We predict crucial inter-atomic interactions between monomers for the autonomous assembly of three icosahedral viral capsids , Adeno Associated Virus type 2 (AAV2) , Murine Parvo Virus (MVM),  and Bromo Mosaic Virus (BMV), by approximating changes in the partition function of the capsid assembly landscape when an interaction is removed. At the interface-scale, the partition function computation usesatlases of selected subassembly landscapes for each interface type and their abbreviated\emph{bar-codes . The first (direct) prediction of cruciality of an interaction for successful capsid assembly assumed the interface assembly is essential for capsid assembly. The interface-scale prediction was blind to the mutagenesis data used for validation. The atlases are generated by the geometric method and curated opensource software EASAL (efficient atlasing and search of assembly landscapes), whose input consists of (a) the monomer geometry - atom coordinates; and for each interface type, (b) pair potentials for a candidate set of interactions along with Van der Waals sterics, and (c) structure of selected, small subassemblies. The method comes with rigorous accuracy-efficiency trade-off estimates. A second two-scale measure of cruciality of interactions and residues for capsid assembly is obtained as follows. Combinatorial entropy is used to define the cruciality of an interface for successful assembly of the capsid. This is combined with the interface-scale prediction using statistical learning. The learning algorithm uses - for training - a fraction of the mutagenesis data, with the remainder of the data being used for validation . By reliably and rapidly narrowing down target interactions, these in-silico predictions can inform and reduce the time spent on  time-consuming in-vitro and in-vivo experimentation .
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, \emph{ 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 .
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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
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This is combined with the interface-scale prediction using statistical learning. The learning algorithm uses - for training - a fraction of the mutagenesis data, with the remainder of the data being used for validation
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