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- Martyna, Agnieszka, et al.
(author)
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Curvature Sensing by a Viral Scission Protein
- 2016
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In: Biochemistry. - : American Chemical Society (ACS). - 0006-2960 .- 1520-4995. ; 55:25, s. 3493-3496
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Journal article (peer-reviewed)abstract
- Membrane scission is the final step in all budding processes wherein a membrane neck is sufficiently constricted so as to allow for fission and the release of the budded particle. For influenza viruses, membrane scission is mediated by an amphipathic helix (AH) domain in the viral M2 protein. While it is known that the M2AH alters membrane curvature, it is not known how the protein is localized to the center neck of budding virions where it would be able to cause membrane scission. Here, we use molecular dynamics simulations on buckled lipid bilayers to show that the M2AH senses membrane curvature and preferentially localizes to regions of high membrane curvature, comparable to that seen at the center neck of budding influenza viruses. These results were then validated using in vitro binding assays to show that the M2AH senses membrane curvature by detecting lipid packing defects in the membrane. Our results show that the M2AH senses membrane curvature and suggest that the AH domain may localize the protein at the viral neck where it can then mediate membrane scission and the release of budding viruses.
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| 2. |
- Martyna, Agnieszka, et al.
(author)
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Likelihood ratio-based probabilistic classifier
- 2023
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In: Chemometrics and Intelligent Laboratory Systems. - : ELSEVIER. - 0169-7439 .- 1873-3239. ; 240
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Journal article (peer-reviewed)abstract
- Modern classification methods are likely to misclassify samples with rare but class-specific data that are more similar (less distant) to the data of another than the original class. This is because they tend to focus on the majority of data, leaving the information provided by the rare data practically ignored. Nevertheless, it is an invaluable source of information that should support classification of samples with such data, despite their low frequency. Current solutions considering the rarity information involve likelihood ratio models (LR). We intend to modify the existing LR models to establish the class membership for the analysed samples by comparing them with the samples of known class label. If two compared samples show similarities of rare but class-specific features it makes the analysed sample much more likely to be a member of this class than any other class, even when its features are less distant to the features of most samples from other classes. The fundamental advantage of the developed methodology is inclusion of information about rare, class-specific features, which is neglected by ordinary classifiers. Converting LR values into probabilities with which a sample belongs to the classes under consideration, generates a powerful tool within the concept of probabilistic classification.
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