Computational Methods for Protein Structure Prediction and Fold Recognition

Amino acid sequence analysis provides important insight into the structure of proteins, which in turn greatly facilitates the understanding of its biochem-ical and cellular function. Efforts to use computational methods in predicting protein structure based only on sequence information started 30 years ago (Nagano 1973; Chou and Fasman 1974). However, only during the last decade, has the introduction of new computational techniques such as protein fold recognition and the growth of sequence and structure databases due to mod-ern high-throughput technologies led to an increase in the success rate of pre-diction methods, so that they can be used by the molecular biologist or bio-chemist as an aid in the experimental investigations. 1.1 Database Searches Sequence similarity searching is a crucial step in analyzing newly determined (hereafter called “target”) protein sequences. Typically, large sequence data-bases such as the non-redundant (nr) database at the NCBI (synthesis of Gen-Bank, EMBL and DDBJ databases) or genome sequences are scanned for DNA or amino acid sequences that are similar to a target sequence. Alignments of the target sequence are constructed for each database entry, typically using dynamic programming algorithms (Needleman and Wunsch 1970; Smith and Waterman 1981), scores derived from these alignments are used to identify statistically significant matches. Matches which have a low probability of occurrence by chance are interpreted as likely to indicate homology, i.e. tha