Abstract: This method enables computational analysis and manipulation of DNA and protein sequence data such as is found in large public databases. The method allows systematic searches of such data to identify portions of sequences which code for key intermolecular surfaces or regions of specific protein targets. In a first example, two amino acid sequences are input (steps 1, 2) to an iterative procedure (steps 4-6). A frame size is selected in terms of a number of sequence elements. The procedure then compares pairs of frames, one from each sequence, to identify intramolecular and intermolecular regions on the basis of relationships between amino acids according to a predetermined coding scheme. The probability of existence of each region within the coding scheme is then evaluated and those regions for which the probability is greater than a predetermined threshold are discarded. The procedure outputs the remaining regions.

Abstract: This invention relates to the prediction of protein sequence interactions. A method and apparatus are described for discovering rules of protein sequence interactions using a machine learning approach. The system uses a database of known protein sequence interactions, an algorithm to asses predictive quality of rules on one or more subsets of a protein sequence interaction database and a technique for generating new rule sets for testing. The system aims to optimise a rule set (descriptors of protein sequence interactions) against one or more predetermined criteria. One or more pairs of protein sequences which are likely to interact are generated according to the rule sets thus generated.