Mixture Models for Spherical Data with Applications to Protein Bioinformatics
Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
Finite mixture models are fitted to spherical data. Kent distributions are used for the components of the mixture because they allow considerable flexibility. Previous work on such mixtures has used an approximate maximum likelihood estimator for the parameters of a single component. However, the approximation causes problems when using the EM algorithm to estimate the parameters in a mixture model. Hence, the exact maximum likelihood estimator is used here for the individual components. This paper is motivated by a challenging prize problem in structural bioinformatics of how proteins fold. It is known that hydrogen bonds play a key role in the folding of a protein. We explore this hydrogen bond geometry using a data set describing bonds between two amino acids in proteins. An appropriate coordinate system to represent the hydrogen bond geometry is proposed, with each bond represented as a point on a sphere. We fit mixtures of Kent distributions to different subsets of the hydrogen bond data to gain insight into how the secondary structure elements bond together, since the distribution of hydrogen bonds depends on which secondary structure elements are involved.
Original language | English |
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Title of host publication | Directional Statistics for Innovative Applications : A Bicentennial Tribute to Florence Nightingale |
Publisher | Springer |
Publication date | 2022 |
Pages | 15-32 |
ISBN (Print) | 978-981-19-1043-2 |
ISBN (Electronic) | 978-981-19-1044-9 |
DOIs | |
Publication status | Published - 2022 |
Series | Forum for Interdisciplinary Mathematics |
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ISSN | 2364-6748 |
Bibliographical note
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ID: 314302529