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iTTCA-MFF: identifying tumor T cell antigens based on multiple feature fusion

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Abstract

Cancer is a terrible disease, recent studies reported that tumor T cell antigens (TTCAs) may play a promising role in cancer treatment. Since experimental methods are still expensive and time-consuming, it is highly desirable to develop automatic computational methods to identify tumor T cell antigens from the huge amount of natural and synthetic peptides. Hence, in this study, a novel computational model called iTTCA-MFF was proposed to identify TTCAs. In order to describe the sequence effectively, the physicochemical (PC) properties of amino acid and residue pairwise energy content matrix (RECM) were firstly employed to encode peptide sequences. Then, two different approaches including covariance and Pearson’s correlation coefficient (PCC) were used to collect discriminative information from PC and RECM matrixes. Next, an effective feature selection approach called the least absolute shrinkage and selection operator (LAASO) was adopted to select the optimal features. These selected optimal features were fed into support vector machine (SVM) for identifying TTCAs. We performed experiments on two different datasets, experimental results indicated that the proposed method is promising and may play a complementary role to the existing methods for identifying TTCAs. The datasets and codes can be available at https://figshare.com/articles/online_resource/iTTCA-MFF/17636120.

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Data availability

The datasets and source code of this study can be downloaded via https://figshare.com/articles/online_resource/iTTCA-MFF/17636120.

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Funding

This work was supported by the National Nature Scientific Foundation of China (No. 62061019), the General Project of Jiangxi Natural Science Foundation (20202BABL202014), and the General Project of Jiangxi Education Department (GJJ190587).

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Authors and Affiliations

  1. School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang, 330003, China

    Hongliang Zou, Fan Yang & Zhijian Yin

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  1. Hongliang Zou
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  2. Fan Yang
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  3. Zhijian Yin
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Contributions

Hongliang Zou: conceptualization, methodology, data curation, writing-original draft, preparation, visualization, investigation, validation, writing-review and editing. Fan Yang: supervision. Zhijian Yin: supervision, funding acquisition.

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Correspondence to Hongliang Zou.

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Zou, H., Yang, F. & Yin, Z. iTTCA-MFF: identifying tumor T cell antigens based on multiple feature fusion. Immunogenetics 74, 447–454 (2022). https://doi.org/10.1007/s00251-022-01258-5

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