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

dc.contributor.advisor Hernandez, Rigoberto
dc.contributor.author Ozer, Gungor en_US
dc.contributor.committeeMember Sherrill, C. David
dc.contributor.committeeMember Brédas, Jean-Luc
dc.contributor.committeeMember Joseph Perry
dc.contributor.committeeMember Stephen Harvey
dc.contributor.department Chemistry and Biochemistry en_US
dc.date.accessioned 2011-07-06T16:47:50Z
dc.date.available 2011-07-06T16:47:50Z
dc.date.issued 2011-04-04 en_US
dc.description.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. en_US
dc.description.degree Ph.D. en_US
dc.identifier.uri http://hdl.handle.net/1853/39577
dc.publisher Georgia Institute of Technology en_US
dc.subject Adaptive steered molecular dynamics en_US
dc.subject Neuropeptide en_US
dc.subject.lcsh Proteins Analysis
dc.subject.lcsh Proteins Structure
dc.subject.lcsh Algorithms
dc.subject.lcsh Proteins Denaturation
dc.subject.lcsh Molecular dynamics
dc.title Understanding protein structure and dynamics: from comparative modeling point of view to dynamical perspectives en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.corporatename School of Chemistry and Biochemistry
local.contributor.corporatename College of Sciences
relation.isOrgUnitOfPublication f1725b93-3ab8-4c47-a4c3-3596c03d6f1e
relation.isOrgUnitOfPublication 85042be6-2d68-4e07-b384-e1f908fae48a
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