Mardia, KV orcid.org/0000-0003-0090-6235, Sriram, K and Deane, CM (2018) A statistical model for helices with applications. Biometrics, 74 (3). pp. 845-854. ISSN 0006-341X
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018, The International Biometric Society. This is the peer reviewed version of the following article: Mardia, KV, Sriram, K and Deane, CM (2018) A statistical model for helices with applications. Biometrics. ISSN 0006-341X, which has been published in final form at https://doi.org/10.1111/biom.12870. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. |
Keywords: | Change point; Crowdsourced data; Helix fitting; Kink detection; Membrane protein; Protein structure |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Feb 2018 16:38 |
Last Modified: | 28 Sep 2020 12:21 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1111/biom.12870 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:126921 |