AbstractsBiology & Animal Science

Protein structure modeling can be studied based on the knowledge of interactions or distances between pairs of atoms, which is so-called distance-based protein structure modeling and this field includes problems of structure determination and refinement as well as analysis of protein dynamics. The distances for certain pairs of atoms in a protein can often be obtained based on our knowledge on various types of bond-lengths and bond-angles or from physical experiments such as nuclear magnetic resonance (NMR). The coordinates of the atoms and hence the protein structure can then be determined by using the known distances. However, it requires the solution of a mathematical problem called the distance geometry problem, which has been proven to be computationally intractable in general. On the other hand, due to insufficient distance data such as nuclear overhauser effect (NOE) data in NMR, the protein structures determined by conventional techniques usually are not as accurate as desired. Therefore, the uses of such protein structures in important applications including homology modeling and rational drug design have been severely limited. In this work, we have developed several efficient algorithms including theories for the solution of the distance geometry problem using a geometric build-up algorithm. We also introduced a knowledge-based method for protein structure refinement, in which we constructed a dedicated structural database for protein inter-atomic distance distributions and derived so-called mean force potentials to refine NMR-determined protein structures. We have participated in CASPR competition regarding comparative models and reported some substantial improvement using mean force potentials. Finally, an efficient and simple method called Local-DME calculations has been developed to study protein dynamics of NMR ensembles specifically.