Simulating 500 million years of evolution with a language model DOI
Thomas Hayes, Roshan Rao, Halil Akin

et al.

Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 16, 2025

More than three billion years of evolution have produced an image biology encoded into the space natural proteins. Here we show that language models trained at scale on evolutionary data can generate functional proteins are far away from known We present ESM3, a frontier multimodal generative model reasons over sequence, structure, and function ESM3 follow complex prompts combining its modalities is highly responsive to alignment improve fidelity. prompted fluorescent Among generations synthesized, found bright protein distance (58% sequence identity) proteins, which estimate equivalent simulating five hundred million evolution.

Language: Английский

Generalized biomolecular modeling and design with RoseTTAFold All-Atom DOI
Rohith Krishna, Jue Wang, Woody Ahern

et al.

Science, Journal Year: 2024, Volume and Issue: 384(6693)

Published: March 7, 2024

Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids DNA bases with an atomic all other groups model assemblies that contain proteins, nucleic acids, small molecules, metals, covalent modifications, given their sequences chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion (RFdiffusionAA), builds structures around molecules. Starting from random distributions acid residues surrounding target designed experimentally validated, through crystallography binding measurements, proteins bind the cardiac disease therapeutic digoxigenin, enzymatic cofactor heme, light-harvesting molecule bilin.

Language: Английский

Citations

335
Illuminating protein space with a programmable generative model DOI Creative Commons
John Ingraham,

Max Baranov,

Zak Costello

et al.

Nature, Journal Year: 2023, Volume and Issue: 623(7989), P. 1070 - 1078

Published: Nov. 15, 2023

Abstract Three billion years of evolution has produced a tremendous diversity protein molecules 1 , but the full potential proteins is likely to be much greater. Accessing this been challenging for both computation and experiments because space possible larger than those have functions. Here we introduce Chroma, generative model complexes that can directly sample novel structures sequences, conditioned steer process towards desired properties To enable this, diffusion respects conformational statistics polymer ensembles, an efficient neural architecture molecular systems enables long-range reasoning with sub-quadratic scaling, layers efficiently synthesizing three-dimensional from predicted inter-residue geometries general low-temperature sampling algorithm models. Chroma achieves design as Bayesian inference under external constraints, which involve symmetries, substructure, shape, semantics even natural-language prompts. The experimental characterization 310 shows results in are highly expressed, fold favourable biophysical properties. crystal two designed exhibit atomistic agreement samples (a backbone root-mean-square deviation around 1.0 Å). With unified approach design, hope accelerate programming matter benefit human health, materials science synthetic biology.

Language: Английский

Citations

189
A Survey on Generative Diffusion Models DOI
Hanqun Cao, Cheng Tan, Zhangyang Gao

et al.

IEEE Transactions on Knowledge and Data Engineering, Journal Year: 2024, Volume and Issue: 36(7), P. 2814 - 2830

Published: Feb. 2, 2024

Deep generative models have unlocked another profound realm of human creativity. By capturing and generalizing patterns within data, we entered the epoch all-encompassing Artificial Intelligence for General Creativity (AIGC). Notably, diffusion models, recognized as one paramount materialize ideation into tangible instances across diverse domains, encompassing imagery, text, speech, biology, healthcare. To provide advanced comprehensive insights diffusion, this survey comprehensively elucidates its developmental trajectory future directions from three distinct angles: fundamental formulation algorithmic enhancements, manifold applications diffusion. Each layer is meticulously explored to offer a comprehension evolution. Structured summarized approaches are presented here.

Language: Английский

Citations

118
Machine Learning-Guided Protein Engineering DOI Creative Commons
Petr Kouba, Pavel Kohout, Faraneh Haddadi

et al.

