Literature DB >> 16392593

Foldamer dynamics expressed via Markov state models. II. State space decomposition.

Sidney P Elmer1, Sanghyun Park, Vijay S Pande.   

Abstract

The structural landscape of poly-phenylacetylene (pPA), otherwise known as m-phenylene ethynylene oligomers, has been shown to consist of a very diverse set of conformations, including helices, turns, and knots. Defining a state space decomposition to classify these conformations into easily identifiable states is an important step in understanding the dynamics in relation to Markov state models. We define the state decomposition of pPA oligomers in terms of the sequence of discretized dihedral angles between adjacent phenyl rings along the oligomer backbone. Furthermore, we derive in mathematical detail an approach to further reduce the number of states by grouping symmetrically equivalent states into a single parent state. A more challenging problem requires a formal definition for knotted states in the structural landscape. Assuming that the oligomer chain can only cross the ideal helix path once, we propose a technique to define a knotted state derived from a helical state determined by the position along the helical nucleus where the chain crosses the ideal helix path. Several examples of helical states and knotted states from the pPA 12-mer illustrate the principles outlined in this article.

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Year:  2005        PMID: 16392593     DOI: 10.1063/1.2008230

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  9 in total

1.  Building Markov state models along pathways to determine free energies and rates of transitions.

Authors:  Albert C Pan; Benoît Roux
Journal:  J Chem Phys       Date:  2008-08-14       Impact factor: 3.488

Review 2.  The protein folding problem.

Authors:  Ken A Dill; S Banu Ozkan; M Scott Shell; Thomas R Weikl
Journal:  Annu Rev Biophys       Date:  2008       Impact factor: 12.981

3.  Threading a peptide through a peptide: protein loops, rotaxanes, and knots.

Authors:  John W Blankenship; Philip E Dawson
Journal:  Protein Sci       Date:  2007-06-13       Impact factor: 6.725

4.  Fast geometric consensus approach for protein model quality assessment.

Authors:  Rafal Adamczak; Jaroslaw Pillardy; Brinda K Vallat; Jaroslaw Meller
Journal:  J Comput Biol       Date:  2011-01-18       Impact factor: 1.479

5.  Using Markov state models to study self-assembly.

Authors:  Matthew R Perkett; Michael F Hagan
Journal:  J Chem Phys       Date:  2014-06-07       Impact factor: 3.488

Review 6.  Everything you wanted to know about Markov State Models but were afraid to ask.

Authors:  Vijay S Pande; Kyle Beauchamp; Gregory R Bowman
Journal:  Methods       Date:  2010-06-04       Impact factor: 3.608

7.  Statistics of knots, geometry of conformations, and evolution of proteins.

Authors:  Rhonald C Lua; Alexander Y Grosberg
Journal:  PLoS Comput Biol       Date:  2006-05-19       Impact factor: 4.475

8.  UQlust: combining profile hashing with linear-time ranking for efficient clustering and analysis of big macromolecular data.

Authors:  Rafal Adamczak; Jarek Meller
Journal:  BMC Bioinformatics       Date:  2016-12-28       Impact factor: 3.169

9.  Building Markov state models with solvent dynamics.

Authors:  Chen Gu; Huang-Wei Chang; Lutz Maibaum; Vijay S Pande; Gunnar E Carlsson; Leonidas J Guibas
Journal:  BMC Bioinformatics       Date:  2013-01-21       Impact factor: 3.169

  9 in total

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