A statistical model for helices with applications
Kanti V. Mardia,
Karthik Sriram and
Charlotte M. Deane
Biometrics, 2018, vol. 74, issue 3, 845-854
Abstract:
Motivated by a cutting edge problem related to the shape of α‐helices in proteins, we formulate a parametric statistical model, which incorporates the cylindrical nature of the helix. Our focus is to detect a “kink,” which is a drastic change in the axial direction of the helix. We propose a statistical model for the straight α‐helix and derive the maximum likelihood estimation procedure. The cylinder is an accepted geometric model for α‐helices, but our statistical formulation, for the first time, quantifies the uncertainty in atom positions around the cylinder. We propose a change point technique “Kink‐Detector” to detect a kink location along the helix. Unlike classical change point problems, the change in direction of a helix depends on a simultaneous shift of multiple data points rather than a single data point, and is less straightforward. Our biological building block is crowdsourced data on straight and kinked helices; which has set a gold standard. We use this data to identify salient features to construct Kink‐detector, test its performance and gain some insights. We find the performance of Kink‐detector comparable to its computational competitor called “Kink‐Finder.” We highlight that identification of kinks by visual assessment can have limitations and Kink‐detector may help in such cases. Further, an analysis of crowdsourced curved α‐helices finds that Kink‐detector is also effective in detecting moderate changes in axial directions.
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://doi.org/10.1111/biom.12870
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:74:y:2018:i:3:p:845-854
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0006-341X
Access Statistics for this article
More articles in Biometrics from The International Biometric Society
Bibliographic data for series maintained by Wiley Content Delivery ().