I present in this work an algorithm for deriving protein functional annotations which are position-specific. The input is based on the results of a sequence similarity search of the query sequence against a sequence database. Strings of words are extracted from the descriptions of the proteins, and the correlation between proteins having the same descriptors and the amino acid conservation is used to compute a score that indicates which descriptor is likely to describe better the function of each particular residue. Analysis of the score curves and comparison of different functions allows an easy detection of parts of the sequence associated to different function. Different levels of functional specificity can be compared, allowing to choose the one that suits better the function of the protein. Immediate applications of this algorithm are, support for (automated) methods of protein functional annotation, and database coherence check.

We describe the basic design of a system for automatic detection of protein-protein interactions extracted from scientific abstracts. By restricting the problem domain and imposing a number of strong assumptions which include pre-specified protein names and a limited set of verbs that represent actions, we show that it is possible to perform accurate information extraction. The performance of the system is evaluated with different cases of real-world interaction networks, including the Drosophila cell cycle control. The results obtained computationally are in good agreement with current biological knowledge and demonstrate the feasibility of developing a fully automated system able to describe networks of protein interactions with sufficient accuracy.