Advertisement

PPI Inference Algorithms Using MS Data

  • Ming Zheng
  • Mugui ZhuoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1117)

Abstract

With the development of proteomics, the focus of research has begun to focus on the establishment of all human protein interaction (PPI) networks. Mass spectrometry has become a representative method for predicting protein interaction. Mass spectrometry is one of the main experimental means to construct protein interaction network. Based on mass spectrometry, a large number of protein purification data, such as AP-MS data and PCP-MS data, have been generated. These data provide important data support for PPI network construction, but it is not only inefficient but also unrealistic to construct PPI network by manual means. Therefore, the network inference algorithm for PCP-MS data is one of the hot topics in bioinformatics. In this paper, the algorithm of constructing a kind of mainstream mass spectrometry data (PCP-MS data) PPI network is studied. Considering the existing bottlenecks, the goal of constructing a high-quality PPI network is achieved. The existing PPI network inference algorithm for PCP-MS data is still in its infancy, and there are few related methods. At the same time, there are some problems in the quality of the results of the algorithm, such as: (1) many wrong interactions are included in the results of different inference algorithms, while some correct interactions are omitted; (2) different inference algorithms perform differently on the same data set; (3) for different data sets, the volatility variance of the performance of the same algorithm is large.

Keywords

MS data PPI network Direct protein interaction Correlation analysis Sequencing integration 

Notes

Acknowledgement

This work was supported by grants from The National Natural Science Foundation of China (No. 61862056), the Guangxi Natural Science Foundation (No. 2017GXNSFAA198148) foundation of Wuzhou University (No. 2017B001), Guangxi Colleges and Universities Key Laboratory of Professional Software Technology, Wuzhou University.

References

  1. 1.
    Wang, E.C., Weng, G.Q., Sun, H.Y., et al.: Assessing the performance of the MM/PBSA and MM/GBSA methods. 10. Impacts of enhanced sampling and variable dielectric model on protein-protein Interactions. Phys. Chem. Chem. Phys. 21(35), 18958–18969 (2019)CrossRefGoogle Scholar
  2. 2.
    Jumppanen, M., Kinnunen, S.M., Valimaki, M.J., et al.: Synthesis, identification, and structure-activity relationship analysis of GATA4 and NKX2-5 protein-protein interaction modulators. J. Med. Chem. 62(17), 8284–8310 (2019)CrossRefGoogle Scholar
  3. 3.
    Li, K., Zhang, Z.X., Ma, S.W., et al.: Effects of NH4H2PO4-loading and temperature on the two-stage pyrolysis of biomass: analytical pyrolysis-gas chromatography/mass spectrometry study. J. Biobased Mater. Bioenergy 14(1), 76–82 (2020)CrossRefGoogle Scholar
  4. 4.
    Wu, D., Li, J.W., Struwe, W.B., et al.: Probing N-glycoprotein microheterogeneity by lectin affinity purification-mass spectrometry analysis. Chem. Sci. 10(19), 5146–5155 (2019)CrossRefGoogle Scholar
  5. 5.
    Crozier, T.W.M., Tinti, M., Larance, M., et al.: Prediction of protein complexes in Trypanosoma brucei by protein correlation profiling mass spectrometry and machine learning. Mol. Cell. Proteomics 16(12), 2254–2267 (2017)CrossRefGoogle Scholar
  6. 6.
    Chavez, J.D., Mohr, J.P., Mathay, M., et al.: Systems structural biology measurements by in vivo cross-linking with mass spectrometry. Nat. Protoc. 14(8), 2318–2343 (2019)CrossRefGoogle Scholar
  7. 7.
    Johnson, M.E., Bustos, A.R.M., Winchester, M.R.: Practical utilization of spICP-MS to study sucrose density gradient centrifugation for the separation of nanoparticles. Anal. Bioanal. Chem. 408(27), 7629–7640 (2016)CrossRefGoogle Scholar
  8. 8.
    Kahle, J., Stein, M., Watzig, H.: Design of experiments as a valuable tool for biopharmaceutical analysis with (imaged) capillary isoelectric focusing. Electrophoresis 40(18–19), 2382–2389 (2019)Google Scholar
  9. 9.
    Shaddeau, A.W., Schneck, N.A., Li, Y., et al.: Development of a new tandem ion exchange and size exclusion chromatography method to monitor vaccine particle titer in cell culture media. Anal. Chem. 91(10), 6430–6434 (2019)Google Scholar
  10. 10.
    Zhang, B., Wu, Q., Xu, R., et al.: The promising novel biomarkers and candidate small molecule drugs in lower-grade glioma: evidence from bioinformatics analysis of high-throughput data[J]. J. Cell. Biochem. 120(9), 15106–15118 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Guangxi Colleges and Universities Key Laboratory of Professional Software TechnologyWuzhou UniversityWuzhouChina

Personalised recommendations