Computer Science ›› 2019, Vol. 46 ›› Issue (12): 313-321.doi: 10.11896/jsjkx.181102215

• Interdiscipline & Frontier • Previous Articles     Next Articles

PPI Network Inference Algorithm for PCP-MS Data

CHEN Zheng, TIAN Bo, HE Zeng-you   

  1. (School of Software Technology,Dalian University of Technology,Dalian,Liaoning 116000,China)
  • Received:2018-11-29 Online:2019-12-15 Published:2019-12-17

Abstract: With the development of proteomics,scholars begin to pay more attention to the construction of Protein-Protein Interaction (PPI) network.Mass spectrometry(MS) has become a representative method for protein-protein inte-raction (PPI) inference,and it is one of the main experiment method to construct PPI network.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 the construction of PPI networks,but constructing PPI networks by hand is impracticable and time consuming.Thus,PPI network inference algorithm for PCP-MS data has begun to become the research hotspot in bioinformatics.This thesis focused on the problem of PPI network inference for two main types of mass spectral data (AP-MS data and PCP-MS data),and designed effective methods respectively to solve the issue of current bottlenecks,achieving the construction of high-quality PPI network.The existing algorithms for PPI network interface from PCP-MS data are still in infancy,and there is a few of related algorithms.The existing method have several problem.Specifically:1)many error interaction is contained in the results produced by the different algorithms,and the correct interaction is omitted in the results.2)Different algorithms may produce very different results when they face the same data set.3)For different data sets,the performance variance of the same algorithm is larger.For the problem of PPI network inference for PCP-MS data,this paper proposed a PPI scoring method based on correlation analysis and rank aggregation.The method is based on unsupervised learning and includes two steps.Firstly,correlation coefficient between protein pairs is computed,and multiple results of PPI scores can be obtained.Secondly,multiple results for each pair of proteins are combininect 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.

Key words: Correlation analysis, MS data, PPI network, Protein direct interaction, Rank aggregation

CLC Number: 

  • TP391.41
[1]GUAN W,WANG J,HE F C.The advance in research methods for large-scale protein-protein interactions [J].Chinese Bulletin of Life Sciences,2006,18(5):507-512.(in Chinese)
[2]KIM M S,PINTO S M,GETNET D,et al.A draft map of the human proteome [J].Nature,2014,509(7502):575-581.
[3]WILHELM M,SCHLEGL J,HAHNE H,et al.Mass-spec- trometry-based draft of the human proteome [J].Nature,2014,509(7502):582-587.
[4]BAKER M.Proteomics:The interaction map [J].Nature,2012,484(7393):271-275.
[5]MIRZAEI H,CARRASCO M.Modern Proteomics-Sample Preparation,Analysis and Practical Applications[M].Springer International Publishing,2016.
[6]MEHTA V,TRINKLE-MULCAHY L.Recent advances in large-scale protein interactome mapping[J].F1000research,2016,5:782.
[7]FAN S B,WU Y J,YANG B,et al.A New Approach to Protein Structure and Interaction Research:Chemical Cross-linking in Combination With Mass Spectrometry [J].Progress in Bioche-mistry and Biophysics,2014,41(11):1109-1125.(in Chinese)
[8]HUTTLIN E L,TING L,BRUCKNER R J,et al.The BioPlex Network:A Systematic Exploration of the Human Interactome.[J].Cell,2015,162(2):425-440.
[9]HUTTLIN E L,BRUCKNER R J,PAULO J A,et al.Architecture of the human interactome defines protein communities and disease networks:[J].Nature,2017,545(7655):505-509.
[10]BEHRENDS C,SOWA M E,GYGI S P,et al.Network organization of the human autophagy system[J].Nature,2010,466(7302):68-76.
[11]JÄGER S,CIMERMANCIC P,GULBAHCE N,et al.Global landscape of HIV-human protein complexes [J].Nature,2012,481(7381):365-370.
[12]SOWA M E,BENNETT E J,GYGI S P,et al.Defining the human deubiquitinating enzyme interaction landscape [J].Cell,2009,138(2):389-403.
[13]GURUHARSHA K G,RUAL J F,ZHAI B,et al.A protein complex network of Drosophila melanogaster [J].Cell,2011,147(3):690-703.
[14]TENG B,ZHAO C,LIU X,et al.Network inference from AP-MS data:computational challenges and solutions [J].Briefings in Bioinformatics,2015,16(4):658-674.
[15]CHEN B,FAN W,LIU J,et al.Identifying protein complexes and functional modules—from static PPI networks to dynamic PPI networks [J].Briefings in Bioinformatics,2014,15(2):177-194.
[16]JI J,ZHANG A,LIU C,et al.Survey:Functional Module Detection from Protein-Protein Interaction Networks [J].IEEE Transactions on Knowledge & Data Engineering,2014,26(2):261-277.
[17]VARJOSALO M,SACCO R,STUKALOV A,et al.Interlaboratory reproducibility of large-scale human protein-complex analysis by standardized AP-MS [J].Nature Methods,2013,10(4):307-314.
[18]SHARAN R,ULITSKY I,SHAMIR R.Network-based prediction of protein function [J].Molecular Systems Biology,2007,3(1):88.
[19]BARABÁSI A L,GULBAHCE N,LOSCALZO J.Network medicine:a network-based approach to human disease [J].Nature Reviews Genetics,2011,12(1):56-68.
[20]TAYLOR I W,LINDING R,WARDE-FARLEY D,et al.Dynamic modularity in protein interaction networks predicts breast cancer outcome [J].Nature Biotechnology,2009,27(2):199-204.
[21]HE Z,YU W.Stable feature selection for biomarker discovery [J].Computational Biology and Chemistry,2010,34(4):215-225.
[22]NESVIZHSKII A I.Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments [J].Proteomics,2012,12(10):1639-1655.
[23]ARMEAN I M,LILLEY K S,TROTTER M W B.Popular computational methods to assess multiprotein complexes derived from label-free affinity purification and mass spectrometry (AP-MS) experiments [J].Molecular & Cellular Proteomics,2013,12(1):1-13.
[24]WAN C,BORGESON B,PHANSE S,et al.Panorama of ancient metazoan macromolecular complexes [J].Nature,2015,525(7569):339-344.
[25]HAVUGIMANA P C,HART G T,NEPUSZ T,et al.A census of human soluble protein complexes [J].Cell,2012,150(5):1068-1081.
[26]DE GELDER R,WEHRENS R,HAGEMAN J A.A generalized expression for the similarity of spectra:application to powder diffraction pattern classification [J].Journal of Computational Chemistry,2001,22(3):273-289 [27]TIAN B,DUAN Q,ZHAO C,et al.Reinforce:An Ensemble Approach for Inferring PPI Network from AP-MS Data [J].IEEE/ACM Transactions on Computational Biology and Bioinformatics,2017,PP(99):1-1.
[28]KOLDE R,LAUR S,ADLER P,et al.Robust rank aggregation for gene list integration and meta-analysis [J].Bioinformatics,2012,28(4):573-580.
[29]STOREY J D.A direct approach to false discovery rates [J]. Journal of the Royal Statistical Society,2002,64(3):479-498.
[30]RUEPP A,WAEGELE B,LECHNER M,et al.CORUM:the comprehensive resource of mammalian protein complexes—2009 [J].Nucleic Acids Research,2009,38(suppl_1):D497-D501.
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