Protein-protein Interaction Network Inference From Mass Spectrometry Data

With the development of proteomics,mass spectrometry has become a representative method for protein-protein interaction(PPI)inference.Based on the technology of mass spectrometry,a large amount of experimental protein MS data is generated,such as affinity purification-mass spectrometry(AP-MS)data and protein correlation profiling-mass spectrometry(PCP-MS)data,which provide important data support for PPI networks inference.This thesis focuses on the problem of PPI network inference for two main types of mass spectral data(AP-MS data and PCP-MS data),and designs effective method respectively to solve the issue of current bottlenecks,which achieves the construction of high-quality PPI network.This paper presents an initialization-and-refinement framework for direct protein-protein interaction network from AP-MS data.It is composed of two phases: initialization phase and refinement phase.In the initialization phase,we utilize a standard PPI scoring method to generate a PPI network that may contains both direct and indirect interactions.In the refinement phase,we distinguish direct interactions from indirect interactions in the initial PPI networks for direct protein-protein interaction network inference.Experimental results show that this model is capable of detecting direct interactions between protein pairs effectively.For the problem of PPI network inference from PCP-MS data,this paper proposes a PPI scoring method based on correlation analysis and rank aggregation.The method is based on unsupervised learning and includes two steps.Firstly,compute correlation coefficient between protein pairs,and obtain multiple results of PPI scores.Secondly,combine multiple results for each pair of proteins via rank aggregation to a single PPI score.The experimental results show that this method is comparable with those supervised learning methods using standard reference set.