Efficient Enumeration and Visualization of Helix-coil Ensembles.
dc.contributor.author | Schmidler, Scott C | |
dc.contributor.author | Hughes, Roy Gene | |
dc.contributor.author | Oas, Terrence G | |
dc.contributor.author | Zhao, Shiwen | |
dc.date.accessioned | 2023-11-01T13:49:28Z | |
dc.date.available | 2023-11-01T13:49:28Z | |
dc.date.issued | 2023-09-17 | |
dc.date.updated | 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 | ||
dc.language | eng | |
dc.relation.ispartof | bioRxiv | |
dc.relation.isversionof | 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 | |
pubs.organisational-group | School of Medicine | |
pubs.organisational-group | Trinity College of Arts & Sciences | |
pubs.organisational-group | Basic Science Departments | |
pubs.organisational-group | Biochemistry | |
pubs.organisational-group | Computer Science | |
pubs.organisational-group | Statistical Science | |
pubs.publication-status | Published online |