Efficient Enumeration and Visualization of Helix-coil Ensembles.

dc.contributor.author

Schmidler, Scott C

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Hughes, Roy Gene

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Oas, Terrence G

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Zhao, Shiwen

dc.date.accessioned

2023-11-01T13:49:28Z

dc.date.available

2023-11-01T13:49:28Z

dc.date.issued

2023-09-17

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2023-11-01T13:49:27Z

dc.description.abstract

Helix-coil models are routinely used to interpret CD data of helical peptides or predict the helicity of naturally-occurring and designed polypeptides. However, a helix-coil model contains significantly more information than mean helicity alone, as it defines the entire ensemble - the equilibrium population of every possible helix-coil configuration - for a given sequence. Many desirable quantities of this ensemble are either not obtained as ensemble averages, or are not available using standard helicity-averaging calculations. Enumeration of the entire ensemble can allow calculation of a wider set of ensemble properties, but the exponential size of the configuration space typically renders this intractable. We present an algorithm that efficiently approximates the helix-coil ensemble to arbitrary accuracy, by sequentially generating a list of the M highest populated configurations in descending order of population. Truncating this list of (configuration, population) pairs at a desired accuracy provides an approximating sub-ensemble. We demonstrate several uses of this approach for providing insight into helix-coil ensembles and folding mechanisms, including landscape visualization.

dc.identifier

2023.09.16.558052

dc.identifier.uri

https://hdl.handle.net/10161/29320

dc.language

eng

dc.relation.ispartof

bioRxiv

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10.1101/2023.09.16.558052

dc.title

Efficient Enumeration and Visualization of Helix-coil Ensembles.

dc.type

Journal article

duke.contributor.orcid

Schmidler, Scott C|0009-0006-3733-3716

duke.contributor.orcid

Oas, Terrence G|0000-0002-3067-2743

pubs.organisational-group

Duke

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School of Medicine

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Trinity College of Arts & Sciences

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Basic Science Departments

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Biochemistry

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Computer Science

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Statistical Science

pubs.publication-status

Published online

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