tailieunhanh - Báo cáo y học: "A first-draft human protein-interaction map"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học quốc tế cung cấp cho các bạn kiến thức về ngành y đề tài: A first-draft human protein-interaction map. | Research A first-draft human protein-interaction map Ben Lehner and Andrew G Fraser Address The Wellcome Trust Sanger Institute Hinxton Cambridge CB10 1SA UK. Correspondence AndrewGFraser. E-mail agf@ Open Access Published 13 August 2004 Genome Biology 2004 5 R63 The electronic version of this article is the complete one and can be found online at http 2004 5 9 R63 Received 7 May 2004 Revised 23 June 2004 Accepted 20 July 2004 2004 Lehner and Fraser licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background Protein-interaction maps are powerful tools for suggesting the cellular functions of genes. Although large-scale protein-interaction maps have been generated for several invertebrate species projects of a similar scale have not yet been described for any mammal. Because many physical interactions are conserved between species it should be possible to infer information about human protein interactions and hence protein function using model organism proteininteraction datasets. Results Here we describe a network of over 70 000 predicted physical interactions between around 6 200 human proteins generated using the data from lower eukaryotic protein-interaction maps. The physiological relevance of this network is supported by its ability to preferentially connect human proteins that share the same functional annotations and we show how the network can be used to successfully predict the functions of human proteins. We find that combining interaction datasets from a single organism but generated using independent assays and combining interaction datasets from two organisms but generated using the same assay are both very effective ways of further improving the accuracy of .