TAILIEUCHUNG - Báo cáo sinh học: "Predicting direct protein interactions from affinity purification mass spectrometry data"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Predicting direct protein interactions from affinity purification mass spectrometry data. | Kim et al. Algorithms for Molecular Biology 2010 5 34 http content 5 1 34 AMR ALGORITHMS FOR MOLECULAR BIOLOGY RESEARCH Open Access Predicting direct protein interactions from affinity purification mass spectrometry data Ethan DH Kim 1 Ashish Sabharwal2 Adrian R Vetta3 Mathieu Blanchette1 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. Background Understanding the organization of protein-protein interactions

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