects: Tumor -- Antigen -- Prediction -- Machine learning -- T-cell -- Peptide; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2021.107452
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Tumor antigens (TAs) are potential immunotherapeutic drugs against cancer. In this work we developed a robust tool for the prediction of TAs called TAP 1.0. TAP 1.0 is based on the quadratic discriminant classifier. TAP 1.0 could have a significant impact in the discovery of new TAs. Abstract: Immunotherapy is a research area with great potential in drug discovery for cancer treatment. Because of the capacity of tumor antigens to activate the immune response and promote the destruction of tumor cells, they are considered excellent immunotherapeutic drugs. In this work, we evaluated fifteen machine learning algorithms for the classification of tumor antigens. For this purpose, we build robust datasets, carefully selected from the TANTIGEN and IEDB databases. The feature computation of all antigens in this study was performed by developing a script written in Python 3.8, which allowed the calculation of 544 physicochemical and biochemical properties extracted from the AAindex database. All classifiers were subjected to the training, 10-fold cross-validation, and testing on an independent dataset. The results of this study showed that the quadratic discriminant classifier presented the best performance measures over the independent dataset, accuracy = 0.7384, AUC = 0.817, recall = 0.676, precision = 0.7857, F1 = 0.713, kappa = 0.4764, and Matthews correlation coefficient = 0.4834, outperforming common machine learning classifiers used in the bioinformatics area. We believe that our prediction model could be of great importance in the field of cancer immunotherapy for the search of potential tumor antigens. Taking all aspects mentioned before, we developed an immunoinformatic tool called TAP 1.0 with a friendly interface for tumor antigens prediction, available at https://tapredictor.herokuapp.com/ .