ACS Catalysis, Journal Year: 2023, Volume and Issue: 13(21), P. 13863 - 13895

Published: Oct. 13, 2023

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid the discovery annotation of enzymes, as well suggesting beneficial mutations for improving known targets. The field protein is gathering steam, driven by recent success stories notable other areas. It already encompasses ambitious tasks such understanding predicting structure function, catalytic efficiency, enantioselectivity, dynamics, stability, solubility, aggregation, more. Nonetheless, still evolving, with many challenges overcome questions address. In this Perspective, we provide an overview ongoing trends domain, highlight case studies, examine current limitations learning-based We emphasize crucial importance thorough validation emerging models before their use rational design. present our opinions on fundamental problems outline potential directions future research.

Language: Английский

Citations

94
Machine learning for functional protein design DOI
Pascal Notin, Nathan Rollins, Yarin Gal

et al.

Nature Biotechnology, Journal Year: 2024, Volume and Issue: 42(2), P. 216 - 228

Published: Feb. 1, 2024

Language: Английский

Citations

91
Simulating 500 million years of evolution with a language model DOI
Thomas Hayes, Roshan Rao, Halil Akin

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 2, 2024

Abstract More than three billion years of evolution have produced an image biology encoded into the space natural proteins. Here we show that language models trained on tokens generated by can act as evolutionary simulators to generate functional proteins are far away from known We present ESM3, a frontier multimodal generative model reasons over sequence, structure, and function ESM3 follow complex prompts combining its modalities is highly responsive biological alignment. prompted fluorescent with chain thought. Among generations synthesized, found bright protein at distance (58% identity) Similarly distant separated five hundred million evolution.

Language: Английский

Citations

89
De novo protein design—From new structures to programmable functions DOI Creative Commons
Tanja Kortemme

Cell, Journal Year: 2024, Volume and Issue: 187(3), P. 526 - 544

Published: Feb. 1, 2024

Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes molecular functions de novo, without starting found in nature. In this Perspective, I will discuss the state field novo protein design at juncture physics-based modeling approaches AI. New folds higher-order assemblies be designed considerable experimental success rates, difficult problems requiring tunable control over conformations precise shape complementarity for recognition are coming into reach. Emerging incorporate engineering principles-tunability, controllability, modularity-into process beginning. Exciting frontiers lie deconstructing cellular and, conversely, constructing synthetic signaling ground up. As methods improve, many more challenges unsolved.

Language: Английский

Citations

87
De novo design of high-affinity binders of bioactive helical peptides DOI Creative Commons
Susana Vázquez Torres, Philip J. Y. Leung, Preetham Venkatesh

et al.

Nature, Journal Year: 2023, Volume and Issue: 626(7998), P. 435 - 442

Published: Dec. 18, 2023

Many peptide hormones form an α-helix on binding their receptors

Language: Английский

Citations

86
Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering DOI Creative Commons
Jason Yang, Francesca-Zhoufan Li, Frances H. Arnold

et al.

ACS Central Science, Journal Year: 2024, Volume and Issue: 10(2), P. 226 - 241

Published: Feb. 5, 2024

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even unlock new activities not found in nature. Because search space possible proteins is vast, enzyme engineering usually involves discovering an starting point that has some desired activity followed by directed evolution improve its "fitness" for a application. Recently, machine learning (ML) emerged powerful tool complement this empirical process. ML models contribute (1) discovery functional annotation known protein or generating novel with functions (2) navigating fitness landscapes optimization mappings between associated values. In Outlook, we explain how complements discuss future potential improved outcomes.

Language: Английский

Citations

72
Atomically accurate de novo design of single-domain antibodies DOI Creative Commons
Nathaniel R. Bennett, Joseph L. Watson, Robert J. Ragotte

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 18, 2024

Despite the central role that antibodies play in modern medicine, there is currently no way to rationally design novel bind a specific epitope on target. Instead, antibody discovery involves time-consuming immunization of an animal or library screening approaches. Here we demonstrate fine-tuned RFdiffusion network capable designing de novo variable heavy chains (VHH's) user-specified epitopes. We experimentally confirm binders four disease-relevant epitopes, and cryo-EM structure designed VHH bound influenza hemagglutinin nearly identical model both configuration CDR loops overall binding pose.

Language: Английский

Citations

72