Understanding protein structure and dynamics: from comparative modeling point of view to dynamical perspectives

Abstract

In this thesis, we have advanced a set of distinct bioinformatic and computational tools to address the structure and function of proteins. Using data mining of the protein data bank (PDB), we have collected statistics connecting the propensity between the protein sequence and the secondary structure. This new tool has enabled us to evaluate new structures as well as a family of structures. A comparison of the wild type staphylococcal nuclease to various mutants using the proposed tool has indicated long-range conformational deviations spatially distant from the mutation point. The energetics of protein unfolding has been studied in terms of the forces observed in molecular dynamics simulations. An adaptive integration of the steered molecular dynamics is proposed to reduce ground state dominance by the rare low energy trajectories on the estimated free energy profile. The proposed adaptive algorithm is utilized to reproduce the potential of mean force of the stretching of decaalanine in vacuum at lower computational cost. It is then used to construct the potential of mean force of this transition in solvent for the first time as to observe the hydration effect on the helix-coil transformation. Adaptive steered molecular dynamics is also implemented to obtain the free energy change during the unfolding of neuropeptide Y and to confirm that the monomeric form of neuropeptide Y adopts halical-hairpin like pancreatic-polypeptide fold.