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: Finding coevolving amino acid residues using row and column weighting of mutual information and multi-dimensional amino acid representation. | Algorithms for Molecular Biology BioMed Central Research Finding coevolving amino acid residues using row and column weighting of mutual information and multi-dimensional amino acid representation Rodrigo Gouveia-Oliveira and Anders G Pedersen Address Center for Biological sequence analysis The Technical University of Denmark Building 208 2800 Lyngby Denmark Email Rodrigo Gouveia-Oliveira - rodrigo@ Anders G Pedersen - gorm@ Corresponding author Open Access Published 3 October 2007 Received 25 May 2007 Algorithms for Molecular Biology 2007 2 12 doi 1748-7188-2-12 Accepted 3 October 2007 This article is available from http content 2 1 12 2007 Gouveia-Oliveira and Pedersen 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 Some amino acid residues functionally interact with each other. This interaction will result in an evolutionary co-variation between these residues - coevolution. Our goal is to find these coevolving residues. Results We present six new methods for detecting coevolving residues. Among other things we suggest measures that are variants of Mutual Information and measures that use a multidimensional representation of each residue in order to capture the physico-chemical similarities between amino acids. We created a benchmarking system in silico able to evaluate these methods through a wide range of realistic conditions. Finally we use the combination of different methods as a way of improving performance. Conclusion Our best method Row and Column Weighed Mutual Information has an estimated accuracy increase of 63 over Mutual Information. Furthermore we show that the combination of different methods is efficient and that the methods are quite sensitive
