Abstract:
Elastic network models based on Normal Mode Analysis (NMA) are very useful in determining the conformational fluctuations that would affect the functions of proteins. Gaussian Network Model (GNM) is proved to be a very efficient method for this purpose. In recent years, coarse-grained elastic network models that use torsional degrees of freedom, have been introduced for aiming to predict the key residues that are responsible for the conformational changes of proteins. In the present thesis, a similar line of approach is applied to the simple one dimensional GNM in order to obtain the mean torsional fluctuations and the correlations between torsional fluctuations of residues by developing a novel method, Torsional Gaussian Network Model (tGNM). The predictions of this new approach is tested by comparing the fluctuations with the results obtained by Molecular Dynamics (MD) simulations for a set of proteins, ASC (Apoptosis-associated Speck-Like Protein Containing CARD), CypA (Cyclophilin A), and CAP (Catabolite Activator Protein). For further analysis of the model predictions, five pairs of protein structures with known open and closed conformations are studied for their conformational transitions. The mean square torsional fluctuations are calculated for the conformations of both states and compared with their torsional angle differences. Some of the peaks of the torsional angle differences are successfully observed in the mean square fluctuations; however, some additional peaks are observed in the model predictions. This suggests the latter fluctuations might be related with the conformations that could be observed on the transition pathway between the two states. The results in general provide that so called tGNM can be used for identifying residues that play an important role in the functional conformational transition of proteins.
