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Identification of Protein–Protein Interaction Associated Functions Based on Gene Ontology

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Abstract

Protein–protein interactions (PPIs) involve the physical or functional contact between two or more proteins. Generally, proteins that can interact with each other always have special relationships. Some previous studies have reported that gene ontology (GO) terms are related to the determination of PPIs, suggesting the special patterns on the GO terms of proteins in PPIs. In this study, we explored the special GO term patterns on human PPIs, trying to uncover the underlying functional mechanism of PPIs. The experimental validated human PPIs were retrieved from STRING database, which were termed as positive samples. Additionally, we randomly paired proteins occurring in positive samples, yielding lots of negative samples. A simple calculation was conducted to count the number of positive samples for each GO term pair, where proteins in samples were annotated by GO terms in the pair individually. The similar number for negative samples was also counted and further adjusted due to the great gap between the numbers of positive and negative samples. The difference of the above two numbers and the relative ratio compared with the number on positive samples were calculated. This ratio provided a precise evaluation of the occurrence of GO term pairs for positive samples and negative samples, indicating the latent GO term patterns for PPIs. Our analysis unveiled several nuclear biological processes, including gene transcription, cell proliferation, and nutrient metabolism, as key biological functions. Interactions between major proliferative or metabolic GO terms consistently correspond with significantly reported PPIs in recent literature.

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Funding

This study was funded by the National Key R&D Program of China [2022YFF1203202], Strategic Priority Research Program of Chinese Academy of Sciences [XDA26040304, XDB38050200], the Fund of the Key Laboratory of Tissue Microenvironment and Tumor of Chinese Academy of Sciences [202002], Shandong Provincial Natural Science Foundation [ZR2022MC072].

Author information

Author notes

  1. Yu-Hang Zhang, FeiMing Huang and JiaBo Li have been contributed equally to this work.

Authors and Affiliations

  1. School of Life Sciences, Shanghai University, Shanghai, 200444, People’s Republic of China

    Yu-Hang Zhang, FeiMing Huang & Yu-Dong Cai

  2. Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Yu-Hang Zhang

  3. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, People’s Republic of China

    JiaBo Li

  4. School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209, People’s Republic of China

    WenFeng Shen

  5. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, People’s Republic of China

    Lei Chen

  6. Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou, 510507, People’s Republic of China

    KaiYan Feng

  7. Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, People’s Republic of China

    Tao Huang

  8. CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, People’s Republic of China

    Tao Huang

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  1. Yu-Hang Zhang
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Contributions

Conceptualization: TH and YDC; Methodology: JL, WS, LC and KF; Formal analysis and investigation: YZ and FH; Writing—original draft preparation: YZ, FH and JL; Writing—review and editing: TH; Funding acquisition: TH and YDC; Supervision: YDC.

Corresponding authors

Correspondence to Tao Huang or Yu-Dong Cai.

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10930_2024_10180_MOESM1_ESM.xlsx

Table S1 Sheet 1 displays the top 1000 gene ontology (GO) term pairs, arranged in descending order according to the frequency of each GO term pair in positive samples. Sheet 2 has been further refined to include only those rows from Sheet 1 that exhibit a positive difference. Furthermore, the relative ratio has been calculated for each row included in this filtered selection (XLSX 154 kb)

Table S2 The frequency of a single GO term appearing within the filtered 967 GO term pairs (XLSX 12 kb)

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Zhang, YH., Huang, F., Li, J. et al. Identification of Protein–Protein Interaction Associated Functions Based on Gene Ontology. Protein J (2024). https://doi.org/10.1007/s10930-024-10180-6

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