  Through evolution, nature has presented a set of remarkable protein
materials, including elastins, silks, keratins and collagens with superior
mechanical performances that play crucial roles in mechanobiology. However,
going beyond natural designs to discover proteins that meet specified
mechanical properties remains challenging. Here we report a generative model
that predicts protein designs to meet complex nonlinear mechanical
property-design objectives. Our model leverages deep knowledge on protein
sequences from a pre-trained protein language model and maps mechanical
unfolding responses to create novel proteins. Via full-atom molecular
simulations for direct validation, we demonstrate that the designed proteins
are novel, and fulfill the targeted mechanical properties, including unfolding
energy and mechanical strength, as well as the detailed unfolding
force-separation curves. Our model offers rapid pathways to explore the
enormous mechanobiological protein sequence space unconstrained by biological
synthesis, using mechanical features as target to enable the discovery of
protein materials with superior mechanical properties.
