Neural-symbolic hybrid model for myosin complex in cardiac ventriculum decodes structural bases for inheritable heart disease from its genetic encoding.

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Human ventriculum myosin (βmys) powers contraction sometimes in complex with myosin binding protein C (MYBPC3). The latter regulates βmys activity and impacts cardiac function. Single residue variants (SRVs) change protein sequence in βmys or MYBPC3 causing inheritable heart diseases by affecting the βmys/MYBPC3 complex. Muscle genetics encode instructions for contraction informing native protein construction, functional integration, and inheritable disease impairment. A digital model decodes these instructions and evolves by processing new information content from diverse data modalities using a human partner-driven virtuous cycle optimization. A general neural-network contraction model characterizes SRV impacts on human health. It rationalizes phenotype and pathogenicity assignment given the SRVs characteristics and, in this sense, decodes βmys/MYBPC3 complex genetics and implicitly captures ventricular muscle functionality. When an SRV modified domain locates to an inter-protein contact in βmys/MYBPC3 it affects complex coordination. Domains involved, one in βmys and the other in MYBPC3, form coordinated domains (co-domains). Bilateral co-domains imply potential for their SRV modification probabilities to respond jointly to a common perturbation revealing location. Human genetic diversity from the serial founder effect is the common systemic perturbation coupling co-domains subsequently mapped by a method called 2-dimensional correlation genetics (2D-CG). Interpreting general neural-network contraction model output involves 2D-CG co-domain mapping providing structural insights with natural language expression. It aligns machine-learned intelligence from the neural network model with human provided structural insight from the 2D-CG map, and other data from the literature, to form a neural-symbolic hybrid model integrating genetic and protein-interaction data into a nascent digital twin. The process forms a template for combining new information content from diverse data modalities into an evolving digital model. This nascent digital twin interprets SRV implications for disease mechanism discovery.