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Spectral approaches for identifying kinetic features in molecular dynamics simulations of globular proteins

Savol, Andrej J (2015) Spectral approaches for identifying kinetic features in molecular dynamics simulations of globular proteins. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Proteins live in an environment of random thermal vibrations yet they convert this constant disorder into selective biological function. As data acquisition methods for resolving protein motions improve more of the randomness is also captured; there is thus a parallel need for analysis methods that filter out the disorder and clarify functionally-relevant protein behavior. Few behaviors are more relevant than folding in the first place, and this thesis opens by addressing which conformational states are kinetically relevant for promoting or inhibiting attainment of the folded native state. Our modeling approach discretizes simulation data into a network of nodes and edges representing, respectively, different protein conformations and observed conformational transitions. A perturbative strategy is then invoked to quantify the importance of each node, i.e. conformational substate, with regard to theoretical folding rates. On a test of 10 proteins this framework identifies unique ‘kinetic traps’ and ‘facilitator substates’ that sometimes evade detection with traditional RMSD-based analysis. We then apply spectral approaches and auto-regressive models to (1) address efficiency concerns for more general networks and (2) mimic protein flexibility with compact linear models.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Savol, Andrej Jajs171@pitt.eduAJS171
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorChennubhotla, Chakrachakracs@pitt.eduCHAKRACS
Committee MemberZuckerman, Danddmmzz@pitt.eduDDMMZZ
Committee MemberKingsford,
Committee MemberKoes, Daviddkoes@pitt.eduDKOES
Committee MemberStanley,
Date: 4 May 2015
Date Type: Publication
Defense Date: 16 April 2015
Approval Date: 4 May 2015
Submission Date: 2 May 2015
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 106
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Computational Biology
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Protein dynamics. Complex networks. Network centrality. Protein folding. Markov state models. Spectral theory. Matrix perturbation. Mean first passage times.
Date Deposited: 04 May 2015 11:46
Last Modified: 15 Nov 2016 14:28


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