|Title||Prediction of Ras-effector interactions using position energy matrices.|
|Publication Type||Journal Article|
|Year of Publication||2007|
|Authors||Kiel C, Serrano L|
|Date Published||2007 Sep 1|
|Keywords||Algorithms, Amino Acid Sequence, Binding Sites, Computer Simulation, Energy Transfer, Models, Chemical, Models, Molecular, Molecular Sequence Data, Protein Binding, Protein Interaction Mapping, ras Proteins, Sequence Alignment, Sequence Analysis, Protein|
MOTIVATION: One of the more challenging problems in biology is to determine the cellular protein interaction network. Progress has been made to predict protein-protein interactions based on structural information, assuming that structural similar proteins interact in a similar way. In a previous publication, we have determined a genome-wide Ras-effector interaction network based on homology models, with a high accuracy of predicting binding and non-binding domains. However, for a prediction on a genome-wide scale, homology modelling is a time-consuming process. Therefore, we here successfully developed a faster method using position energy matrices, where based on different Ras-effector X-ray template structures, all amino acids in the effector binding domain are sequentially mutated to all other amino acid residues and the effect on binding energy is calculated. Those pre-calculated matrices can then be used to score for binding any Ras or effector sequences.
RESULTS: Based on position energy matrices, the sequences of putative Ras-binding domains can be scanned quickly to calculate an energy sum value. By calibrating energy sum values using quantitative experimental binding data, thresholds can be defined and thus non-binding domains can be excluded quickly. Sequences which have energy sum values above this threshold are considered to be potential binding domains, and could be further analysed using homology modelling. This prediction method could be applied to other protein families sharing conserved interaction types, in order to determine in a fast way large scale cellular protein interaction networks. Thus, it could have an important impact on future in silico structural genomics approaches, in particular with regard to increasing structural proteomics efforts, aiming to determine all possible domain folds and interaction types.
AVAILABILITY: All matrices are deposited in the ADAN database (http://adan-embl.ibmc.umh.es/).
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.