Structural classifications aid the interpretation of proteins by providing a detailed and comprehensive description of the relationships of all known proteins structures. Researches have recently revealed strikingly skewed distributions of protein structures at all levels, only a small number of folds are far more common than others, and just a few superfamilies are known to have diverged widely. Understanding of structural homology helps to investigate the relationship between sequence and structure and finally solve the problem of protein structure prediction. We are investigating the strategies for protein classifications based on structural similarity within the protease superfamily. Many members of the protease superfamily are involved in significant biological pathways and disease developments, therfore to be considered as promising targets for therapeutic drug design. We investigate the structural similarities among the superfamily members whose structures are available in the PDB database. One strategy is based on distance geometry of the active sites. Through calculation of distances between points, we are able to get conformational information and develop algorithms to classify according to this information. Some other strategies may also be developed to apply to the structural classification. For example, the spacial arrangement of recognition pockets. Sequence similarity is also a way to describe the structural evolution of the superfamily. This research project is significantly related to structure-based drug design. We are expecting to develop an useful algorithm to derive structural similarities.
NIH ExPorter
NIH ExPorter