ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model
This work enables the discovery of protein materials with superior mechanical properties, potentially advancing fields like mechanobiology and materials science, though it is incremental as it builds on existing protein language models.
The authors tackled the challenge of designing proteins with specific nonlinear mechanical properties by developing ForceGen, a generative model that uses a protein language diffusion model to create novel protein sequences. They demonstrated through molecular simulations that the designed proteins meet targeted mechanical properties such as unfolding energy and strength.
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.