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Predicting direct protein interactions from affinity purification mass spectrometry data.

Kim ED, Sabharwal A, Vetta AR, Blanchette M - Algorithms Mol Biol (2010)

Bottom Line: We address this challenging graph theoretical problem by first characterizing signatures that can identify weakly connected nodes as well as dense regions of the network.As the sensitivity of AP-MS pipeline improves, the fraction of indirect interactions detected will also increase, thereby making the ability to distinguish them even more desirable.Despite the simplicity of our model for indirect interactions, our method provides a good performance on the test networks.

View Article: PubMed Central - HTML - PubMed

Affiliation: McGill Centre for Bioinformatics, McGill University, Quebec, Canada. blanchem@mcb.mcgill.ca.

ABSTRACT

Background: Affinity purification followed by mass spectrometry identification (AP-MS) is an increasingly popular approach to observe protein-protein interactions (PPI) in vivo. One drawback of AP-MS, however, is that it is prone to detecting indirect interactions mixed with direct physical interactions. Therefore, the ability to distinguish direct interactions from indirect ones is of much interest.

Results: We first propose a simple probabilistic model for the interactions captured by AP-MS experiments, under which the problem of separating direct interactions from indirect ones is formulated. Then, given idealized quantitative AP-MS data, we study the problem of identifying the most likely set of direct interactions that produced the observed data. We address this challenging graph theoretical problem by first characterizing signatures that can identify weakly connected nodes as well as dense regions of the network. The rest of the direct PPI network is then inferred using a genetic algorithm.Our algorithm shows good performance on both simulated and biological networks with very high sensitivity and specificity. Then the algorithm is used to predict direct interactions from a set of AP-MS PPI data from yeast, and its performance is measured against a high-quality interaction dataset.

Conclusions: As the sensitivity of AP-MS pipeline improves, the fraction of indirect interactions detected will also increase, thereby making the ability to distinguish them even more desirable. Despite the simplicity of our model for indirect interactions, our method provides a good performance on the test networks.

No MeSH data available.


Inferring direct interactions from actual AP-MS dataset. Overlap between the Y2 H interaction network of Yu et al. and various AP-MS-based networks: (a) High-confidence set of interactions from Krogan et al. (b) Set of 164 highest scoring interactions from Krogan et al. (c) Set of 164 interactions predicted as direct interactions by our algorithms, based on the AP-MS data from Krogan et al.
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Figure 3: Inferring direct interactions from actual AP-MS dataset. Overlap between the Y2 H interaction network of Yu et al. and various AP-MS-based networks: (a) High-confidence set of interactions from Krogan et al. (b) Set of 164 highest scoring interactions from Krogan et al. (c) Set of 164 interactions predicted as direct interactions by our algorithms, based on the AP-MS data from Krogan et al.

Mentions: As shown in Figure 3, our results show that the high-confidence AP-MS data GKroganHigh exhibited very little overlap with the direct binary interaction set GYu. 72.6% of interactions in GKroganHigh is disjoint from GY u, and 25% of GYu remains undetected by GKroganHigh. Furthermore, even the top scoring set of interactions showed high discrepancy ratios against GYu. In contrast, GKim produced by our algorithm coincide with GYu with better sensitivity and specificity. Given the crudeness of the method in translating the AP-MS data into a connectivity matrix, our algorithm has thus performed relatively well in predicting direct interactions from real AP-MS data.


Predicting direct protein interactions from affinity purification mass spectrometry data.

Kim ED, Sabharwal A, Vetta AR, Blanchette M - Algorithms Mol Biol (2010)

Inferring direct interactions from actual AP-MS dataset. Overlap between the Y2 H interaction network of Yu et al. and various AP-MS-based networks: (a) High-confidence set of interactions from Krogan et al. (b) Set of 164 highest scoring interactions from Krogan et al. (c) Set of 164 interactions predicted as direct interactions by our algorithms, based on the AP-MS data from Krogan et al.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2991326&req=5

Figure 3: Inferring direct interactions from actual AP-MS dataset. Overlap between the Y2 H interaction network of Yu et al. and various AP-MS-based networks: (a) High-confidence set of interactions from Krogan et al. (b) Set of 164 highest scoring interactions from Krogan et al. (c) Set of 164 interactions predicted as direct interactions by our algorithms, based on the AP-MS data from Krogan et al.
Mentions: As shown in Figure 3, our results show that the high-confidence AP-MS data GKroganHigh exhibited very little overlap with the direct binary interaction set GYu. 72.6% of interactions in GKroganHigh is disjoint from GY u, and 25% of GYu remains undetected by GKroganHigh. Furthermore, even the top scoring set of interactions showed high discrepancy ratios against GYu. In contrast, GKim produced by our algorithm coincide with GYu with better sensitivity and specificity. Given the crudeness of the method in translating the AP-MS data into a connectivity matrix, our algorithm has thus performed relatively well in predicting direct interactions from real AP-MS data.

Bottom Line: We address this challenging graph theoretical problem by first characterizing signatures that can identify weakly connected nodes as well as dense regions of the network.As the sensitivity of AP-MS pipeline improves, the fraction of indirect interactions detected will also increase, thereby making the ability to distinguish them even more desirable.Despite the simplicity of our model for indirect interactions, our method provides a good performance on the test networks.

View Article: PubMed Central - HTML - PubMed

Affiliation: McGill Centre for Bioinformatics, McGill University, Quebec, Canada. blanchem@mcb.mcgill.ca.

ABSTRACT

Background: Affinity purification followed by mass spectrometry identification (AP-MS) is an increasingly popular approach to observe protein-protein interactions (PPI) in vivo. One drawback of AP-MS, however, is that it is prone to detecting indirect interactions mixed with direct physical interactions. Therefore, the ability to distinguish direct interactions from indirect ones is of much interest.

Results: We first propose a simple probabilistic model for the interactions captured by AP-MS experiments, under which the problem of separating direct interactions from indirect ones is formulated. Then, given idealized quantitative AP-MS data, we study the problem of identifying the most likely set of direct interactions that produced the observed data. We address this challenging graph theoretical problem by first characterizing signatures that can identify weakly connected nodes as well as dense regions of the network. The rest of the direct PPI network is then inferred using a genetic algorithm.Our algorithm shows good performance on both simulated and biological networks with very high sensitivity and specificity. Then the algorithm is used to predict direct interactions from a set of AP-MS PPI data from yeast, and its performance is measured against a high-quality interaction dataset.

Conclusions: As the sensitivity of AP-MS pipeline improves, the fraction of indirect interactions detected will also increase, thereby making the ability to distinguish them even more desirable. Despite the simplicity of our model for indirect interactions, our method provides a good performance on the test networks.

No MeSH data available.