ArticleScience advances2024
ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Scaffold-Lab: Critical evaluation and ranking of protein backbone generation methods in a unified framework.PLoS computational biology · 2026Article
- Article
- Single-round evolution of RNA aptamers with GRAPE-LM.Nature biotechnology · 2026Article
- Computational design of superstable proteins through maximized hydrogen bonding.Nature chemistry · 2026Article
- GenUnfold: Rapidly Predict Protein Mechanical Unfolding Trajectory via a Physics-Guided Diffusion Model.Proceedings of machine learning research · 2026Article
- Optimizing the breadth of SARS-CoV-2-neutralizing antibodies in vivo and in silico.Human vaccines & immunotherapeutics · 2025Review
- Biophysics-based protein language models for protein engineering.Nature methods · 2025Article
- CPL-Diff: A Diffusion Model for De Novo Design of Functional Peptide Sequences with Fixed Length.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Biophysics-based protein language models for protein engineering.bioRxiv : the preprint server for biology · 2025Article
- AI-accelerated discovery of altermagnetic materials.National science review · 2025Article
- Sifting through the noise: A survey of diffusion probabilistic models and their applications to biomolecules.Journal of molecular biology · 2025Review
- Automating alloy design and discovery with physics-aware multimodal multiagent AI.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Progress in Multiscale Modeling of Silk Materials.Biomacromolecules · 2024Review
- ProtAgents: protein discoveryDigital discovery · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
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 proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are de novo, 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 the target to enable the discovery of protein materials with superior mechanical properties.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.