Mutations in the sarcomeric protein titin are an emerging cause of inherited myopathies and other distinctive phenotypes involving cardiac and skeletal muscles. To understand their effect on disease mediation, generic methods have been developed recently that predict the pathogenicity of missense mutations across the proteome; along with curated gene-specific databases like TITINdb. This work combines the above ethos to characterize the differences underlying pathogenicity mediation exclusively in TTN. Using this combinatorial approach, we compare the molecular consequences of n=203 pathogenic and n=878 benign mutations on titin protein sequences (isoforms), structure, and function. We aim to develop a screening tool that exploits these insights within machine learning pipelines to (a) predict the effects of TTN variants on disease and (b) suggest phenotypes of variants of unknown significance, which is a considerable challenge clinically. These derived interpretable evidence-based correlations to observed clinical phenotypes will pre-emptively predict mutations, facilitate patient risk assessment, and guide therapeutic intervention