ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model

作     者：Ni, Bo Kaplan, David L. Buehler, Markus J. 

作者机构： Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States Department of Biomedical Engineering Tufts University MedfordMA02155 United States Center for Computational Science and Engineering Schwarzman College of Computing Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Proteins 

摘      要：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 pretrained 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. Teaser: Based on a new force-unfolding dataset derived from full-atomistic molecular modeling, we propose an algorithm that designs novel proteins that meet complex nonlinear mechanical unfolding properties, including detailed unfolding force-separation curves. © 2023, CC BY-NC-ND.