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Scalable computational modelling tools are required to guide the rational design of complex hierarchical materials with predictable functions. Here, we utilize mesoscopic modelling, integrated with genetic block copolymer synthesis and bioinspired spinning process, to demonstrate de novo materials design that incorporates chemistry, processing and material characterization. We find that intermediate hydrophobic/hydrophilic block ratios observed in natural spider silks and longer chain lengths lead to outstanding silk fibre formation. This design by nature is based on the optimal combination of protein solubility, self-assembled aggregate size and polymer network topology. The original homogeneous network structure becomes heterogeneous after spinning, enhancing the anisotropic network connectivity along the shear flow direction. Extending beyond the classical polymer theory, with insights from the percolation network model, we illustrate the direct proportionality between network conductance and fibre Young's modulus. This integrated approach provides a general path towards de novo functional network materials with enhanced mechanical properties and beyond (optical, electrical or thermal) as we have experimentally verified.National Institutes of Health (U.S.) (NIH (U01 EB014967))National Science Foundation (U.S.) (NSF award No. ECS–0335765)National Research Foundation of Korea (2013R1A1A010091)European Research Council (ERC StG Ideas 2011 BIHSNAM no. 279985 on ‘Bio-Inspired hierarchical super-nanomaterials’)European Research Council (ERC PoC 2013-1 REPLICA2 no. 619448 on ‘Large-area replication of biological anti-adhesive nanosurfaces’)European Research Council (ERC PoC 2013-2 KNOTOUGH no. 632277 on ‘Super-tough knotted fibers’)European Commission (Graphene Flagship (WP10 ‘Nanocomposites’, no. 604391))Autonomous Province of Trento (‘Graphene Nanocomposites’, no. S116/2012-242637 and reg. delib. no. 2266

Predictive modelling-based design and experiments for synthesis and spinning of bioinspired silk fibres