TY - JOUR
T1 - Probabilistic assembly of human protein interaction networks from label-free quantitative proteomics
AU - Sardiu, Mihaela E.
AU - Cai, Yong
AU - Jin, Jingji
AU - Swanson, Selene K.
AU - Conaway, Ronald C.
AU - Conaway, Joan W.
AU - Florens, Laurence
AU - Washburn, Michael P.
PY - 2008/2/5
Y1 - 2008/2/5
N2 - Large-scale affinity purification and mass spectrometry studies have played important roles in the assembly and analysis of comprehensive protein interaction networks for lower eukaryotes. However, the development of such networks for human proteins has been slowed by the high cost and significant technical challenges associated with systematic studies of protein interactions. To address this challenge, we have developed a method for building local and focused networks. This approach couples vector algebra and statistical methods with normalized spectral counting (NSAF) derived from the analysis of affinity purifications via chromatography-based proteomics. After mathematical removal of contaminant proteins, the core components of multiprotein complexes are determined by singular value decomposition analysis and clustering. The probability of interactions within and between complexes is computed solely based upon NSAFs using Bayes' approach. To demonstrate the application of this method to small-scale datasets, we analyzed an expanded human TIP49a and TIP49b dataset. This dataset contained proteins affinity-purified with 27 different epitope-tagged components of the chromatin remodeling SRCAP, hINO80, and TRRAP/TIP60 complexes, and the nutrient sensing complex Uri/Prefoldin. Within a core network of 65 unique proteins, we captured all known components of these complexes and novel protein associations, especially in the Uri/ Prefoldin complex. Finally, we constructed a probabilistic human interaction network composed of 557 protein pairs.
AB - Large-scale affinity purification and mass spectrometry studies have played important roles in the assembly and analysis of comprehensive protein interaction networks for lower eukaryotes. However, the development of such networks for human proteins has been slowed by the high cost and significant technical challenges associated with systematic studies of protein interactions. To address this challenge, we have developed a method for building local and focused networks. This approach couples vector algebra and statistical methods with normalized spectral counting (NSAF) derived from the analysis of affinity purifications via chromatography-based proteomics. After mathematical removal of contaminant proteins, the core components of multiprotein complexes are determined by singular value decomposition analysis and clustering. The probability of interactions within and between complexes is computed solely based upon NSAFs using Bayes' approach. To demonstrate the application of this method to small-scale datasets, we analyzed an expanded human TIP49a and TIP49b dataset. This dataset contained proteins affinity-purified with 27 different epitope-tagged components of the chromatin remodeling SRCAP, hINO80, and TRRAP/TIP60 complexes, and the nutrient sensing complex Uri/Prefoldin. Within a core network of 65 unique proteins, we captured all known components of these complexes and novel protein associations, especially in the Uri/ Prefoldin complex. Finally, we constructed a probabilistic human interaction network composed of 557 protein pairs.
KW - Chromatin remodeling
KW - Multidimensional protein identification technology
KW - Normalized spectral abundance factor
UR - http://www.scopus.com/inward/record.url?scp=40349092949&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=40349092949&partnerID=8YFLogxK
U2 - 10.1073/pnas.0706983105
DO - 10.1073/pnas.0706983105
M3 - Article
C2 - 18218781
AN - SCOPUS:40349092949
SN - 0027-8424
VL - 105
SP - 1454
EP - 1459
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 5
ER -