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

Abstract

ABSTRACTBackgroundHuman ventriculum myosin (βmys) powers contraction sometimes in complex with myosin binding protein C (MYBPC3). The latter regulates βmys activity and impacts overall cardiac function. Nonsynonymous single nucleotide variants (SNVs) 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 continuously processing new information content from diverse data modalities in partnership with the human agent.MethodsA general neural-network contraction model characterizes SNV impacts on human health. It rationalizes phenotype and pathogenicity assignment given the SNVs genetic characteristics and in this sense decodes βmys/MYBPC3 complex genetics and implicitly captures ventricular muscle functionality. When a SNV 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). Co-domains are bilateral implying potential for their SNV modification probabilities to respond jointly to a common perturbation to reveal their location. Human genetic diversity from the serial founder effect is the common systemic perturbation coupling co-domains that are mapped by a methodology called 2-dimensional correlation genetics (2D-CG).ResultsInterpreting the general neural-network contraction model output involves 2D-CG co-domain mapping that provides natural language expressed structural insights. 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. This process is the template for combining new information content from diverse data modalities into a digital model that can evolve. The nascent digital twin interprets SNV implications to discover disease mechanism, can evaluate potential remedies for efficacy, and does so without animal models.HighlightsNeural-symbolic hybrid model decodes muscle genetics into contraction mechanisms And evolves in virtuous cycleOptimize-Interpret-Revise-Repeataided by human partner Nascent digital twin unravels inheritable disease mechanism without animal models And estimates cardiac phenotype coupling strength to myosin thick-filament structures