UCLA-DOE Laboratory of Structural Biology & Molecular Medicine, Molecular Biology Institute, UCLA, BOX 951570, Los Angeles, CA-90095-1570
With the advent of genome sequencing projects, the amino acid sequences of thousands of proteins are determined every year. Each of these protein sequences must be identified with its function and its three-dimensional structure for us to gain a full understanding of the molecular biology of organisms. To meet this challenge, new methods are being developed for fold recognition -- the computational assignment of newly determined amino acid sequences to three-dimensional protein structures. These methods start with a library of known three-dimensional target protein structures. The new probe sequence is then aligned to each target protein structure in the library and the compatibility of the sequence for that structure is scored. If a target structure is found to have a significantly high compatibility score, it is assumed that the probe sequence folds in much the same way as the target structure. The fundamental assumptions of this approach are that many different sequences fold in similar ways and that there is a relatively high probability that a new sequence possesses a previously observed fold.
We have recently demonstrated the increase in sensitivity and selectivity achieved in fold recognition by the use of sequence-derived properties such as multiple alignment information and predicted secondary structure. Current work focuses on "directional" profiles. These profiles determine the structural environment of each residue structure as a function of geometrical and energetic characteristics of the surrounding atoms. They are called directional, because rather than generating one pseudo-energy for all atoms within a sphere of a given radius, the sphere is partitioned into different "directions". We expect that the use of this more detailed structural signature will result in a more sensitive fold-recognition method.
The parameter tuning is carried out with the aid of a computational benchmark which consists of a representative set of fold-recognition problems. The method's performance is evaluated using an independent test set.