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Mechanical and Assembly Units of Viral Capsids Identified via Quasi-Rigid Domain Decomposition

Overview of attention for article published in PLoS Computational Biology, November 2013
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Title
Mechanical and Assembly Units of Viral Capsids Identified via Quasi-Rigid Domain Decomposition
Published in
PLoS Computational Biology, November 2013
DOI 10.1371/journal.pcbi.1003331
Pubmed ID
Authors

Guido Polles, Giuliana Indelicato, Raffaello Potestio, Paolo Cermelli, Reidun Twarock, Cristian Micheletti

Abstract

Key steps in a viral life-cycle, such as self-assembly of a protective protein container or in some cases also subsequent maturation events, are governed by the interplay of physico-chemical mechanisms involving various spatial and temporal scales. These salient aspects of a viral life cycle are hence well described and rationalised from a mesoscopic perspective. Accordingly, various experimental and computational efforts have been directed towards identifying the fundamental building blocks that are instrumental for the mechanical response, or constitute the assembly units, of a few specific viral shells. Motivated by these earlier studies we introduce and apply a general and efficient computational scheme for identifying the stable domains of a given viral capsid. The method is based on elastic network models and quasi-rigid domain decomposition. It is first applied to a heterogeneous set of well-characterized viruses (CCMV, MS2, STNV, STMV) for which the known mechanical or assembly domains are correctly identified. The validated method is next applied to other viral particles such as L-A, Pariacoto and polyoma viruses, whose fundamental functional domains are still unknown or debated and for which we formulate verifiable predictions. The numerical code implementing the domain decomposition strategy is made freely available.

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Geographical breakdown

Country Count As %
United States 2 5%
Netherlands 1 2%
Norway 1 2%
Brazil 1 2%
Unknown 39 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 27%
Researcher 11 25%
Student > Master 8 18%
Professor 4 9%
Student > Doctoral Student 2 5%
Other 3 7%
Unknown 4 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 27%
Biochemistry, Genetics and Molecular Biology 9 20%
Physics and Astronomy 9 20%
Chemistry 5 11%
Computer Science 1 2%
Other 3 7%
Unknown 5 11%