A Method for Extracting Spatially Close Peptide Segments in Proteins

抄録

In order to predict protein structures from their primary sequences, the understanding of long-range interactions is one of the most critical points. We are dealing with this problem by focusing on the pairs of peptide segments which are separated in the primary sequence but are close in the three-dimensional structure. The method is applied to a set of structure-resolved proteins to see if there are any significant features for association of local structures, such as secondary structure segments. The dataset consists of 88 nonhomologous proteins selected from the Brookhaven Protein Data Bank (PDB) using the superfamily classification of the Protein Information Resource (PIR). In the method, given the definition of the distance between two segments, spatially close segment-pairs are extracted for Ca segments of 4 or 7 residues long. The result shows that there are no preferred distances for association of two helical segments but there is a minimum of twenty intervening residues required for parallel helical segments.