Abstract:
Elucidation of conformational transitions between open/closed or free/bound states of proteins sheds light on the mechanism of their action. In this thesis, a new technique (ANM-MC) is proposed, in which collective modes obtained from anisotropic network model (ANM) are used in conjunction with a Monte Carlo (MC) simulation approach to investigate conformational transition pathways and pathway intermediates of proteins. ANM-MC is applied to Adenylate Kinase (AK), hemoglobin, Human Serum Transferrin (HSTR), and Lysine/Arginine/Ornithine (LAO) binding protein. Target conformations are reached by root mean-square deviation (RMSD) of 2.27, 1.90, 1.81, and 1.40 Å for AK, hemoglobin, HSTR, and LAO-binding protein cases respectively. Intermediate snapshots seem as plausible pathway intermediates when compared with related x-ray structures. Targeted Monte Carlo (TMC) approach, which is a forcing algorithm towards the target, is utilized without the use of collective modes. Both ANM-MC and TMC can explore the sequence of events with an efficient yet realistic conformational search. ANM-MC is further improved to be applicable to proteins with unknown target conformations. In this technique, called RG-ANM-MC, starting from open conformation, transitional path is generated by selecting lowest energy conformations obtained by normal modes with decreasing radius of gyration (RG). Application of the method on AK, HSTR, and LAObinding proteins reveal 3.18, 3.45, and 2.61 Å RMSD approach values to corresponding target states, respectively. RG-ANM-MC proves to be an efficient tool for proposing plausible closed states of proteins exhibiting hinge-like high amplitude collective motions. Conformational changes arising due to ligand binding are found to be intrinsic properties of binding protein, i.e. unliganded proteins possess a pre-existing fluctuation mechanism even in the absence of ligands. In both approaches (ANM-MC and RG-ANM-MC), lowest frequency modes are effective during transitions.
