Icosahedral viruses range in diameter from 10's to 100's of nanometers, their infectious genome encapsulated by a shell assembled by a poorly understood multiscale process, starting from an integer multiple (the T number) of 60 nearly identical viral capsid or coat protein (VP) monomers. We predict and experimentally validate inter-atomic hotspot interactions between VP monomers that are important for the assembly of 3 types of icosahedral viral capsids: Adeno Associated Virus serotype 2 (AAV2) and Minute Virus of Mice (MVM), both T=1 single stranded DNA viruses, and Bromo Mosaic Virus (BMV), a T=3 single stranded RNA virus. Experimental validation is by  site-directed mutagenesis data and biophysical assays . We combine  predictions at two scales: at the interface-scale, we predict the cruciality of an interaction for successful subassembly across each interface between VP monomers; and at the capsid-scale, we predict the cruciality of an interface for successful capsid assembly. At the interface-scale, we measure cruciality by approximating changes in the capsid free-energy landscape partition function when an interaction is removed. The partition function computation uses atlases of interface subassembly landscapes, rapidly generated by a novel geometric method and curated opensource software EASAL  . At the capsid-scale, combinatorial entropy is used to define cruciality of an interface for successful assembly of the capsid  . Our study goes all the way from resource-light, multiscale computational predictions of crucial hotspot inter-atomic interactions to experimental validation by biophysical assays of the effect of site-directed mutagenesis  on capsid assembly. By reliably and rapidly narrowing down target interactions,  our predictions can inform and reduce the time spent on more computationally intensive in-silico analyses, and time-consuming in-vitro and in-vivo experiments  .
Icosahedral viruses have their infectious genome encapsulated by a shell assembled by a multiscale process, starting from an integer multiple of 60 viral capsid or coat protein (VP) monomers. We predict and validate inter-atomic hotspot interactions between VP monomers that are important for the assembly of 3 icosahedral viral capsids: Adeno Associated Virus serotype 2 (AAV2) and Minute Virus of Mice (MVM), both T=1 single stranded DNA viruses, and Bromo Mosaic Virus (BMV), a T=3 single stranded RNA virus. Experimental validation is by in-vitro, site-directed mutagenesis data found in literature . We combine ab-initio predictions at two scales: at the interface-scale, we predict the importance (cruciality) of an interaction for successful subassembly across each interface between VP monomers; and at the capsid-scale, we predict the cruciality of an interface for successful capsid assembly. At the interface-scale, we measure cruciality by changes in the capsid free-energy landscape partition function when an interaction is removed. The partition function computation uses atlases of interface subassembly landscapes, rapidly generated by a novel geometric method and curated opensource software EASAL (efficient atlasing and search of assembly landscapes) . At the capsid-scale, cruciality of an interface for successful assembly of the capsid is based on combinatorial entropy . Our study goes from resource-light, multiscale computational predictions of crucial hotspot inter-atomic interactions to validation using data on site-directed mutagenesis ' effect on capsid assembly. By reliably and rapidly narrowing down target interactions, (no more than 1.5 hours per interface on a laptop with Intel Core i5-2500K 3.2Ghz CPU and 8GB of RAM) our predictions can inform and reduce time-consuming in-vitro and in-vivo experiments , or more computationally intensive in-silico analyses .
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range in diameter from 10's to 100's of nanometers,
have
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poorly understood
D
(the T number)
D
nearly identical
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experimentally
D
types of
A
in-vitro,
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and biophysical assays
found in literature
A
ab-initio
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cruciality
importance (cruciality)
D
approximating
A
(efficient atlasing and search of assembly landscapes)
D
combinatorial entropy is used to define
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is based on combinatorial entropy
D
all the way
R
experimental validation by biophysical assays of the effect of
validation using data on
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' effect
A
(no more than 1.5 hours per interface on a laptop with Intel Core i5-2500K 3.2Ghz CPU and 8GB of RAM)
D
the time spent on more computationally intensive in-silico analyses, and
A
, or more computationally intensive in-silico analyses