Knowing the assembly-crucial interaction of virus capsid help us understand its assembly process. Validating assembly-crucial interaction via mutagenesis experiments in wet lab is usually very slow. We predict the assembly crucial interactions by measuring the entropy change of the capsid assembly system before and after the removal of each interaction. The entropy-change is measured by analysis of the viral capsid configuration space on two levels. On the interface level the entropy of the small interface assembly system is approximated by the sampling of the configurational space using the recently reported geometric method called  EASAL (efficient atlasing and sampling of assembly landscapes) . On the capsid level the method statistically learns how to combine the crucialness of interactions for assembly of each interface (obtained using EASAL) with crucialness of each interface for assembly of capsid (obtained from capsid structure and symmetry) to predict crucialness of interactions for capsid assembly . The learning algorithm uses - for training - a fraction of the mutagenesis-based experimental dataabout assembly-crucialness of interactions. The remainder of the mutagenesis data is used to validate .
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 abbreviatedbar-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 .
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Knowing the assembly-crucial interaction of virus capsid help us understand its assembly process. Validating assembly-crucial interaction via mutagenesis experiments in wet lab is usually very slow. We predict the assembly crucial interactions by measuring the entropy change
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
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system before and after the removal of each interaction. The entropy-change is measured by analysis of the viral capsid configuration space on two levels. On the interface level the entropy of the small interface assembly system is approximated by the sampling of the configurational space using the recently reported geometric method called
landscape when an interaction is removed. At the interface-scale, the partition function computation uses
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atlases
A
of selected subassembly landscapes for each interface type and their abbreviated
A
bar-codes
A
. 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
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sampling
search
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. On the capsid level the method statistically learns how to combine the crucialness of interactions for assembly of each interface (obtained using EASAL) with crucialness of each interface for assembly of capsid (obtained from capsid structure and symmetry) to predict crucialness of interactions
, 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
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.
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.
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mutagenesis-based experimental dataabout assembly-crucialness of interactions. The
mutagenesis data, with the
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mutagenesis data is used to validate
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