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Inferring physical protein contacts from large-scale purification data of protein complexes.

Schelhorn SE, Mestre J, Albrecht M, Zotenko E - Mol. Cell Proteomics (2011)

Bottom Line: Our results show that raw purification data can indeed be exploited to determine high-confidence physical protein contacts within protein complexes.In contrast to previous findings, we observe that physical contacts inferred from purification experiments of protein complexes can be qualitatively comparable to binary protein interactions measured by experimental high-throughput assays such as yeast two-hybrid.This suggests that computationally derived physical contacts might complement binary protein interaction assays and guide large-scale interactome mapping projects by prioritizing putative physical contacts for further experimental screens.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute for Informatics, Saarbrücken, Germany. sven@mpi-inf.mpg.de

ABSTRACT
Recent large-scale data sets of protein complex purifications have provided unprecedented insights into the organization of cellular protein complexes. Several computational methods have been developed to detect co-complexed proteins in these data sets. Their common aim is the identification of biologically relevant protein complexes. However, much less is known about the network of direct physical protein contacts within the detected protein complexes. Therefore, our work investigates whether direct physical contacts can be computationally derived by combining raw data of large-scale protein complex purifications. We assess four established scoring schemes and introduce a new scoring approach that is specifically devised to infer direct physical protein contacts from protein complex purifications. The physical contacts identified by the five methods are comprehensively benchmarked against different reference sets that provide evidence for true physical contacts. Our results show that raw purification data can indeed be exploited to determine high-confidence physical protein contacts within protein complexes. In particular, our new method outperforms competing approaches at discovering physical contacts involving proteins that have been screened multiple times in purification experiments. It also excels in the analysis of recent protein purification screens of molecular chaperones and protein kinases. In contrast to previous findings, we observe that physical contacts inferred from purification experiments of protein complexes can be qualitatively comparable to binary protein interactions measured by experimental high-throughput assays such as yeast two-hybrid. This suggests that computationally derived physical contacts might complement binary protein interaction assays and guide large-scale interactome mapping projects by prioritizing putative physical contacts for further experimental screens.

Show MeSH
Top-ranking physical contacts inferred by the ISA method and their relation to physical contacts inferred by other methods. A, A network induced by the 3000 protein interactions having the top ISA score ranks. B, Similarity of the inferred physical contacts generated by the five scoring methods. Nodes represent different scoring schemes. Edges are labeled with the Salama-Quade correlation coefficient, which measures agreement in the ranking of inferred protein contacts induced by the scores of the corresponding methods.
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Figure 1: Top-ranking physical contacts inferred by the ISA method and their relation to physical contacts inferred by other methods. A, A network induced by the 3000 protein interactions having the top ISA score ranks. B, Similarity of the inferred physical contacts generated by the five scoring methods. Nodes represent different scoring schemes. Edges are labeled with the Salama-Quade correlation coefficient, which measures agreement in the ranking of inferred protein contacts induced by the scores of the corresponding methods.

Mentions: Let {1, …, m} be a set of elements, R1(i) be the rank of element i under the first method, and R2(i) be the rank of element i under the second method. The Salama-Quade coefficient measures the agreement between rankings R1 and R2 and is given by Salama-Quade(R1,R2) = Σk=1KTk/k, where Tk is the number of elements having rank less or equal to k under both R1 and R2. For similarity values in Fig. 1B we used K = 10,000 and normalization Salama-Quade(R1,R2)/K.


Inferring physical protein contacts from large-scale purification data of protein complexes.

Schelhorn SE, Mestre J, Albrecht M, Zotenko E - Mol. Cell Proteomics (2011)

Top-ranking physical contacts inferred by the ISA method and their relation to physical contacts inferred by other methods. A, A network induced by the 3000 protein interactions having the top ISA score ranks. B, Similarity of the inferred physical contacts generated by the five scoring methods. Nodes represent different scoring schemes. Edges are labeled with the Salama-Quade correlation coefficient, which measures agreement in the ranking of inferred protein contacts induced by the scores of the corresponding methods.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3108834&req=5

Figure 1: Top-ranking physical contacts inferred by the ISA method and their relation to physical contacts inferred by other methods. A, A network induced by the 3000 protein interactions having the top ISA score ranks. B, Similarity of the inferred physical contacts generated by the five scoring methods. Nodes represent different scoring schemes. Edges are labeled with the Salama-Quade correlation coefficient, which measures agreement in the ranking of inferred protein contacts induced by the scores of the corresponding methods.
Mentions: Let {1, …, m} be a set of elements, R1(i) be the rank of element i under the first method, and R2(i) be the rank of element i under the second method. The Salama-Quade coefficient measures the agreement between rankings R1 and R2 and is given by Salama-Quade(R1,R2) = Σk=1KTk/k, where Tk is the number of elements having rank less or equal to k under both R1 and R2. For similarity values in Fig. 1B we used K = 10,000 and normalization Salama-Quade(R1,R2)/K.

Bottom Line: Our results show that raw purification data can indeed be exploited to determine high-confidence physical protein contacts within protein complexes.In contrast to previous findings, we observe that physical contacts inferred from purification experiments of protein complexes can be qualitatively comparable to binary protein interactions measured by experimental high-throughput assays such as yeast two-hybrid.This suggests that computationally derived physical contacts might complement binary protein interaction assays and guide large-scale interactome mapping projects by prioritizing putative physical contacts for further experimental screens.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute for Informatics, Saarbrücken, Germany. sven@mpi-inf.mpg.de

ABSTRACT
Recent large-scale data sets of protein complex purifications have provided unprecedented insights into the organization of cellular protein complexes. Several computational methods have been developed to detect co-complexed proteins in these data sets. Their common aim is the identification of biologically relevant protein complexes. However, much less is known about the network of direct physical protein contacts within the detected protein complexes. Therefore, our work investigates whether direct physical contacts can be computationally derived by combining raw data of large-scale protein complex purifications. We assess four established scoring schemes and introduce a new scoring approach that is specifically devised to infer direct physical protein contacts from protein complex purifications. The physical contacts identified by the five methods are comprehensively benchmarked against different reference sets that provide evidence for true physical contacts. Our results show that raw purification data can indeed be exploited to determine high-confidence physical protein contacts within protein complexes. In particular, our new method outperforms competing approaches at discovering physical contacts involving proteins that have been screened multiple times in purification experiments. It also excels in the analysis of recent protein purification screens of molecular chaperones and protein kinases. In contrast to previous findings, we observe that physical contacts inferred from purification experiments of protein complexes can be qualitatively comparable to binary protein interactions measured by experimental high-throughput assays such as yeast two-hybrid. This suggests that computationally derived physical contacts might complement binary protein interaction assays and guide large-scale interactome mapping projects by prioritizing putative physical contacts for further experimental screens.

Show MeSH